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Plant biology is rapidly entering an era where we have the ability to conduct intricate studies that investigate how a plant interacts with the entirety of its environment. This requires complex, large studies to measure how plant genotypes simultaneously interact with a diverse array of environmental stimuli. Successful interpretation of the results from these studies requires us to transition away from the traditional standard of conducting an array of pairwise t tests toward more general linear modeling structures, such as those provided by the extendable ANOVA framework. In this Perspective, we present arguments for making this transition and illustrate how it will help to avoid incorrect conclusions in factorial interaction studies (genotype × genotype, genotype × treatment, and treatment × treatment, or higher levels of interaction) that are becoming more prevalent in this new era of plant biology.

How standing genetic variation within a pathogen contributes to diversity in host/pathogen interactions is poorly understood, partly because most studied pathogens are host-specific, clonally reproducing organisms which complicates genetic analysis. In contrast, Botrytis cinerea is a sexually reproducing, true haploid ascomycete that can infect a wide range of diverse plant hosts. While previous work had shown significant genomic variation between two isolates, we proceeded to assess the level and frequency of standing variation in a population of B. cinerea. To begin measuring standing genetic variation in B. cinerea, we re-sequenced the genomes of 13 different isolates and aligned them to the previously sequenced T4 reference genome. In addition one of these isolates was resequenced from four independently repeated cultures. A high level of genetic diversity was found within the 13 isolates. Within this variation, we could identify clusters of genes with major effect polymorphisms, i.e., polymorphisms that lead to a predicted functional knockout, that surrounded genes involved in controlling vegetative incompatibility. The genotype at these loci was able to partially predict the interaction of these isolates in vegetative fusion assays showing that these loci control vegetative incompatibility. This suggests that the vegetative incompatibility loci within B. cinerea are associated with regions of increased genetic diversity. The genome re-sequencing of four clones from the one isolate (Grape) that had been independently propagated over 10 years showed no detectable spontaneous mutation. This suggests that B. cinerea does not display an elevated spontaneous mutation rate. Future work will allow us to test if, and how, this diversity may be contributing to the pathogen's broad host range.

Plant pathogens can decimate crops and render the local cultivation of a species unprofitable. In extreme cases this has caused famine and economic collapse. Timing is vital in treating crop diseases, and the use of computer vision for precise disease detection and timing of pesticide application is gaining popularity. Computer vision can reduce labour costs, prevent misdiagnosis of disease, and prevent misapplication of pesticides. Pesticide misapplication is both financially costly and can exacerbate pesticide resistance and pollution. Here, we review the application and development of computer vision and machine learning methods for the detection of plant disease. This review goes beyond the scope of previous works to discuss important technical concepts and considerations when applying computer vision to plant pathology. We present new case studies on adapting standard computer vision methods and review techniques for acquiring training data, the use of diagnostic tools from biology, and the inspection of informative features. In addition to an in‐depth discussion of convolutional neural networks (CNNs) and transformers, we also highlight the strengths of methods such as support vector machines and evolved neural networks. We discuss the benefits of carefully curating training data and consider situations where less computationally expensive techniques are advantageous. This includes a comparison of popular model architectures and a guide to their implementation. [ABSTRACT FROM AUTHOR]

Abstract Background Polygalacturonase-inhibiting proteins (PGIPs) directly limit the effective ingress of fungal pathogens by inhibiting cell wall-degrading endopolygalacturonases (ePGs). Transgenic tobacco plants over-expressing grapevine (Vitis vinifera) Vvpgip1 have previously been shown to be resistant to Botrytis infection. In this study we characterized two of these PGIP over-expressing lines with known resistance phenotypes by gene expression and hormone profiling in the absence of pathogen infection. Results Global gene expression was performed by a cross-species microarray approach using a potato cDNA microarray. The degree of potential cross-hybridization between probes was modeled by a novel computational workflow designed in-house. Probe annotations were updated by predicting probe-to-transcript hybridizations and combining information derived from other plant species. Comparing uninfected Vvpgip1-overexpressing lines to wild-type (WT), 318 probes showed significant change in expression. Functional groups of genes involved in metabolism and associated to the cell wall were identified and consequent cell wall analysis revealed increased lignin-levels in the transgenic lines, but no major differences in cell wall-derived polysaccharides. GO enrichment analysis also identified genes responsive to auxin, which was supported by elevated indole-acetic acid (IAA) levels in the transgenic lines. Finally, a down-regulation of xyloglucan endotransglycosylase/hydrolases (XTHs), which are important in cell wall remodeling, was linked to a decrease in total XTH activity. Conclusions This evaluation of PGIP over-expressing plants performed under pathogen-free conditions to exclude the classical PGIP-ePG inhibition interaction indicates additional roles for PGIPs beyond the inhibition of ePGs.

Abstract Background Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data analysis, little attention has been paid to uncertainty in the results obtained. Results We present an R/Bioconductor port of a fast novel algorithm for Bayesian agglomerative hierarchical clustering and demonstrate its use in clustering gene expression microarray data. The method performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. Conclusion Biologically plausible results are presented from a well studied data set: expression profiles of A. thaliana subjected to a variety of biotic and abiotic stresses. Our method avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric.

Gall-inducing insects are capable of exerting a high level of control over their hosts' cellular machinery to the extent that the plant's development, metabolism, chemistry, and physiology are all altered in favour of the insect. Many gallers are devastating pests in global agriculture and the limited understanding of their relationship with their hosts prevents the development of robust management strategies. Omics technologies are proving to be important tools in elucidating the mechanisms involved in the interaction as they facilitate analysis of plant hosts and insect effectors for which little or no prior knowledge exists. In this review, we examine the mechanisms behind insect gall development using evidence from omics-level approaches. The secretion of effector proteins and induced phytohormonal imbalances are highlighted as likely mechanisms involved in gall development. However, understanding how these components function within the system is far from complete and a number of questions need to be answered before this information can be used in the development of strategies to engineer or breed plants with enhanced resistance. [ABSTRACT FROM AUTHOR]

Summary: Every year, a large number of novel algorithms are introduced to the scientific community for a myriad of applications, but using these across different research groups is often troublesome, due to suboptimal implementations and specific dependency requirements. This does not have to be the case, as public cloud computing services can easily house tractable implementations within self-contained dependency environments, making the methods easily accessible to a wider public. We have taken 14 popular methods, the majority related to expression data or promoter analysis, developed these up to a good implementation standard and housed the tools in isolated Docker containers which we integrated into the CyVerse Discovery Environment, making these easily usable for a wide community as part of the CyVerse UK project. [ABSTRACT FROM AUTHOR]

Numerous studies have suggested that plant/pathogen interactions are partially mediated via plant secondary metabolite production and corresponding pathogen tolerance. However, there are inconsistent reports on the ability of particular compounds to provide resistance to a pathogen. Most of these studies have focused on individual isolates of a given pathogen, suggesting that pathogens vary in their sensitivity to plant-produced toxins. We tested variability in virulence among pathogen isolates, and the impact on this by plant production of, and pathogen tolerance to, secondary metabolites. Botrytis cinerea isolates showed differing sensitivity to purified camalexin, and camalexin-sensitive isolates produced larger lesions on camalexin-deficient Arabidopsis genotypes than on the wild type. In contrast, the camalexin-insensitive isolate produced lesions of similar size on wild-type and camalexin-deficient Arabidopsis. Additional analysis with Arabidopsis secondary metabolite biosynthetic mutants suggests that Botrytis also has variable sensitivity to phenylpropanoids and glucosinolates. Furthermore, Botrytis infection generates a gradient of secondary metabolite responses emanating from the developing lesion, with the Botrytis isolate used determining the accumulation pattern. Collectively, our results indicate that Arabidopsis/ Botrytis interactions are influenced at the metabolic level by variations in toxin production in the host and sensitivity in the pathogen. [ABSTRACT FROM AUTHOR]

Microarrays are useful tools to investigate the expression of thousands of genes rapidly. Some researchers remain reluctant to use the technology, however, largely because of its expense. Careful design of a microarray experiment is key to generating cost-effective results. This article explores issues that researchers face when embarking on a microarray experiment for the first time. These include decisions about which microarray platform is available for the organism of interest, the degree of replication (biological and technical) needed and which design (direct or indirect, loop or balanced block) is suitable. [ABSTRACT FROM AUTHOR]

A complex signal transduction network involving salicylic acid, jasmonic acid and ethylene underlies disease resistance inArabidopsis. To understand this defence signalling network further, we identified mutants that expressed the marker genePR-1::luciferasein the absence of pathogen infection. Thesecirmutants all display constitutive expression of a suite of defence-related genes but exhibit different disease resistance profiles to two biotrophic pathogens,Pseudomonas syringaepv.tomatoandPeronospora parasiticaNOCO2, and the necrotrophic pathogenBotrytis cinerea. We further characterizedcir3, which displays enhanced resistance only to the necrotrophic pathogen.Cir3-mediated resistance toB. cinereais dependent on accumulated salicylic acid and a functional EIN2 protein. [ABSTRACT FROM AUTHOR]

Examines the signalling pathways underlying disease resistance to specific groups of pathogens in Arabidopsis and discusses the application of the emerging trends to the study of plant pathology in South Africa. Discussion on salicylic acid (SA)-dependent defence responses; SA-independent defence responses; Complexity of disease resistance signalling in Arabidopsis.