Computer vision for plant pathology: A review with examples from cocoa agriculture.

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.

Impact on Arabidopsis growth and stress resistance of depleting the Maf1 repressor of RNA polymerase III.

Maf1 is a transcription factor that is conserved in sequence and structure between yeasts, animals and plants. Its principal molecular function is also well conserved, being to bind and repress RNA polymerase (pol) III, thereby inhibiting synthesis of tRNAs and other noncoding RNAs. Restrictions on tRNA production and hence protein synthesis can provide a mechanism to preserve resources under conditions that are suboptimal for growth.

Time-series transcriptomics reveals a BBX32-directed control of acclimation to high light in mature Arabidopsis leaves.

The photosynthetic capacity of mature leaves increases after several days' exposure to constant or intermittent episodes of high light (HL) and is manifested primarily as changes in chloroplast physiology. How this chloroplast-level acclimation to HL is initiated and controlled is unknown. From expanded Arabidopsis leaves, we determined HL-dependent changes in transcript abundance of 3844 genes in a 0-6 h time-series transcriptomics experiment.

A chromosome-level Amaranthus cruentus genome assembly highlights gene family evolution and biosynthetic gene clusters that may underpin the nutritional value of this traditional crop.

Traditional crops have historically provided accessible and affordable nutrition to millions of rural dwellers but have been neglected, with most modern agricultural systems over-reliant on a small number of internationally traded crops. Traditional crops are typically well-adapted to local agro-ecological conditions and many are nutrient-dense. They can play a vital role in local food systems through enhanced nutrition (particularly where diets are dominated by starch crops), food security and livelihoods for smallholder farmers, and a climate-resilient and biodiverse agriculture.

Insect egg-induced physiological changes and transcriptional reprogramming leading to gall formation.

Gall-inducing insects and their hosts present some of the most intricate plant-herbivore interactions. Oviposition on the host is often the first cue of future herbivory and events at this early time point can affect later life stages. Many gallers are devastating plant pests, yet little information regarding the plant-insect molecular interplay exists, particularly following egg deposition.

A Framework for Engineering Stress Resilient Plants Using Genetic Feedback Control and Regulatory Network Rewiring.

Crop disease leads to significant waste worldwide, both pre- and postharvest, with subsequent economic and sustainability consequences. Disease outcome is determined both by the plants' response to the pathogen and by the ability of the pathogen to suppress defense responses and manipulate the plant to enhance colonization. The defense response of a plant is characterized by significant transcriptional reprogramming mediated by underlying gene regulatory networks, and components of these networks are often targeted by attacking pathogens.

Bringing numerous methods for expression and promoter analysis to a public cloud computing service.

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.

Insect Gallers and Their Plant Hosts: From Omics Data to Systems Biology.

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.

Jasmonate signalling drives time-of-day differences in susceptibility of Arabidopsis to the fungal pathogen Botrytis cinerea.

The circadian clock, an internal time-keeping mechanism, allows plants to anticipate regular changes in the environment, such as light and dark, and biotic challenges such as pathogens and herbivores. Here, we demonstrate that the plant circadian clock influences susceptibility to the necrotrophic fungal pathogen, Botrytis cinerea. Arabidopsis plants show differential susceptibility to B.

Whole genome resequencing of Botrytis cinerea isolates identifies high levels of standing diversity.

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.

Modelling signaling networks underlying plant defence.

Transcriptional reprogramming plays a significant role in governing plant responses to pathogens. The underlying regulatory networks are complex and dynamic, responding to numerous input signals. Most network modelling studies to date have used large-scale expression data sets from public repositories but defence network models with predictive ability have also been inferred from single time series data sets, and sophisticated biological insights generated from focused experiments containing multiple network perturbations.

Reassess the t Test: Interact with All Your Data via ANOVA.

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.

Increased resistance to biotrophic pathogens in the Arabidopsis constitutive induced resistance 1 mutant is EDS1 and PAD4-dependent and modulated by environmental temperature.

The Arabidopsis constitutive induced resistance 1 (cir1) mutant displays salicylic acid (SA)-dependent constitutive expression of defence genes and enhanced resistance to biotrophic pathogens. To further characterise the role of CIR1 in plant immunity we conducted epistasis analyses with two key components of the SA-signalling branch of the defence network, ENHANCED DISEASE SUSCEPTIBILITY1 (EDS1) and PHYTOALEXIN DEFICIENT4 (PAD4). We demonstrate that the constitutive defence phenotypes of cir1 require both EDS1 and PAD4, indicating that CIR1 lies upstream of the EDS1-PAD4 regulatory node in the immune signalling network.

Network modeling to understand plant immunity.

Deciphering the networks that underpin complex biological processes using experimental data remains a significant, but promising, challenge, a task made all the harder by the added complexity of host-pathogen interactions. The aim of this article is to review the progress in understanding plant immunity made so far by applying network modeling algorithms and to show how this computational/mathematical strategy is facilitating a systems view of plant defense. We review the different types of network modeling that have been used, the data required, and the type of insight that such modeling can provide.

Wigwams: identifying gene modules co-regulated across multiple biological conditions.

Motivation: Identification of modules of co-regulated genes is a crucial first step towards dissecting the regulatory circuitry underlying biological processes. Co-regulated genes are likely to reveal themselves by showing tight co-expression, e.g.

A local regulatory network around three NAC transcription factors in stress responses and senescence in Arabidopsis leaves.

A model is presented describing the gene regulatory network surrounding three similar NAC transcription factors that have roles in Arabidopsis leaf senescence and stress responses. ANAC019, ANAC055 and ANAC072 belong to the same clade of NAC domain genes and have overlapping expression patterns. A combination of promoter DNA/protein interactions identified using yeast 1-hybrid analysis and modelling using gene expression time course data has been applied to predict the regulatory network upstream of these genes.

Gene-for-gene tolerance to bacterial wilt in Arabidopsis.

Bacterial wilt caused by Ralstonia solanacearum is a disease of widespread economic importance that affects numerous plant species, including Arabidopsis thaliana. We describe a pathosystem between A. thaliana and biovar 3 phylotype I strain BCCF402 of R.

Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis.

Transcriptional reprogramming forms a major part of a plant's response to pathogen infection. Many individual components and pathways operating during plant defense have been identified, but our knowledge of how these different components interact is still rudimentary. We generated a high-resolution time series of gene expression profiles from a single Arabidopsis thaliana leaf during infection by the necrotrophic fungal pathogen Botrytis cinerea.

MEDIATOR25 acts as an integrative hub for the regulation of jasmonate-responsive gene expression in Arabidopsis.

The PHYTOCHROME AND FLOWERING TIME1 gene encoding the MEDIATOR25 (MED25) subunit of the eukaryotic Mediator complex is a positive regulator of jasmonate (JA)-responsive gene expression in Arabidopsis (Arabidopsis thaliana). Based on the function of the Mediator complex as a bridge between DNA-bound transcriptional activators and the RNA polymerase II complex, MED25 has been hypothesized to function in association with transcriptional regulators of the JA pathway. However, it is currently not known mechanistically how MED25 functions to regulate JA-responsive gene expression.

Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks.

Motivation: The generation of time series transcriptomic datasets collected under multiple experimental conditions has proven to be a powerful approach for disentangling complex biological processes, allowing for the reverse engineering of gene regulatory networks (GRNs). Most methods for reverse engineering GRNs from multiple datasets assume that each of the time series were generated from networks with identical topology. In this study, we outline a hierarchical, non-parametric Bayesian approach for reverse engineering GRNs using multiple time series that can be applied in a number of novel situations including: (i) where different, but overlapping sets of transcription factors are expected to bind in the different experimental conditions; that is, where switching events could potentially arise under the different treatments and (ii) for inference in evolutionary related species in which orthologous GRNs exist.

Constitutive expression of a grapevine polygalacturonase-inhibiting protein affects gene expression and cell wall properties in uninfected tobacco.

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.

Spatial and temporal transcriptomic analysis of the Arabidopsis thaliana-Botrytis cinerea interaction.

Plants activate an array of defence responses following recognition of pathogenic organisms. This study attempted to characterize at a transcriptional level, the defence responses of Arabidopsis thaliana after infection with Botrytis cinerea using microarrays. Alteration in transcript levels following infection was investigated in time (temporal) and space (spatial).

Inferring the time-invariant topology of a nonlinear sparse gene regulatory network using fully Bayesian spline autoregression.

We propose a semiparametric Bayesian model, based on penalized splines, for the recovery of the time-invariant topology of a causal interaction network from longitudinal data. Our motivation is inference of gene regulatory networks from low-resolution microarray time series, where existence of nonlinear interactions is well known. Parenthood relations are mapped by augmenting the model with kinship indicators and providing these with either an overall or gene-wise hierarchical structure.

A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series.

Understanding the regulatory mechanisms that are responsible for an organism's response to environmental change is an important issue in molecular biology. A first and important step towards this goal is to detect genes whose expression levels are affected by altered external conditions. A range of methods to test for differential gene expression, both in static as well as in time-course experiments, have been proposed.