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. It was hypothesized that among such genes were those that contribute to the initiation of HL acclimation. By focusing on differentially expressed transcription (co-)factor genes and applying dynamic statistical modelling to the temporal transcriptomics data, a regulatory network of 47 predominantly photoreceptor-regulated transcription (co-)factor genes was inferred. The most connected gene in this network was B-BOX DOMAIN CONTAINING PROTEIN32 (BBX32). Plants overexpressing BBX32 were strongly impaired in acclimation to HL and displayed perturbed expression of photosynthesis-associated genes under LL and after exposure to HL. These observations led to demonstrating that as well as regulation of chloroplast-level acclimation by BBX32, CRYPTOCHROME1, LONG HYPOCOTYL5, CONSTITUTIVELY PHOTOMORPHOGENIC1 and SUPPRESSOR OF PHYA-105 are important. In addition, the BBX32-centric gene regulatory network provides a view of the transcriptional control of acclimation in mature leaves distinct from other photoreceptor-regulated processes, such as seedling photomorphogenesis.

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. Using short-read, long-read and phased sequencing technologies, we generated a high-quality chromosome-level genome assembly for Amaranthus cruentus, an under-researched crop with micronutrient- and protein-rich leaves and gluten-free seed, but lacking improved varieties, with respect to productivity and quality traits. The 370.9 Mb genome demonstrates a shared whole genome duplication with a related species, Amaranthus hypochondriacus. Comparative genome analysis indicates chromosomal loss and fusion events following genome duplication that are common to both species, as well as fission of chromosome 2 in A. cruentus alone, giving rise to a haploid chromosome number of 17 (versus 16 in A. hypochondriacus). Genomic features potentially underlying the nutritional value of this crop include two A. cruentus-specific genes with a likely role in phytic acid synthesis (an anti-nutrient), expansion of ion transporter gene families, and identification of biosynthetic gene clusters conserved within the amaranth lineage. The A. cruentus genome assembly will underpin much-needed research and global breeding efforts to develop improved varieties for economically viable cultivation and realization of the benefits to global nutrition security and agrobiodiversity.

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. We studied the physiological and transcriptional responses of Eucalyptus following oviposition by the gall-inducing wasp, Leptocybe invasa, to explore potential mechanisms governing defence responses and gall development. RNA sequencing and microscopy were used to explore a susceptible Eucalyptus-L. invasa interaction. Infested and control material was compared over time (1-3, 7 and 90 days post oviposition) to examine the transcriptional and morphological changes. Oviposition induces accumulation of reactive oxygen species and phenolics which is reflected in the transcriptome analysis. Gene expression supports phytohormones and 10 transcription factor subfamilies as key regulators. The egg and oviposition fluid stimulate cell division resulting in gall development. Eucalyptus responses to oviposition are apparent within 24 hr. Putative defences include the oxidative burst and barrier reinforcement. However, egg and oviposition fluid stimuli may redirect these responses towards gall development.

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. Availability and implementation: The integrated apps can be found at http://www.cyverse.org/discovery-environment, while the raw code is available at https://github.com/cyversewarwick and the corresponding Docker images are housed at https://hub.docker.com/r/cyversewarwick/. Contact: info@cyverse.warwick.ac.uk or D.L.Wild@warwick.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Time-Series Transcriptomics Reveals That AGAMOUS-LIKE22 Affects Primary Metabolism and Developmental Processes in Drought-Stressed Arabidopsis.

In Arabidopsis thaliana, changes in metabolism and gene expression drive increased drought tolerance and initiate diverse drought avoidance and escape responses. To address regulatory processes that link these responses, we set out to identify genes that govern early responses to drought. To do this, a high-resolution time series transcriptomics data set was produced, coupled with detailed physiological and metabolic analyses of plants subjected to a slow transition from well-watered to drought conditions. A total of 1815 drought-responsive differentially expressed genes were identified. The early changes in gene expression coincided with a drop in carbon assimilation, and only in the late stages with an increase in foliar abscisic acid content. To identify gene regulatory networks (GRNs) mediating the transition between the early and late stages of drought, we used Bayesian network modeling of differentially expressed transcription factor (TF) genes. This approach identified AGAMOUS-LIKE22 (AGL22), as key hub gene in a TF GRN. It has previously been shown that AGL22 is involved in the transition from vegetative state to flowering but here we show that AGL22 expression influences steady state photosynthetic rates and lifetime water use. This suggests that AGL22 uniquely regulates a transcriptional network during drought stress, linking changes in primary metabolism and the initiation of stress responses.

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. 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.

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. cinerea depending on the time of day of inoculation. Decreased susceptibility after inoculation at dawn compared with night persists under constant light conditions and is disrupted in dysfunctional clock mutants, demonstrating the role of the plant clock in driving time-of-day susceptibility to B. cinerea. The decreased susceptibility to B. cinerea following inoculation at subjective dawn was associated with faster transcriptional reprogramming of the defence response with gating of infection-responsive genes apparent. Direct target genes of core clock regulators were enriched among the transcription factors that responded more rapidly to infection at subjective dawn than subjective night, suggesting an influence of the clock on the defence-signalling network. In addition, jasmonate signalling plays a crucial role in the rhythmic susceptibility of Arabidopsis to B. cinerea with the enhanced susceptibility to this pathogen at subjective night lost in a jaz6 mutant.

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. 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.

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. high correlation of expression profiles across multiple time series datasets. However, numbers of up- or downregulated genes are often large, making it difficult to discriminate between dependent co-expression resulting from co-regulation and independent co-expression. Furthermore, modules of co-regulated genes may only show tight co-expression across a subset of the time series, i.e. show condition-dependent regulation. RESULTS: Wigwams is a simple and efficient method to identify gene modules showing evidence for co-regulation in multiple time series of gene expression data. Wigwams analyzes similarities of gene expression patterns within each time series (condition) and directly tests the dependence or independence of these across different conditions. The expression pattern of each gene in each subset of conditions is tested statistically as a potential signature of a condition-dependent regulatory mechanism regulating multiple genes. Wigwams does not require particular time points and can process datasets that are on different time scales. Differential expression relative to control conditions can be taken into account. The output is succinct and non-redundant, enabling gene network reconstruction to be focused on those gene modules and combinations of conditions that show evidence for shared regulatory mechanisms. Wigwams was run using six Arabidopsis time series expression datasets, producing a set of biologically significant modules spanning different combinations of conditions. AVAILABILITY AND IMPLEMENTATION: A Matlab implementation of Wigwams, complete with graphical user interfaces and documentation, is available at: warwick.ac.uk/wigwams. .

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. Approximately one-third of the Arabidopsis genome is differentially expressed during the first 48 h after infection, with the majority of changes in gene expression occurring before significant lesion development. We used computational tools to obtain a detailed chronology of the defense response against B. cinerea, highlighting the times at which signaling and metabolic processes change, and identify transcription factor families operating at different times after infection. Motif enrichment and network inference predicted regulatory interactions, and testing of one such prediction identified a role for TGA3 in defense against necrotrophic pathogens. These data provide an unprecedented level of detail about transcriptional changes during a defense response and are suited to systems biology analyses to generate predictive models of the gene regulatory networks mediating the Arabidopsis response to B. cinerea.

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. Similarities and divergence in regulation during a variety of stress responses are predicted by different combinations of upstream transcription factors binding and also by the modelling. Mutant analysis with potential upstream genes was used to test and confirm some of the predicted interactions. Gene expression analysis in mutants of ANAC019 and ANAC055 at different times during leaf senescence has revealed a distinctly different role for each of these genes. Yeast 1-hybrid analysis is shown to be a valuable tool that can distinguish clades of binding proteins and be used to test and quantify protein binding to predicted promoter motifs.

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. 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.

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. solanacearum isolated from Eucalyptus trees. A. thaliana accession Be-0 was susceptible and accession Kil-0 was tolerant. Kil-0 exhibited no wilting symptoms and no significant reduction in fitness (biomass, seed yield, and germination efficiency) after inoculation with R. solanacearum BCCF402, despite high bacterial numbers in planta. This was in contrast to the well-characterized resistance response in the accession Nd-1, which limits bacterial multiplication at early stages of infection and does not wilt. R. solanacearum BCCF402 was highly virulent because the susceptible accession Be-0 was completely wilted after inoculation. Genetic analyses, allelism studies with Nd-1, and RRS1 cleaved amplified polymorphic sequence marker analysis showed that the tolerance phenotype in Kil-0 was dependent upon the resistance gene RRS1. Knockout and complementation studies of the R. solanacearum BCCF402 effector PopP2 confirmed that the tolerance response in Kil-0 was dependent upon the RRS1-PopP2 interaction. Our data indicate that the gene-for-gene interaction between RRS1 and PopP2 can contribute to tolerance, as well as resistance, which makes it a useful model system for evolutionary studies of the arms race between plants and bacterial pathogens. In addition, the results alert biotechnologists to the risk that deployment of RRS1 in transgenic crops may result in persistence of the pathogen in the field.

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. More generally, the method can be used to identify context-specific regulation by leveraging time series gene expression data alongside methods that can identify putative lists of transcription factors or transcription factor targets. RESULTS: The hierarchical inference outperforms related (but non-hierarchical) approaches when the networks used to generate the data were identical, and performs comparably even when the networks used to generate data were independent. The method was subsequently used alongside yeast one hybrid and microarray time series data to infer potential transcriptional switches in Arabidopsis thaliana response to stress. The results confirm previous biological studies and allow for additional insights into gene regulation under various abiotic stresses. AVAILABILITY: The methods outlined in this article have been implemented in Matlab and are available on request.

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. In this study, we show that MED25 physically interacts with several key transcriptional regulators of the JA signaling pathway, including the APETALA2 (AP2)/ETHYLENE RESPONSE FACTOR (ERF) transcription factors OCTADECANOID-RESPONSIVE ARABIDOPSIS AP2/ERF59 and ERF1 as well as the master regulator MYC2. Physical interaction detected between MED25 and four group IX AP2/ERF transcription factors was shown to require the activator interaction domain of MED25 as well as the recently discovered Conserved Motif IX-1/EDLL transcription activation motif of MED25-interacting AP2/ERFs. Using transcriptional activation experiments, we also show that OCTADECANOID-RESPONSIVE ARABIDOPSIS AP2/ERF59- and ERF1-dependent activation of PLANT DEFENSIN1.2 as well as MYC2-dependent activation of VEGETATIVE STORAGE PROTEIN1 requires a functional MED25. In addition, MED25 is required for MYC2-dependent repression of pathogen defense genes. These results suggest an important role for MED25 as an integrative hub within the Mediator complex during the regulation of JA-associated gene expression.

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. Appropriate specification of the prior is crucial to control the flexibility of the splines, especially under circumstances of scarce data; thus, we provide an informative, proper prior. Substantive improvement in network inference over a linear model is demonstrated using synthetic data drawn from ordinary differential equation models and gene expression from an experimental data set of the Arabidopsis thaliana circadian rhythm.

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). A number of genes were up- and down-regulated specifically at 12 h, others at 24 h while others were up- and down-regulated at both time points. Similarly, some genes were specifically induced very close to the lesion while others in more distal tissue. Clustering of expression profiles resulting from other biotic and abiotic interactions with Arabidopsis indicated a large overlap in gene expression. This study highlighted a multitude of genes induced in Arabidopsis spatially and temporally following infection with B. cinerea providing an insight into key processes of defence against this pathogen. The plethora of altered genes identified are candidates for further investigation.

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. Accordingly, Maf1 is found in some organisms to influence growth and/or stress survival. Because of their sessile nature, plants are especially vulnerable to environmental changes and molecular adaptations that enhance growth under benign circumstances can increase sensitivity to external challenges. We tested if Maf1 depletion in the model plant Arabidopsis affects growth, pathogen resistance and tolerance of drought or soil salinity, a common physiological challenge that imposes both osmotic and ionic stress. We find that disruption of the Maf1 gene or RNAi-mediated depletion of its transcript is well-tolerated and confers a modest growth advantage without compromising resistance to common biotic and abiotic challenges.

Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana.

MOTIVATION: Identifying regulatory modules is an important task in the exploratory analysis of gene expression time series data. Clustering algorithms are often used for this purpose. However, gene regulatory events may induce complex temporal features in a gene expression profile, including time delays, inversions and transient correlations, which are not well accounted for by current clustering methods. As the cost of microarray experiments continues to fall, the temporal resolution of time course studies is increasing. This has led to a need to take account of detailed temporal features of this kind. Thus, while standard clustering methods are both widely used and much studied, their shared shortcomings with respect to such temporal features motivates the work presented here. RESULTS: Here, we introduce a temporal clustering approach for high-dimensional gene expression data which takes account of time delays, inversions and transient correlations. We do so by exploiting a recently introduced, message-passing-based algorithm called Affinity Propagation (AP). We take account of temporal features of interest following an approximate but efficient dynamic programming approach due to Qian et al. The resulting approach is demonstrably effective in its ability to discern non-obvious temporal features, yet efficient and robust enough for routine use as an exploratory tool. We show results on validated transcription factor-target pairs in yeast and on gene expression data from a study of Arabidopsis thaliana under pathogen infection. The latter reveals a number of biologically striking findings. AVAILABILITY: Matlab code for our method is available at http://www.wsbc.warwick.ac.uk/stevenkiddle/tcap.html.

Granger causality vs. dynamic Bayesian network inference: a comparative study.

BACKGROUND: In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. Currently, there are two common approaches used to explore the network structure among elements. One is the Granger causality approach, and the other is the dynamic Bayesian network inference approach. Both have at least a few thousand publications reported in the literature. A key issue is to choose which approach is used to tackle the data, in particular when they give rise to contradictory results. RESULTS: In this paper, we provide an answer by focusing on a systematic and computationally intensive comparison between the two approaches on both synthesized and experimental data. For synthesized data, a critical point of the data length is found: the dynamic Bayesian network outperforms the Granger causality approach when the data length is short, and vice versa. We then test our results in experimental data of short length which is a common scenario in current biological experiments: it is again confirmed that the dynamic Bayesian network works better. CONCLUSION: When the data size is short, the dynamic Bayesian network inference performs better than the Granger causality approach; otherwise the Granger causality approach is better.