Richard Hickman

High-Resolution Temporal Profiling of Transcripts during Arabidopsis Leaf Senescence Reveals a Distinct Chronology of Processes and Regulation

This work presents a high-resolution time-course analysis of gene expression during development of a leaf from expansion through senescence. Enrichment in ontologies, sequence motifs, and transcription factor families within genes showing altered expression over time identified both metabolic pathways and potential regulators active at different stages of leaf development and senescence. Leaf senescence is an essential developmental process that impacts dramatically on crop yields and involves altered regulation of thousands of genes and many metabolic and signaling pathways, resulting in major changes in the leaf. The regulation of senescence is complex, and although senescence regulatory genes have been characterized, there is little information on how these function in the global control of the process. We used microarray analysis to obtain a high-resolution time-course profile of gene expression during development of a single leaf over a 3-week period to senescence. A complex experimental design approach and a combination of methods were used to extract high-quality replicated data and to identify differentially expressed genes. The multiple time points enable the use of highly informative clustering to reveal distinct time points at which signaling and metabolic pathways change. Analysis of motif enrichment, as well as comparison of transcription factor (TF) families showing altered expression over the time course, identify clear groups of TFs active at different stages of leaf development and senescence. These data enable connection of metabolic processes, signaling pathways, and specific TF activity, which will underpin the development of network models to elucidate the process of senescence.

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

The authors generated a high-resolution time series of Arabidopsis thaliana gene expression following infection with the fungal pathogen Botrytis cinerea. Computational analysis of this large data set identified the timing of specific processes and regulatory events in the host plant and showed a role for the transcription factor TGA3 in the defense response against the fungal pathogen. 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

Summary 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.

RNA-Seq: revelation of the messengers

Next-generation RNA-sequencing (RNA-Seq) is rapidly outcompeting microarrays as the technology of choice for whole-transcriptome studies. However, the bioinformatics skills required for RNA-Seq data analysis often pose a significant hurdle for many biologists. Here, we put forward the concepts and considerations that are critical for RNA-Seq data analysis and provide a generic tutorial with example data that outlines the whole pipeline from next-generation sequencing output to quantification of differential gene expression.

Arabidopsis HEAT SHOCK TRANSCRIPTION FACTORA1b overexpression enhances water productivity, resistance to drought, and infection

Heat-stressed crops suffer dehydration, depressed growth, and a consequent decline in water productivity, which is the yield of harvestable product as a function of lifetime water consumption and is a trait associated with plant growth and development. Heat shock transcription factor (HSF) genes have been implicated not only in thermotolerance but also in plant growth and development, and therefore could influence water productivity. Here it is demonstrated that Arabidopsis thaliana plants with increased HSFA1b expression showed increased water productivity and harvest index under water-replete and water-limiting conditions. In non-stressed HSFA1b-overexpressing (HSFA1bOx) plants, 509 genes showed altered expression, and these genes were not over-represented for development-associated genes but were for response to biotic stress. This confirmed an additional role for HSFA1b in maintaining basal disease resistance, which was stress hormone independent but involved H2O2 signalling. Fifty-five of the 509 genes harbour a variant of the heat shock element (HSE) in their promoters, here named HSE1b. Chromatin immunoprecipitation-PCR confirmed binding of HSFA1b to HSE1b in vivo, including in seven transcription factor genes. One of these is MULTIPROTEIN BRIDGING FACTOR1c (MBF1c). Plants overexpressing MBF1c showed enhanced basal resistance but not water productivity, thus partially phenocopying HSFA1bOx plants. A comparison of genes responsive to HSFA1b and MBF1c overexpression revealed a common group, none of which harbours a HSE1b motif. From this example, it is suggested that HSFA1b directly regulates 55 HSE1b-containing genes, which control the remaining 454 genes, collectively accounting for the stress defence and developmental phenotypes of HSFA1bOx.

Transcriptional Dynamics Driving MAMP-Triggered Immunity and Pathogen Effector-Mediated Immunosuppression in Arabidopsis Leaves Following Infection with Pseudomonas syringae pv tomato DC3000

High-resolution microarray analysis of Pseudomonas syringae-inoculated Arabidopsis leaves reveals transcriptional dynamics underpinning basal defense and effector modulation leading to disease development. Transcriptional reprogramming is integral to effective plant defense. Pathogen effectors act transcriptionally and posttranscriptionally to suppress defense responses. A major challenge to understanding disease and defense responses is discriminating between transcriptional reprogramming associated with microbial-associated molecular pattern (MAMP)-triggered immunity (MTI) and that orchestrated by effectors. A high-resolution time course of genome-wide expression changes following challenge with Pseudomonas syringae pv tomato DC3000 and the nonpathogenic mutant strain DC3000hrpA- allowed us to establish causal links between the activities of pathogen effectors and suppression of MTI and infer with high confidence a range of processes specifically targeted by effectors. Analysis of this information-rich data set with a range of computational tools provided insights into the earliest transcriptional events triggered by effector delivery, regulatory mechanisms recruited, and biological processes targeted. We show that the majority of genes contributing to disease or defense are induced within 6 h postinfection, significantly before pathogen multiplication. Suppression of chloroplast-associated genes is a rapid MAMP-triggered defense response, and suppression of genes involved in chromatin assembly and induction of ubiquitin-related genes coincide with pathogen-induced abscisic acid accumulation. Specific combinations of promoter motifs are engaged in fine-tuning the MTI response and active transcriptional suppression at specific promoter configurations by P. syringae.

Transcriptome dynamics of Arabidopsis during sequential biotic and abiotic stresses

In nature, plants have to cope with a wide range of stress conditions that often occur simultaneously or in sequence. To investigate how plants cope with multi-stress conditions, we analyzed the dynamics of whole-transcriptome profiles of Arabidopsis thaliana exposed to six sequential double stresses inflicted by combinations of: (i) infection by the necrotrophic fungus Botrytis cinerea, (ii) herbivory by chewing larvae of Pieris rapae, and (iii) drought stress. Each of these stresses induced specific expression profiles over time, in which one-third of all differentially expressed genes was shared by at least two single stresses. Of these, 394 genes were differentially expressed during all three stress conditions, albeit often in opposite directions. When two stresses were applied in sequence, plants displayed transcriptome profiles that were very similar to the second stress, irrespective of the nature of the first stress. Nevertheless, significant first-stress signatures could be identified in the sequential stress profiles. Bioinformatic analysis of the dynamics of co-expressed gene clusters highlighted specific clusters and biological processes of which the timing of activation or repression was altered by a prior stress. The first-stress signatures in second stress transcriptional profiles were remarkably often related to responses to phytohormones, strengthening the notion that hormones are global modulators of interactions between different types of stress. Because prior stresses can affect the level of tolerance against a subsequent stress (e.g. prior herbivory strongly affected resistance to B. cinerea), the first-stress signatures can provide important leads for the identification of molecular players that are decisive in the interactions between stress response pathways.

MEME-LaB: motif analysis in clusters.

Summary: Genome-wide expression analysis can result in large numbers of clusters of co-expressed genes. Although there are tools for ab initio discovery of transcription factor-binding sites, most do not provide a quick and easy way to study large numbers of clusters. To address this, we introduce a web tool called MEME-LaB. The tool wraps MEME (an ab initio motif finder), providing an interface for users to input multiple gene clusters, retrieve promoter sequences, run motif finding and then easily browse and condense the results, facilitating better interpretation of the results from large-scale datasets. Availability: MEME-LaB is freely accessible at: http://wsbc.warwick.ac.uk/wsbcToolsWebpage/. Contact: p.e.brown@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

Temporal transcriptome analysis during drought stress coupled with Bayesian network modeling reveals early drought signaling events and identifies AGL22 as a regulator of primary metabolism. 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.

Architecture and Dynamics of the Jasmonic Acid Gene Regulatory Network

Detailed analysis of temporal reprogramming of gene activity illuminates the complexity of the jasmonic acid (JA) gene regulatory network and pinpoints transcriptional regulators of JA responses. Jasmonic acid (JA) is a critical hormonal regulator of plant growth and defense. To advance our understanding of the architecture and dynamic regulation of the JA gene regulatory network, we performed a high-resolution RNA-seq time series of methyl JA-treated Arabidopsis thaliana at 15 time points over a 16-h period. Computational analysis showed that methyl JA (MeJA) induces a burst of transcriptional activity, generating diverse expression patterns over time that partition into distinct sectors of the JA response targeting specific biological processes. The presence of transcription factor (TF) DNA binding motifs correlated with specific TF activity during temporal MeJA-induced transcriptional reprogramming. Insight into the underlying dynamic transcriptional regulation mechanisms was captured in a chronological model of the JA gene regulatory network. Several TFs, including MYB59 and bHLH27, were uncovered as early network components with a role in pathogen and insect resistance. Analysis of subnetworks surrounding the TFs ORA47, RAP2.6L, MYB59, and ANAC055, using transcriptome profiling of overexpressors and mutants, provided insights into their regulatory role in defined modules of the JA network. Collectively, our work illuminates the complexity of the JA gene regulatory network, pinpoints and validates previously unknown regulators, and provides a valuable resource for functional studies on JA signaling components in plant defense and development.