The process of leaf senescence is induced by an extensive range of developmental and environmental signals and controlled by multiple, cross-linking pathways, many of which overlap with plant stress-response signals. Elucidation of this complex regulation requires a step beyond a traditional one-gene-at-a-time analysis. Application of a more global analysis using statistical and mathematical tools of systems biology is an approach that is being applied to address this problem. A variety of modelling methods applicable to the analysis of current and future senescence data are reviewed and discussed using some senescence-specific examples. Network modelling with a senescence transcriptome time course followed by testing predictions with gene-expression data illustrates the application of systems biology tools.
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, for example high correlation of expression profiles across multiple time series data sets. However, numbers of up- or down-regulated 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.
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 H?O? 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.
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.
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.
A unique broccoli × broccoli doubled haploid (DH) population has been created from the F(1) of a cross between two DH broccoli lines derived from cultivars Green Duke and Marathon. We genotyped 154 individuals from this population with simple sequence repeat and amplified fragment length polymorphism markers to create a B. oleracea L. var. italica intra-crop specific framework linkage map. The map is composed of nine linkage groups with a total length of 946.7 cM. Previous published B. oleracea maps have been constructed using diverse crosses between morphotypes of B. oleracea; this map therefore represents a useful breeding resource for the dissection of broccoli specific traits. Phenotype data have been collected from the population over five growing seasons; the framework linkage map has been used to locate quantitative trait loci for agronomically important broccoli traits including head weight (saleable yield), head diameter, stalk diameter, weight loss and relative weight loss during storage, as well as traits for broccoli leaf architecture. This population and associated linkage map will aid breeders to directly map agronomically important traits for the improvement of elite broccoli cultivars.
Petal development and senescence entails a normally irreversible process. It starts with petal expansion and pigment production, and ends with nutrient remobilization and ultimately cell death. In many species this is accompanied by petal abscission. Post-harvest stress is an important factor in limiting petal longevity in cut flowers and accelerates some of the processes of senescence such as petal wilting and abscission. However, some of the effects of moderate stress in young flowers are reversible with appropriate treatments. Transcriptomic studies have shown that distinct gene sets are expressed during petal development and senescence. Despite this, the overlap in gene expression between developmental and stress-induced senescence in petals has not been fully investigated in any species. Here a custom-made cDNA microarray from Alstroemeria petals was used to investigate the overlap in gene expression between developmental changes (bud to first sign of senescence) and typical post-harvest stress treatments. Young flowers were stressed by cold or ambient temperatures without water followed by a recovery and rehydration period. Stressed flowers were still at the bud stage after stress treatments. Microarray analysis showed that ambient dehydration stress accelerates many of the changes in gene expression patterns that would normally occur during developmental senescence. However, a higher proportion of gene expression changes in response to cold stress were specific to this stimulus and not senescence related. The expression of 21 transcription factors was characterized, showing that overlapping sets of regulatory genes are activated during developmental senescence and by different stresses.
A major goal of the life sciences is to understand how molecular processes control phenotypes. Because understanding biological systems relies on the work of multiple laboratories, biologists implicitly assume that organisms with the same genotype will display similar phenotypes when grown in comparable conditions. We investigated to what extent this holds true for leaf growth variables and metabolite and transcriptome profiles of three Arabidopsis (Arabidopsis thaliana) genotypes grown in 10 laboratories using a standardized and detailed protocol. A core group of four laboratories generated similar leaf growth phenotypes, demonstrating that standardization is possible. But some laboratories presented significant differences in some leaf growth variables, sometimes changing the genotype ranking. Metabolite profiles derived from the same leaf displayed a strong genotype x environment (laboratory) component. Genotypes could be separated on the basis of their metabolic signature, but only when the analysis was limited to samples derived from one laboratory. Transcriptome data revealed considerable plant-to-plant variation, but the standardization ensured that interlaboratory variation was not considerably larger than intralaboratory variation. The different impacts of the standardization on phenotypes and molecular profiles could result from differences of temporal scale between processes involved at these organizational levels. Our findings underscore the challenge of describing, monitoring, and precisely controlling environmental conditions but also demonstrate that dedicated efforts can result in reproducible data across multiple laboratories. Finally, our comparative analysis revealed that small variations in growing conditions (light quality principally) and handling of plants can account for significant differences in phenotypes and molecular profiles obtained in independent laboratories.
Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to certain characteristics. The majority of clustering techniques are based on distance or visual similarity measures which may not be suitable for clustering of temporal microarray data where the sequential nature of time is important. We present a Granger causality based technique to cluster temporal microarray gene expression data, which measures the interdependence between two time-series by statistically testing if one time-series can be used for forecasting the other time-series or not.
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.
Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs.
Senescence of plant organs is a genetically controlled process that regulates cell death to facilitate nutrient recovery and recycling, and frequently precedes, or is concomitant with, ripening of reproductive structures. In Arabidopsis thaliana, the seeds are contained within a silique, which is itself a photosynthetic organ in the early stages of development and undergoes a programme of senescence prior to dehiscence. A transcriptional analysis of the silique wall was undertaken to identify changes in gene expression during senescence and to correlate these events with ultrastructural changes. The study revealed that the most highly up-regulated genes in senescing silique wall tissues encoded seed storage proteins, and the significance of this finding is discussed. Global transcription profiles of senescing siliques were compared with those from senescing Arabidopsis leaf or petal tissues using microarray datasets and metabolic pathway analysis software (MapMan). In all three tissues, members of NAC and WRKY transcription factor families were up-regulated, but components of the shikimate and cell-wall biosynthetic pathways were down-regulated during senescence. Expression of genes encoding ethylene biosynthesis and action showed more similarity between senescing siliques and petals than between senescing siliques and leaves. Genes involved in autophagy were highly expressed in the late stages of death of all plant tissues studied, but not always during the preceding remobilization phase of senescence. Analyses showed that, during senescence, silique wall tissues exhibited more transcriptional features in common with petals than with leaves. The shared and distinct regulatory events associated with senescence in the three organs are evaluated and discussed.
Conserved noncoding sequences (CNSs) in DNA are reliable pointers to regulatory elements controlling gene expression. Using a comparative genomics approach with four dicotyledonous plant species (Arabidopsis thaliana, papaya [Carica papaya], poplar [Populus trichocarpa], and grape [Vitis vinifera]), we detected hundreds of CNSs upstream of Arabidopsis genes. Distinct positioning, length, and enrichment for transcription factor binding sites suggest these CNSs play a functional role in transcriptional regulation. The enrichment of transcription factors within the set of genes associated with CNS is consistent with the hypothesis that together they form part of a conserved transcriptional network whose function is to regulate other transcription factors and control development. We identified a set of promoters where regulatory mechanisms are likely to be shared between the model organism Arabidopsis and other dicots, providing areas of focus for further research.
After initiation of the leaf primordium, biomass accumulation is controlled mainly by cell proliferation and expansion in the leaves(1). However, the Arabidopsis leaf is a complex organ made up of many different cell types and several structures. At the same time, the growing leaf contains cells at different stages of development, with the cells furthest from the petiole being the first to stop expanding and undergo senescence(1). Different cells within the leaf are therefore dividing, elongating or differentiating; active, stressed or dead; and/or responding to stimuli in sub-sets of their cellular type at any one time. This makes genomic study of the leaf challenging: for example when analyzing expression data from whole leaves, signals from genetic networks operating in distinct cellular response zones or cell types will be confounded, resulting in an inaccurate profile being generated. To address this, several methods have been described which enable studies of cell specific gene expression. These include laser-capture microdissection (LCM)(2) or GFP expressing plants used for protoplast generation and subsequent fluorescence activated cell sorting (FACS)(3,4), the recently described INTACT system for nuclear precipitation(5) and immunoprecipitation of polysomes(6). FACS has been successfully used for a number of studies, including showing that the cell identity and distance from the root tip had a significant effect on the expression profiles of a large number of genes(3,7). FACS of GFP lines have also been used to demonstrate cell-specific transcriptional regulation during root nitrogen responses and lateral root development(8), salt stress(9) auxin distribution in the root(10) and to create a gene expression map of the Arabidopsis shoot apical meristem(11). Although FACS has previously been used to sort Arabidopsis leaf derived protoplasts based on autofluorescence(12,13), so far the use of FACS on Arabidopsis lines expressing GFP in the leaves has been very limited(4). In the following protocol we describe a method for obtaining Arabidopsis leaf protoplasts that are compatible with FACS while minimizing the impact of the protoplast generation regime. We demonstrate the method using the KC464 Arabidopsis line, which express GFP in the adaxial epidermis(14), the KC274 line, which express GFP in the vascular tissue(14) and the TP382 Arabidopsis line, which express a double GFP construct linked to a nuclear localization signal in the guard cells (data not shown; Figure 2). We are currently using this method to study both cell-type specific expression during development and stress, as well as heterogeneous cell populations at various stages of senescence.
Transcriptional reprogramming forms a major part of a plants 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.
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.
Entry into mitosis is regulated by cyclin dependent kinases that in turn are phosphoregulated. In most eukaryotes, phosphoregulation is through WEE1 kinase and CDC25 phosphatase. In higher plants a homologous CDC25 gene is unconfirmed and hence the mitotic inducer Schizosaccharomyces pombe (Sp) cdc25 has been used as a tool in transgenic plants to probe cell cycle function. Expression of Spcdc25 in tobacco BY-2 cells accelerates entry into mitosis and depletes cytokinins; in whole plants it stimulates lateral root production. Here we show, for the first time, that alterations to cytokinin and ethylene signaling explain the rooting phenotype elicited by Spcdc25 expression in Arabidopsis.
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