High-resolution temporal profiling of transcripts during Arabidopsis leaf senescence reveals a distinct chronology of processes and regulation

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 highresolution 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

Modelling transcriptional networks in plant senescence

Senescence is a highly regulated developmental process in plants in which nutrients are remobilised from organs which are no longer required or are stressed so that they may be used by organs which are only just developing. Whilst much is known about the causes of and the resulting outcomes of this process, very little is known about the genetic machinery which link them. Some genes have been identified as having a regulatory role in the senescence process, but many of these have been determined by a forward genetics approach whereby mutants are randomly screened for phenotypical e↵ects. A much better approach, where possible, is the reverse genetics approach whereby mutants are sought for testing as they are suspected to demonstrate a phenotypical e↵ect. It was the purpose of this study to find novel ways of identifying those genes which may be highly regulatory of the senescence process and to determine how they are able to lead from several di↵erent known causes of senescence through to the senescence response itself. It was hypothesised that, using measurements of the expression levels of a very large number of genes throughout the senescence process, theoretical models of regulation between those genes could be determined and that these theoretical models would allow specific interactions to be identified and explained using biological validation techniques. A large microarray experiment, performed prior to the start of this project, measured the expression of over 30,000 genes in the Arabidopsis thaliana genome over a period of 21 days during natural senescence. By cleaning this data and fitting an ANOVA driven model to the resulting intensity measurements, it has been possible to separate e↵ects leading to observed expression changes. The levels of each of these e↵ects were tested by F-tests and this has allowed the identification of 8,878 genes which are significantly di↵erentially expressed during senescence. By first using theoretical models to find genes amongst the set of 8,878 which demonstrate highly robust regulatory behaviour on other genes in the set, 118 genes were able to be isolated for further study. A senescence phenotype screen was developed to assess reduced-expression mutants of many of those 118 genes and 8 were shown to have a significantly altered timing of senescence when compared with wild-type plants. The surrounding networks of each of those 8 genes were formed by applying theoretical regulatory network modelling in another novel manner similar to a Metropolis-Hastings approach which identified a set of 75 genes providing a testable regulatory network model. The resulting network model has been tested biologically to establish the accuracy of the predictions. Whilst many of the predictions were not confirmed, a vast network has been identified surrounding two of the highly regulatory genes indicating a junction of two separate pathways leading to the senescence response and providing a network structure which could be used in another round of theory and validation. Additionally, these results introduce new interesting questions about how the senescence network may have evolved to respond to so many inputs

Supply Act (No. 1), 1981, No. 50

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

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