Investigating the biological roles of the HSPRO genes in Arabidopsis thaliana

As a consequence of an immobile lifestyle, plants have had to evolve appropriate perception mechanisms and responses to diverse environmental stresses. Stress can be the result of both biotic and abiotic agents and the ORTHOLOG OF SUGAR BEET HS1 PRO-1 (HSPRO 1) and HSPRO2 genes were previously shown to be induced in response to several stresses including infection with Pseudomonas syringae and drought stress in Arabidopsis thaliana. The aim of this study was to characterise the biological role(s) played by these proteins in Arabidopsis. Several bioinformatics approaches provided evidence that supported function of both genes in response to both biotic and abiotic stresses and identified potential regulatory elements that may drive HSPRO gene expression during stress responses. Accordingly, analysis of null hspro mutants revealed antagonistic functions of the two proteins in PAMP-triggered immunity to P. syringae infections of shoot tissues and osmotic stress tolerance in plant roots. HSPRO proteins have been shown to interact with a central integrator of stress and energy signalling, SUCROSE NON-FERMENTING-1-RELATED KINASE1 (SnRK1) and microarray analysis of the null mutants suggested potential roles in carbohydrate signalling. An array of energy responsive genes including a subset of SnRK1 targets were misregulated in hspro mutants under standard growth conditions supporting involvement of HSPRO in energy signalling. Mutant phenotype and gene expression analysis revealed that HSPRO2 may be of importance in energy perception as hspro2 seeds were hypersensitive to exogenous glucose during germination, and that perception and/or signalling of low energy status may require HSPRO2. Although HSPRO2 expression may be driven via perception of environmental stress cues, promoter-luciferase assays revealed a diurnal expression pattern of the gene that was driven by the circadian clock. However, phenotypic analysis did not reveal a requirement of HSPRO2 for normal clock modulation. Since stress perception typically causes fluctuations in energy levels, it is proposed that HSPRO genes are important for the integration of energy and stress signalling in an effort to maintain a homeostatic balance between coping with environmental stress and normal growth and development

Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant

Gene regulatory networks (GRNs) consist of different molecular interactions that closely work together to establish proper gene expression in time and space. Especially in higher eukaryotes, many questions remain on how these interactions collectively coordinate gene regulation. We study high quality GRNs consisting of undirected protein-protein, genetic and homologous interactions, and directed protein-DNA, regulatory and miRNA-mRNA interactions in the worm Caenorhabditis elegans and the plant Ara-bidopsis thaliana. Our data-integration framework integrates interactions in composite network motifs, clusters these in biologically relevant, higher-order topological network motif modules, overlays these with gene expression profiles and discovers novel connections between modules and regulators. Similar modules exist in the integrated GRNs of worm and plant. We show how experimental or computational methodologies underlying a certain data type impact network topology. Through phylogenetic decomposition, we found that proteins of worm and plant tend to functionally interact with proteins of a similar age, while at the regulatory level TFs favor same age, but also older target genes. Despite some influence of the duplication mode difference, we also observe at the motif and module level for both species a preference for age homogeneity for undirected and age heterogeneity for directed interactions. This leads to a model where novel genes are added together to the GRNs in a specific biological functional context, regulated by one or more TFs that also target older genes in the GRNs. Overall, we detected topological, functional and evolutionary properties of GRNs that are potentially universal in all species

Tree Peony Species Are a Novel Resource for Production of α-Linolenic Acid

Tree peony is known worldwide for its excellent ornamental and medical values, but recent reports that their seeds contain over 40% α-linolenic acid (ALA), an essential fatty acid for humans drew additional interest of biochemists. To understand the key factors that contribute to this rich accumulation of ALA, we carried out a comprehensive study of oil accumulation in developing seeds of nine wild tree peony species. The fatty acid content and composition was highly variable among the nine species; however, we selected a high- (P. rockii) and low-oil (P. lutea) accumulating species for a comparative transcriptome analysis. Similar to other oilseed transcriptomic studies, upregulation of select genes involved in plastidial fatty acid synthesis, and acyl editing, desaturation and triacylglycerol assembly in the endoplasmic reticulum was noted in seeds of P. rockii relative to P. lutea. Also, in association with the ALA content, transcript levels for fatty acid desaturases (SAD, FAD2 and FAD3), which encode for enzymes necessary for polyunsaturated fatty acid synthesis were higher in P. rockii compared to P. lutea. We further showed that the overexpression of PrFAD2 and PrFAD3 in Arabidopsis increased linoleic and α-linolenic acid content, respectively and modulated their final ratio in the seed oil. In conclusion, we identified the key steps that contribute to efficient ALA synthesis and validated the necessary desaturases in P. rockii that are responsible for not only increasing oil content but also modulating 18:2/18:3 ratio in seeds. Together, these results will aid to improve essential fatty acid content in seeds of tree peonies and other crops of agronomic interest

Two Novel Methods for Clustering Short Time-Course Gene Expression Profiles

As genes with similar expression pattern are very likely having the same biological function, cluster analysis becomes an important tool to understand and predict gene functions from gene expression profi les. In many situations, each gene expression profi le only contains a few data points. Directly applying traditional clustering algorithms to such short gene expression profi les does not yield satisfactory results. Developing clustering algorithms for short gene expression profi les is necessary. In this thesis, two novel methods are developed for clustering short gene expression pro files. The fi rst method, called the network-based clustering method, deals with the defect of short gene expression profi les by generating a gene co-expression network using conditional mutual information (CMI), which measures the non-linear relationship between two genes, as well as considering indirect gene relationships in the presence of other genes. The network-based clustering method consists of two steps. A gene co-expression network is firstly constructed from short gene expression profi les using a path consistency algorithm (PCA) based on the CMI between genes. Then, a gene functional module is identi ed in terms of cluster cohesiveness. The network-based clustering method is evaluated on 10 large scale Arabidopsis thaliana short time-course gene expression profi le datasets in terms of gene ontology (GO) enrichment analysis, and compared with an existing method called Clustering with Over-lapping Neighbourhood Expansion (ClusterONE). Gene functional modules identi ed by the network-based clustering method for 10 datasets returns target GO p-values as low as 10-24, whereas the original ClusterONE yields insigni cant results. In order to more speci cally cluster gene expression profi les, a second clustering method, namely the protein-protein interaction (PPI) integrated clustering method, is developed. It is designed for clustering short gene expression profi les by integrating gene expression profi le patterns and curated PPI data. The method consists of the three following steps: (1) generate a number of prede ned profi le patterns according to the number of data points in the profi les and assign each gene to the prede fined profi le to which its expression profi le is the most similar; (2) integrate curated PPI data to refi ne the initial clustering result from (1); (3) combine the similar clusters from (2) to gradually reduce cluster numbers by a hierarchical clustering method. The PPI-integrated clustering method is evaluated on 10 large scale A. thaliana datasets using GO enrichment analysis, and by comparison with an existing method called Short Time-series Expression Miner (STEM). Target gene functional clusters identi ed by the PPI-integrated clustering method for 10 datasets returns GO p-values as low as 10-62, whereas STEM returns GO p-values as low as 10-38. In addition to the method development, obtained clusters by two proposed methods are further analyzed to identify cross-talk genes under fi ve stress conditions in root and shoot tissues. A list of potential abiotic stress tolerant genes are found