PROSYSTEMIN: A HUB OF TOMATO PLANT DEFENSE RESPONSES

Plants live in a complex environment suffering various stress constraints. To counteract stress condition plants have evolved sophisticated defense systems. In tomato plants a key role in defense is played by systemin (Sys), an octadecapeptide, released upon leaf damage from a larger precursor, prosystemin (Prosys). Considering the need to reduce the agro-chemicals we investigated foliar and hydroponic application of Sys to tomato plants that increased both direct and indirect defenses (Chapter 1): treated plants strongly reduce growth and vitality of Spodoptera littoralis larvae also damaging the development of future insect generations. In addition, Sys treated plants reduce leaves colonization of the necrotrophic fungus Botrytis cinerea and have an increased level of attractiveness of natural herbivores antagonists. In order to investigate the molecular mechanism underpinning Prosys' defence activation, a prediction study of protein-protein interactions (PPIs) was done (Chapter 2). More than 16000 interactions were captured from the interactome query and, among them, 98 Prosys direct interactors were catalogued using GO terms. Prosys sub-network evidenced that Prosys links with two large groups of kinases and transcription factors confirming that the precursor is associated with the very early steps of plant stress perception. Prosys PPIs were also investigated in vitro and in vivo (Chapter 3). Affinity Purification Mass Spectrometry (AP-MS) detected more than 300 Prosys interactors, including two molecular partners identified in silico, a heat shock protein 70 (Sl-HSP70-1), which plays a key role in stress responses, and NAD-dependent epimerase\dehydratase (NaDED), likely associated with both sugar and hormonal plant defense signalling. Some PPIs were validated through BiFC that confirmed the interaction with an ATP-dependent clp protease, detected with AP-MS, and with the NaDED, detected both in silico and in vitro. BiFC also confirmed two interactors of the in silico network, MYB transcription factor and a MAP-Kinase. Overall, the results proved that Sys is a very effective plant protectant, and its use could reduce the application of chemical pesticide while Prosys is involved in a large number of interactions possibly due to its ID structure and consequent biological function

Microarray data can predict diurnal changes of starch content in the picoalga Ostreococcus

<p>Abstract</p> <p>Background</p> <p>The storage of photosynthetic carbohydrate products such as starch is subject to complex regulation, effected at both transcriptional and post-translational levels. The relevant genes in plants show pronounced daily regulation. Their temporal RNA expression profiles, however, do not predict the dynamics of metabolite levels, due to the divergence of enzyme activity from the RNA profiles.</p> <p>Unicellular phytoplankton retains the complexity of plant carbohydrate metabolism, and recent transcriptomic profiling suggests a major input of transcriptional regulation.</p> <p>Results</p> <p>We used a quasi-steady-state, constraint-based modelling approach to infer the dynamics of starch content during the 12 h light/12 h dark cycle in the model alga <it>Ostreococcus tauri</it>. Measured RNA expression datasets from microarray analysis were integrated with a detailed stoichiometric reconstruction of starch metabolism in <it>O. tauri </it>in order to predict the optimal flux distribution and the dynamics of the starch content in the light/dark cycle. The predicted starch profile was validated by experimental data over the 24 h cycle. The main genetic regulatory targets within the pathway were predicted by <it>in silico </it>analysis.</p> <p>Conclusions</p> <p>A single-reaction description of starch production is not able to account for the observed variability of diurnal activity profiles of starch-related enzymes. We developed a detailed reaction model of starch metabolism, which, to our knowledge, is the first attempt to describe this polysaccharide polymerization while preserving the mass balance relationships. Our model and method demonstrate the utility of a quasi-steady-state approach for inferring dynamic metabolic information in <it>O. tauri </it>directly from time-series gene expression data.</p

\u3ci\u3eZea mays i\u3c/i\u3eRS1563: A Comprehensive Genome-Scale Metabolic Reconstruction of Maize Metabolism

The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR) associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments (i.e., cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular). GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species

Flux-Balance Modeling of Plant Metabolism

Flux-balance modeling of plant metabolic networks provides an important complement to 13C-based metabolic flux analysis. Flux-balance modeling is a constraints-based approach in which steady-state fluxes in a metabolic network are predicted by using optimization algorithms within an experimentally bounded solution space. In the last 2 years several flux-balance models of plant metabolism have been published including genome-scale models of Arabidopsis metabolism. In this review we consider what has been learnt from these models. In addition, we consider the limitations of flux-balance modeling and identify the main challenges to generating improved and more detailed models of plant metabolism at tissue- and cell-specific scales. Finally we discuss the types of question that flux-balance modeling is well suited to address and its potential role in metabolic engineering and crop improvement

Unfolding plant desiccation tolerance : evolution, structure, and function of LEA proteins

When plants colonized land they developed a wide range of adaptations to cope with life in a drier environment. One key adaptation was desiccation tolerance (DT) which is the ability to survive the removal of almost all cellular water without irreparable damage. DT is recurrent in orthodox seeds and in the vegetative body of species commonly known as ‘resurrection plants’. In this thesis a multilevel approach, combining genomics, transcriptomics, gene family evolution, protein structural and functional analysis, and seed physiology was employed in order to tackle curiosity-driven fundamental questions about the major mechanisms governing DT. Several mechanisms were found to be important for DT, including the coordinated activation of cell protection through Late Embryogenesis Abundant (LEA) proteins, which were shown to be common amongst resurrection plants and orthodox seeds. These findings aid to the comprehension of the complexity of DT in plants, and may provide transferrable knowledge to design more water-stress tolerant crops.</p

A Multi-Omics Analysis Pipeline for the Metabolic Pathway Reconstruction in the Orphan Species Quercus ilex

Holm oak (Quercus ilex) is the most important and representative species of the Mediterranean forest and of the Spanish agrosilvo-pastoral “dehesa” ecosystem. Despite its environmental and economic interest, Holm oak is an orphan species whose biology is very little known, especially at the molecular level. In order to increase the knowledge on the chemical composition and metabolism of this tree species, the employment of a holistic and multi-omics approach, in the Systems Biology direction would be necessary. However, for orphan and recalcitrant plant species, specific analytical and bioinformatics tools have to be developed in order to obtain adequate quality and data-density before to coping with the study of its biology. By using a plant sample consisting of a pool generated by mixing equal amounts of homogenized tissue from acorn embryo, leaves, and roots, protocols for transcriptome (NGS-Illumina), proteome (shotgun LC-MS/MS), and metabolome (GC-MS) studies have been optimized. These analyses resulted in the identification of around 62629 transcripts, 2380 protein species, and 62 metabolites. Data are compared with those reported for model plant species, whose genome has been sequenced and is well annotated, including Arabidopsis, japonica rice, poplar, and eucalyptus. RNA and protein sequencing favored each other, increasing the number and confidence of the proteins identified and correcting erroneous RNA sequences. The integration of the large amount of data reported using bioinformatics tools allows the Holm oak metabolic network to be partially reconstructed: from the 127 metabolic pathways reported in KEGG pathway database, 123 metabolic pathways can be visualized when using the described methodology. They included: carbohydrate and energy metabolism, amino acid metabolism, lipid metabolism, nucleotide metabolism, and biosynthesis of secondary metabolites. The TCA cycle was the pathway most represented with 5 out of 10 metabolites, 6 out of 8 protein enzymes, and 8 out of 8 enzyme transcripts. On the other hand, gaps, missed pathways, included metabolism of terpenoids and polyketides and lipid metabolism. The multi-omics resource generated in this work will set the basis for ongoing and future studies, bringing the Holm oak closer to model species, to obtain a better understanding of the molecular mechanisms underlying phenotypes of interest (productive, tolerant to environmental cues, nutraceutical value) and to select elite genotypes to be used in restoration and reforestation programs, especially in a future climate change scenario