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Translatome and metabolome effects triggered by gibberellins during rosette growth in Arabidopsis
(2012)
Although gibberellins (GAs) are well known for their growth control function, little is known about their effects on primary metabolism. Here the modulation of gene expression and metabolic adjustment in response to changes in plant (Arabidopsis thaliana) growth imposed on varying the gibberellin regime were evaluated. Polysomal mRNA populations were profiled following treatment of plants with paclobutrazol (PAC), an inhibitor of GA biosynthesis, and gibberellic acid (GA(3)) to monitor translational regulation of mRNAs globally. Gibberellin levels did not affect levels of carbohydrates in plants treated with PAC and/or GA(3). However, the tricarboxylic acid cycle intermediates malate and fumarate, two alternative carbon storage molecules, accumulated upon PAC treatment. Moreover, an increase in nitrate and in the levels of the amino acids was observed in plants grown under a low GA regime. Only minor changes in amino acid levels were detected in plants treated with GA(3) alone, or PAC plus GA(3). Comparison of the molecular changes at the transcript and metabolite levels demonstrated that a low GA level mainly affects growth by uncoupling growth from carbon availability. These observations, together with the translatome changes, reveal an interaction between energy metabolism and GA-mediated control of growth to coordinate cell wall extension, secondary metabolism, and lipid metabolism.
The ability of some chemical compounds to cause oxidative stress offers a fast and convenient way to study the responses of plants to reactive oxygen species (ROS). In order to unveil potential novel genetic players of the ROS-regulatory network, a population of similar to 2,000 randomly selected Arabidopsis thaliana T-DNA insertion mutants was screened for ROS sensitivity/resistance by growing seedlings on agar medium supplemented with stress-inducing concentrations of the superoxide-eliciting herbicide methyl viologen or the catalase inhibitor 3-amino-triazole. A semi-robotic setup was used to capture and analyze images of the chemically treated seedlings which helped interpret the screening results by providing quantitative information on seedling area and healthy-to-chlorotic tissue ratios for data verification. A ROS-related phenotype was confirmed in three of the initially selected 33 mutant candidates, which carry T-DNA insertions in genes encoding a Ring/Ubox superfamily protein, ABI5 binding protein 1 (AFP1), previously reported to be involved in ABA signaling, and a protein of unknown function, respectively. In addition, we identified six mutants, most of which have not been described yet, that are related to growth or chloroplast development and show defects in a ROS-independent manner. Thus, semi-automated image capturing and phenotyping applied on publically available T-DNA insertion collections adds a simple means for discovering novel mutants in complex physiological processes and identifying the genes involved.
To gain a deeper understanding of the mechanisms behind biomass accumulation, it is important to study plant growth behavior. Manually phenotyping large sets of plants requires important human resources and expertise and is typically not feasible for detection of weak growth phenotypes. Here, we established an automated growth phenotyping pipeline for Arabidopsis thaliana to aid researchers in comparing growth behaviors of different genotypes.
The analysis pipeline includes automated image analysis of two-dimensional digital plant images and evaluation of manually annotated information of growth stages. It employs linear mixed-effects models to quantify genotype effects on total rosette area and relative leaf growth rate (RLGR) and ANOVAs to quantify effects on developmental times.
Using the system, a single researcher can phenotype up to 7000 plants d(-1). Technical variance is very low (typically < 2%). We show quantitative results for the growth-impaired starch-excessmutant sex4-3 and the growth-enhancedmutant grf9.
We show that recordings of environmental and developmental variables reduce noise levels in the phenotyping datasets significantly and that careful examination of predictor variables (such as d after sowing or germination) is crucial to avoid exaggerations of recorded phenotypes and thus biased conclusions.