Trends in Plant Science
Abiotic stress seriesReactive oxygen gene network of plants
Section snippets
Modulation of ROS signaling by the reactive oxygen gene network of plants
Whereas Ca2+ signaling is predominantly controlled in plants by storage and release, ROS signaling is controlled by production and scavenging (Figure 1). Different developmental or environmental signals feed into the ROS signaling network and perturb ROS homeostasis in a compartment-specific or even cell-specific manner. Perturbed ROS levels are perceived by different proteins, enzymes or receptors and modulate different developmental, metabolic and defense pathways. ROS can be generated by
Production of ROS in plants
Organelles with a highly oxidizing metabolic activity or with an intense rate of electron flow, such as chloroplasts, mitochondria and microbodies, are a major source of ROS production in plant cells. Together with an extensive battery of oxidases, the plant cell is well armed for bountiful yet flexible ROS production. In chloroplasts, the primary sources of ROS production are the Mehler reaction and the antenna pigments [2]. Production of ROS by these sources is enhanced in plants by
Enzymatic components of the ROS-scavenging pathways of plants
Major ROS-scavenging enzymes of plants include superoxide dismutase (SOD), ascorbate peroxidase (APX), catalase (CAT), glutathione peroxidase (GPX) and peroxiredoxin (PrxR) (Table 1). Together with the antioxidants ascorbic acid and glutathione [35], these enzymes provide cells with highly efficient machinery for detoxifying O2− and H2O2. The balance between SODs and the different H2O2-scavenging enzymes in cells is considered to be crucial in determining the steady-state level of O2− and H2O2.
Cellular localization and coordination of the ROS-scavenging pathways of plants
The various scavenging enzymes encoded by the ROS network can be found in almost every subcellular compartment (Figure 2). In addition, usually more than one enzymatic scavenging activity per a particular ROS can be found in each of the different compartments (e.g. GPXs, PrxRs and APXs in the cytosol and chloroplast, and APXs and CATs in peroxisomes; Figure 2). When the relative function of the different enzymes in the different cellular compartments is considered, it is important to remember
Gene annotation and expression of the ROS network in Arabidopsis
Table 1 and the table in the supplementary material (available in the on-line version) summarize all known ROS-scavenging genes and NADPH oxidases in Arabidopsis. Expression data for the different genes in three different knockout or antisense lines (Apx1, CSD2 and Cat2) and in plants subjected to different abiotic stresses (e.g. drought, salt, cold or high light) are also included. Although data were assembled from different experiments and should only be considered from a qualitative point of
Key components of the reactive oxygen gene network identified by reverse genetics
Recent studies of knockout and antisense lines for Cat2, Apx1, chlAOX, mitAOX, CSD2, 2-cysteine PrxR and various NADPH oxidases have revealed a strong link between ROS and processes such as growth, development, stomatal responses and biotic and abiotic stress responses 7, 8, 50, 52, 57, 59, 60, 61, 62. These findings demonstrate the complex nature of the ROS gene network in plants and its modulation of key biological processes. Although mutants for all the proteins listed above are viable,
ROS signal transduction pathway of plants
Recent studies in Arabidopsis have uncovered some of the key components involved in the ROS signal transduction pathway of plants. Although the receptors for ROS are unknown at present, it has been suggested that plant cells sense ROS via at least three different mechanisms (Figure 3): (i) unidentified receptor proteins; (ii) redox-sensitive transcription factors, such as NPR1 or HSFs; and (iii) direct inhibition of phosphatases by ROS 6, 13, 20, 64.
Downstream signaling events associated with
Acknowledgements
We thank Ivan Baxter and Jeffery Harper for sharing unpublished work. This work was supported by funding from The National Science Foundation (NSF-0431327) and a grant from the Research Fund of the Ghent University (Geconcerteerde Onderzoeksacties no. 12051403).
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