Plants grown under abiotic stress conditions such as salinity, drought, cold, or heat display a variety of molecular, biochemical, and physiological changes including excessive generation of reactive oxygen species (ROS). The over‐production of ROS causes DNA damage and protein cross‐linking, which can lead to poor molecular transport, reduced enzyme activity, and loss of cell functions. The accumulation of ROS in response to stress also activates several signalling pathways which, in coordination with hormones such as ethylene and abscisic acid (ABA), allow for an adaptive response to stress, resulting in adjustments in plant growth and development in an attempt to maximise survival. Thus, the increase in ROS level by environmental stress results in both damage and an adaptive response; the outcome of the stress, i.e. survival or death then depends on the severity of the stress in combination with the stress response. In this article, we review elements of abiotic stress responses, in particular how ROS work together with the hormones ethylene and ABA to induce distinct stress responses in tissues of different age.
Leaf senescence is the final stage of leaf development and is induced by the gradual occurrence of Age-Related Changes (ARCs). The process of leaf senescence has been well described but the cellular events leading to this process are still poorly understood. By analysis of progressively ageing, but not yet senescing Arabidopsis thaliana rosette leaves, we aimed to better understand processes occurring prior to the onset of senescence. Using gene expression analysis, we found that as leaves mature, genes responding to oxidative stress and genes involved in stress-hormone biosynthesis and signalling were upregulated. A decrease in primary metabolites that provide protection against oxidative stress provided a possible explanation for the increased stress signature. The gene expression and metabolomics changes occurred concomitantly to a decrease in drought, salinity and dark stress tolerance of individual leaves. Importantly, stress-related genes showed elevated expression in the early ageing mutant old5 and decreased expression in the delayed-ageing mutant ore9. We propose that the decreased stress tolerance with age results from the occurrence of senescence-inducing ARCs that is integrated into the leaf developmental program, and that this ensures a timely and certain death.
Abiotic stresses, which at the molecular level leads to oxidative damage, are major determinants of crop yield loss worldwide. Therefore, considerable efforts are directed towards developing strategies for their limitation and mitigation. Here the superoxide-inducing agent paraquat (PQ) was used to generate oxidative stress in the model species Arabidopsis thaliana and the crops tomato and pepper. Pre-treatment with the biostimulant SuperFifty (SF) effectively and universally suppressed PQ-induced leaf lesions, H2O2 build up, cell destruction and photosynthesis inhibition. To further investigate the stress responses and SF-induced protection at the molecular level, we investigated the metabolites by GC-MS metabolomics. PQ induced specific metabolic changes such as accumulation of free amino acids (AA) and stress metabolites. These changes were fully prevented by the SF pre-treatment. Moreover, the metabolic changes of the specific groups were tightly correlating with their phenotypic characteristics. Overall, this study presents physiological and metabolomics data which shows that SF protects against oxidative stress in all three plant species.
Ageing in plants is a highly coordinated and complex process that starts with the birth of the plant or plant organ and ends with its death. A vivid manifestation of the final stage of leaf ageing is exemplified by the autumn colours of deciduous trees. Over the past decades, technological advances have allowed plant ageing to be studied on a systems biology level, by means of multi-omics approaches. Here, we review some of these studies and argue that these provide strong support for basic metabolic processes as drivers for ageing. In particular, core cellular processes that control the metabolism of chlorophyll, amino acids, sugars, DNA and reactive oxygen species correlate with leaf ageing. However, while multi-omics studies excel at identifying correlative processes and pathways, molecular genetic approaches can provide proof that such processes and pathways control ageing, by means of knock-out and ectopic expression of predicted regulatory genes. Therefore, we also review historic and current molecular evidence to directly test the hypotheses unveiled by the systems biology approaches. We found that the molecular genetic approaches, by and large, confirm the multi-omics-derived hypotheses with notable exceptions, where there is scant evidence that chlorophyll and DNA metabolism are important drivers of leaf ageing. We present a model that summarises the core cellular processes that drive leaf ageing and propose that developmental processes are tightly linked to primary metabolism to inevitably lead to ageing and death.
Postharvest deterioration of fruits and vegetables can be accelerated by biological, environmental, and physiological stresses. Fully understanding tissue response to harvest will provide new opportunities for limiting postharvest losses during handling and storage. The model plant Arabidopsis thaliana (Arabidopsis) has many attributes that make it excellent for studying the underlying control of postharvest responses. It is also one of the best resourced plants with numerous web-based bioinformatic programs and large numbers of mutant collections. Here we introduce a novel assay system called AIDA (the Arabidopsis Inflorescence Degreening Assay) that we developed for understanding postharvest response of immature tissues. We also demonstrate how the high-throughput screening capability of AIDA can be used with mapping technologies (high-resolution melting [HRM] and needle in the k-stack [NIKS]) to identify regulators of postharvest senescence in ethyl methanesulfonate (EMS) mutagenized plant populations. Whether it is best to use HRM or NIKS or both technologies will depend on your laboratory facilities and computing capabilities.
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