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      Physiological and proteomic analyses reveal the effects of exogenous nitrogen in diminishing Cd detoxification in Acacia auriculiformis

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      Ecotoxicology and Environmental Safety
      Elsevier BV

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            Determinations of total carotenoids and chlorophyllsaandbof leaf extracts in different solvents

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              Plant hormone-mediated regulation of stress responses

              Background Being sessile organisms, plants are often exposed to a wide array of abiotic and biotic stresses. Abiotic stress conditions include drought, heat, cold and salinity, whereas biotic stress arises mainly from bacteria, fungi, viruses, nematodes and insects. To adapt to such adverse situations, plants have evolved well-developed mechanisms that help to perceive the stress signal and enable optimal growth response. Phytohormones play critical roles in helping the plants to adapt to adverse environmental conditions. The elaborate hormone signaling networks and their ability to crosstalk make them ideal candidates for mediating defense responses. Results Recent research findings have helped to clarify the elaborate signaling networks and the sophisticated crosstalk occurring among the different hormone signaling pathways. In this review, we summarize the roles of the major plant hormones in regulating abiotic and biotic stress responses with special focus on the significance of crosstalk between different hormones in generating a sophisticated and efficient stress response. We divided the discussion into the roles of ABA, salicylic acid, jasmonates and ethylene separately at the start of the review. Subsequently, we have discussed the crosstalk among them, followed by crosstalk with growth promoting hormones (gibberellins, auxins and cytokinins). These have been illustrated with examples drawn from selected abiotic and biotic stress responses. The discussion on seed dormancy and germination serves to illustrate the fine balance that can be enforced by the two key hormones ABA and GA in regulating plant responses to environmental signals. Conclusions The intricate web of crosstalk among the often redundant multitudes of signaling intermediates is just beginning to be understood. Future research employing genome-scale systems biology approaches to solve problems of such magnitude will undoubtedly lead to a better understanding of plant development. Therefore, discovering additional crosstalk mechanisms among various hormones in coordinating growth under stress will be an important theme in the field of abiotic stress research. Such efforts will help to reveal important points of genetic control that can be useful to engineer stress tolerant crops.
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                Author and article information

                Journal
                Ecotoxicology and Environmental Safety
                Ecotoxicology and Environmental Safety
                Elsevier BV
                01476513
                January 2022
                January 2022
                : 229
                : 113057
                Article
                10.1016/j.ecoenv.2021.113057
                34883325
                0a618b9c-6a88-4de1-baac-2581c65fa2a6
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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