A Labor-saving and Repeatable Touch-force Signaling Mutant Screen Protocol for the Study of Thigmomorphogenesis of a Model Plant Arabidopsis thaliana.

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ID: 15162
2019
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Abstract
Plants responding to both intracellular and extracellular mechanical stimulations (or force signals) and develop special morphological changes, a called thigmomorphogenesis. In past decades, several signaling components have been identified and reported for being involved in the mechanotransduction (e.g., calcium ion binding proteins and jasmonic acid biosynthesis enzymes). However, the relatively slow pace of research in the study of force signaling or thigmomorphogenesis is largely attributed to two reasons: the requirement for laborious human hand-manipulated touch induction of thigmomorphogenesis and the force strength errors associated with people's hand-touch. To enhance the efficiency of external force loading on a plant organism, an automatic touch-force loading machine was built. This robotic arm-driven hair brush touches provide a labor-saving and easily repeatable touch-force simulation, unlimited rounds of touch repetition and adjustable touch strength. This hair touch-force loading machine can be used for both large scale screening of touch-force signaling mutants and the phenomics study of plant thigmomorphogenesis. In addition, touch materials such as human hair, can be replaced with other natural materials like animal hair, silk threads and cotton fibers. The automated moving arms on the machine may be equipped with water sprinkling nozzles and air blowers to mimic the natural forces of rain drops and wind, respectively. By using this automatic hair touch-force loading machine in combination with the hand-performed cotton swab touching, we have investigated the touch response of two force signaling mutants, MAP KINASE KINASE 1 (MKK1) and MKK2 plants. The phenomes of the touch-force loaded wild type plants and two mutants were evaluated statistically. They have exhibited significant differences in touch response.
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wang2019ajournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wang, Kai;Law, Kayin;Leung, Manhin;Wong, Waishing;Li, Ning;
Journal Journal of visualized experiments : JoVE
Year 2019
DOI
10.3791/59392
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