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Review
. 2020 Oct 31;21(21):8179.
doi: 10.3390/ijms21218179.

State-of-the-Art Technologies for Understanding Brassinosteroid Signaling Networks

Haijiao Wang  1 , Song Song  2 , Huaqiang Cheng  3 , Yan-Wen Tan  3
Affiliations
Review

State-of-the-Art Technologies for Understanding Brassinosteroid Signaling Networks

Haijiao Wang et al. Int J Mol Sci. .

Abstract

Brassinosteroids, the steroid hormones of plants, control physiological and developmental processes through its signaling pathway. The major brassinosteroid signaling network components, from the receptor to transcription factors, have been identified in the past two decades. The development of biotechnologies has driven the identification of novel brassinosteroid signaling components, even revealing several crosstalks between brassinosteroid and other plant signaling pathways. Herein, we would like to summarize the identification and improvement of several representative brassinosteroid signaling components through the development of new technologies, including brassinosteroid-insensitive 1 (BRI1), BRI1-associated kinase 1 (BAK1), BR-insensitive 2 (BIN2), BRI1 kinase inhibitor 1 (BKI1), BRI1-suppressor 1 (BSU1), BR signaling kinases (BSKs), BRI1 ethyl methanesulfonate suppressor 1 (BES1), and brassinazole resistant 1 (BZR1). Furthermore, improvement of BR signaling knowledge, such as the function of BKI1, BES1 and its homologous through clustered regularly interspaced short palindromic repeats (CRISPR), the regulation of BIN2 through single-molecule methods, and the new in vivo interactors of BIN2 identified by proximity labeling are described. Among these technologies, recent advanced methods proximity labeling and single-molecule methods will be reviewed in detail to provide insights to brassinosteroid and other phytohormone signaling pathway studies.

Keywords: brassinosteroids; proximity labeling; signaling; single-molecule methods; technologies.

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Conflict of interest statement

The authors declare that no competing interest exist.

Figures

Figure 1
Figure 1
A simplified model for brassinosteroids (BRs) signaling pathway.
Figure 2
Figure 2
Three major categories of proximity labeling. Each category is illustrated by one or two representative enzyme systems. (a) Binary-candidate PL method using BirA and BAP attached to the bait and prey proteins, respectively. The reaction is induced by biotin and ATP; (b) Peroxidase based PL using Horseradish peroxidase (HRP)-based method on cell surface or APEX in a cellular compartment. The reaction is induced by modified biotin molecules and H2O2; (c) Promiscuous PL enzyme illustrated by BioID, which has a working distance around 10 nm. This PL reaction is induced by biotin.
Figure 3
Figure 3
(a) Schematic of the total internal reflection fluorescence microscopy (TIRFM) and variable-angle (VA)-TIRFM layout. A focused laser beam (green) is reflected at the back of the objective focal plane, passed through the objective lens, and totally internally reflected. The signal (red) passes through the dichroic and is captured on a camera. By changing the angle of the laser beam (θ1), the incident angle (θ2) is variable and converts TIRFM to VA-TIRFM. (b) Enlarged view of the total reflection area in the sample and slide surface. Fluorophores within the evanescent filed field (approximately 100 nm) were excited (red and yellow). (c) Schematic representation of CoSMoS. If fluorophore-s labeled target proteins associate with each other, positions of spots in each channel (red box in channel 1 and yellow circle in channel 2) can be overlaid in the merged image. (d) An example of the double-step bleaching event trajectory. (e) Schematic representation of fluorescence correlation spectroscopy/fluorescence cross-correlation spectroscopy (FCS/FCCS) and photon counting histogram (PCH). The diffusion of fluorophore-labeled molecules in and out of the observed volume induces fluctuations in fluorescence with time. Their diffusion parameters and number of particles can be obtained by fitting the fluorescence fluctuation curve to an adequate diffusion model.
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