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  1. Wong HL, Akamatsu A, Wang Q, Higuchi M, Matsuda T, Okuda J, et al.
    Plant Methods, 2018;14:56.
    PMID: 30002723 DOI: 10.1186/s13007-018-0325-4
    Background: Small GTPases act as molecular switches that regulate various plant responses such as disease resistance, pollen tube growth, root hair development, cell wall patterning and hormone responses. Thus, to monitor their activation status within plant cells is believed to be the key step in understanding their roles.

    Results: We have established a plant version of a Förster resonance energy transfer (FRET) probe called Ras and interacting protein chimeric unit (Raichu) that can successfully monitor activation of the rice small GTPase OsRac1 during various defence responses in cells. Here, we describe the protocol for visualizing spatiotemporal activity of plant Rac/ROP GTPase in living plant cells, transfection of rice protoplasts with Raichu-OsRac1 and acquisition of FRET images.

    Conclusions: Our protocol should be adaptable for monitoring activation for other plant small GTPases and protein-protein interactions for other FRET sensors in various plant cells.

  2. Abdullah-Zawawi MR, Govender N, Karim MB, Altaf-Ul-Amin M, Kanaya S, Mohamed-Hussein ZA
    Plant Methods, 2022 Nov 05;18(1):118.
    PMID: 36335358 DOI: 10.1186/s13007-022-00951-6
    BACKGROUND: Phytochemicals or secondary metabolites are low molecular weight organic compounds with little function in plant growth and development. Nevertheless, the metabolite diversity govern not only the phenetics of an organism but may also inform the evolutionary pattern and adaptation of green plants to the changing environment. Plant chemoinformatics analyzes the chemical system of natural products using computational tools and robust mathematical algorithms. It has been a powerful approach for species-level differentiation and is widely employed for species classifications and reinforcement of previous classifications.

    RESULTS: This study attempts to classify Angiosperms using plant sulfur-containing compound (SCC) or sulphated compound information. The SCC dataset of 692 plant species were collected from the comprehensive species-metabolite relationship family (KNApSAck) database. The structural similarity score of metabolite pairs under all possible combinations (plant species-metabolite) were determined and metabolite pairs with a Tanimoto coefficient value > 0.85 were selected for clustering using machine learning algorithm. Metabolite clustering showed association between the similar structural metabolite clusters and metabolite content among the plant species. Phylogenetic tree construction of Angiosperms displayed three major clades, of which, clade 1 and clade 2 represented the eudicots only, and clade 3, a mixture of both eudicots and monocots. The SCC-based construction of Angiosperm phylogeny is a subset of the existing monocot-dicot classification. The majority of eudicots present in clade 1 and 2 were represented by glucosinolate compounds. These clades with SCC may have been a mixture of ancestral species whilst the combinatorial presence of monocot-dicot in clade 3 suggests sulphated-chemical structure diversification in the event of adaptation during evolutionary change.

    CONCLUSIONS: Sulphated chemoinformatics informs classification of Angiosperms via machine learning technique.

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