A rapid and robust colorimetric method for measuring relative abundance of auxins in plant tissues.

IF 3 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS Phytochemical Analysis Pub Date : 2024-07-01 Epub Date: 2024-02-28 DOI:10.1002/pca.3340
Mrinalini Manna, Balakrishnan Rengasamy, Alok Krishna Sinha
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Abstract

Introduction: Auxin estimation in plant tissues is a crucial component of auxin signaling studies. Despite the availability of various high-throughput auxin quantification methods like LC-MS, GC-MS, HPLC, biosensors, and DR5-gus/gfp-based assays, auxin quantification remains troublesome because these techniques are very expensive and technology intensive and they mostly involve elaborate sample preparation or require the development of transgenic plants.

Objectives: To find a solution to these problems, we made use of an old auxin detection system to quantify microbe derived auxins and modified it to effectively measure auxin levels in rice plants.

Materials and methods: Auxins from different tissues of rice plants, including root samples of seedlings exposed to IAA/TIBA or subjected to different abiotic stresses, were extracted in ethanol. The total auxin level was measured by the presently described colorimetric assay and counterchecked by other auxin estimation methods like LC-MS or gus staining of DR5-gus overexpressing lines.

Results: The presented colorimetric method could measure (1) the auxin levels in different tissues of rice plants, thus identifying the regions of higher auxin abundance, (2) the differential accumulation of auxins in rice roots when auxin or its transport inhibitor was supplied exogenously, and (3) the levels of auxin in roots of rice seedlings subjected to various abiotic stresses. The thus obtained auxin levels correlated well with the auxin levels determined by other methods like LC-MS or gus staining and the expression pattern of auxin biosynthesis pathway genes.

Conclusions: The auxin estimation method described here is simple, rapid, cost-effective, and sensitive and allows for the efficient detection of relative auxin abundances in plant tissues.

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一种快速、稳健的比色法,用于测量植物组织中辅助素的相对丰度。
简介植物组织中的叶黄素含量是叶黄素信号转导研究的重要组成部分。尽管目前已有多种高通量的辅助素定量方法,如 LC-MS、GC-MS、HPLC、生物传感器和基于 DR5-gus/gfp 的检测方法,但辅助素的定量仍然很麻烦,因为这些技术都非常昂贵,属于技术密集型技术,而且大多涉及精细的样品制备或需要开发转基因植物:为了找到解决这些问题的方法,我们使用了一种旧的辅助素检测系统来定量检测微生物衍生的辅助素,并对其进行了改进,以有效测量水稻植株中的辅助素水平:用乙醇提取水稻植株不同组织中的辅酶,包括暴露于 IAA/TIBA 或受到不同非生物胁迫的幼苗的根部样本。采用目前所描述的比色法测量总的辅助素水平,并通过其他辅助素估算方法(如 LC-MS 或对 DR5-gus 过表达品系进行 gus 染色)进行反检验:本发明的比色法可测定:(1) 水稻植株不同组织中的辅素水平,从而确定辅素丰度较高的区域;(2) 外源提供辅素或其转运抑制剂时,水稻根系中辅素的不同积累情况;(3) 受到各种非生物胁迫的水稻幼苗根系中的辅素水平。由此得出的植物生长素水平与其他方法(如液相色谱-质谱法或古斯染色法)测定的植物生长素水平以及植物生长素生物合成途径基因的表达模式有很好的相关性:本文所述的辅助素估算方法简单、快速、经济、灵敏,可有效检测植物组织中的相对辅助素丰度。
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来源期刊
Phytochemical Analysis
Phytochemical Analysis 生物-分析化学
CiteScore
6.00
自引率
6.10%
发文量
88
审稿时长
1.7 months
期刊介绍: Phytochemical Analysis is devoted to the publication of original articles concerning the development, improvement, validation and/or extension of application of analytical methodology in the plant sciences. The spectrum of coverage is broad, encompassing methods and techniques relevant to the detection (including bio-screening), extraction, separation, purification, identification and quantification of compounds in plant biochemistry, plant cellular and molecular biology, plant biotechnology, the food sciences, agriculture and horticulture. The Journal publishes papers describing significant novelty in the analysis of whole plants (including algae), plant cells, tissues and organs, plant-derived extracts and plant products (including those which have been partially or completely refined for use in the food, agrochemical, pharmaceutical and related industries). All forms of physical, chemical, biochemical, spectroscopic, radiometric, electrometric, chromatographic, metabolomic and chemometric investigations of plant products (monomeric species as well as polymeric molecules such as nucleic acids, proteins, lipids and carbohydrates) are included within the remit of the Journal. Papers dealing with novel methods relating to areas such as data handling/ data mining in plant sciences will also be welcomed.
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