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用于理解水稻盐响应机制的综合组学方法综述

A Review of Integrative Omic Approaches for Understanding Rice Salt Response Mechanisms.

作者信息

Ullah Mohammad Asad, Abdullah-Zawawi Muhammad-Redha, Zainal-Abidin Rabiatul-Adawiah, Sukiran Noor Liyana, Uddin Md Imtiaz, Zainal Zamri

机构信息

Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia.

Bangladesh Institute of Nuclear Agriculture (BINA), BAU Campus, Mymensingh 2202, Bangladesh.

出版信息

Plants (Basel). 2022 May 27;11(11):1430. doi: 10.3390/plants11111430.

Abstract

Soil salinity is one of the most serious environmental challenges, posing a growing threat to agriculture across the world. Soil salinity has a significant impact on rice growth, development, and production. Hence, improving rice varieties' resistance to salt stress is a viable solution for meeting global food demand. Adaptation to salt stress is a multifaceted process that involves interacting physiological traits, biochemical or metabolic pathways, and molecular mechanisms. The integration of multi-omics approaches contributes to a better understanding of molecular mechanisms as well as the improvement of salt-resistant and tolerant rice varieties. Firstly, we present a thorough review of current knowledge about salt stress effects on rice and mechanisms behind rice salt tolerance and salt stress signalling. This review focuses on the use of multi-omics approaches to improve next-generation rice breeding for salinity resistance and tolerance, including genomics, transcriptomics, proteomics, metabolomics and phenomics. Integrating multi-omics data effectively is critical to gaining a more comprehensive and in-depth understanding of the molecular pathways, enzyme activity and interacting networks of genes controlling salinity tolerance in rice. The key data mining strategies within the artificial intelligence to analyse big and complex data sets that will allow more accurate prediction of outcomes and modernise traditional breeding programmes and also expedite precision rice breeding such as genetic engineering and genome editing.

摘要

土壤盐渍化是最严峻的环境挑战之一,对全球农业构成日益严重的威胁。土壤盐渍化对水稻的生长、发育和产量有重大影响。因此,提高水稻品种对盐胁迫的抗性是满足全球粮食需求的可行解决方案。适应盐胁迫是一个多方面的过程,涉及相互作用的生理特性、生化或代谢途径以及分子机制。多组学方法的整合有助于更好地理解分子机制,以及改良耐盐和抗盐水稻品种。首先,我们全面综述了关于盐胁迫对水稻的影响以及水稻耐盐性和盐胁迫信号传导背后机制的现有知识。本综述重点关注利用多组学方法改进下一代水稻耐盐性育种,包括基因组学、转录组学、蛋白质组学、代谢组学和表型组学。有效整合多组学数据对于更全面、深入地理解控制水稻耐盐性的分子途径、酶活性和基因相互作用网络至关重要。人工智能中的关键数据挖掘策略可分析大型复杂数据集,从而更准确地预测结果,使传统育种计划现代化,并加快精准水稻育种,如基因工程和基因组编辑。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a36c/9182744/f790e0b59d1f/plants-11-01430-g001.jpg

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