水稻產(chǎn)量、抗逆性狀代謝組研究

羅杰 漆小泉

摘 要：水稻是世界超過半數(shù)人口的主要糧食作物，占世界人口60%的亞洲，其水稻種植面積超過世界的90%。性狀受到眾多因素的控制，通過對單個基因或單個合成/信號轉(zhuǎn)導(dǎo)途徑的研究往往無法闡明控制這些性狀的生理學(xué)/生物化學(xué)機理，因此，以系統(tǒng)生物學(xué)手段從整體上對上述性狀的形成及其調(diào)控進行研究就顯得尤為必要。代謝組學(xué)利用高通量、高靈敏度與高精確度的現(xiàn)代分析手段，對細胞及組織器官的代謝物的整體組成成分進行動態(tài)分析，在此基礎(chǔ)上借助多變量統(tǒng)計分析方法，解析生物體在各種環(huán)境因子的作用下產(chǎn)生的生理、病理及發(fā)育變化的物質(zhì)基礎(chǔ)。該研究利用代謝組學(xué)手段全面、系統(tǒng)研究正常和逆境條件下水稻產(chǎn)量及抗性形成過程各關(guān)鍵階段（如水稻種子萌發(fā)、劍葉形成、灌漿過程等）代謝產(chǎn)物及代謝途徑的動態(tài)變化，將能揭示水稻產(chǎn)量和抗逆的代謝生物學(xué)基礎(chǔ)，為作物遺傳改良提供新思路。通過研究，建立了在單次分析中定量水稻中>600種（包括類黃酮、生物堿、萜類等）代謝物的高通量代謝組學(xué)分析方法；建立>1000種代謝物的水稻代謝組學(xué)數(shù)據(jù)庫；定位超過2000個水稻代謝QTL（mQTL），鑒定其中部分mQTL基因；構(gòu)建了水稻發(fā)芽種子和劍葉的代謝調(diào)控網(wǎng)絡(luò)。

關(guān)鍵詞：水稻 代謝組 QTL

Abstract：Rice （Oryza sativa） is one of the most important crops that feed more than half of the worlds population. Asia that has 60% of the people in the world has 90% of the land of cultivate rice across the world. Agro-traits are controlled by multiple factors and these factors are difficult to identified by examining single gene or pathway. It is therefore crucial to investigate them by a systematic approach. Metabolomics is a modern omics technology of high-throughput and resolution that measure the dynamic levels of small molecular in cells and tissues of an organism. It can be applied to the investigation of metabolic mechanism of physiological and developmental processes. In this study， metabolic profiling of tissues （germinating seedling， flag leaf， and spikelet） in normal and stressed conditions was used in combination with genomic strategy to elucidate the mechanism of yield and stress resistance， which would benefit the genetic improvement of crops such as rice. A novel LC-MS based metabolomics strategy was established to measure the levels of over 600 metabolites including flavonoids， alkaloids， terpenoids etc. A metabolic database was built containing more than 1000 different metabolites in rice. Over 2000 metabolic QTL （mQTL） was mapped using a recombinant inbred line population. The candidate genes underlying some of the mQTL were identified and the corresponding metabolic pathways were reconstructed in rice seedling and flag leaf.

Key Words：Oryza Sativa；Metabolomics；QTL

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