Soil nutrition-dependent dynamics of the root-associated microbiome in paddy rice

Asahi Adachi, Yuniar Devi Utami, John Jewish Arellano Dominguez, Masako Fuji, Sumire Kirita, Shunsuke Imai, Takumi Murakami, Yuichi Hongoh, Rina Shinjo, Takehiro Kamiya, Toru Fujiwara, Kiwamu Minamisawa, Naoaki Ono, Shigehiko Kanaya, Yusuke Saijo
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Abstract

Plants accommodate diverse microbial communities (microbiomes), which can change dynamically during plant adaptation to varying environmental conditions. However, the direction of these changes and the underlying mechanisms driving them, particularly in crops adapting to the field conditions, remain poorly understood. We investigate the root-associated microbiome of rice (Oryza sativa L.) using 16S rRNA gene amplicon and metagenome sequencing, across four consecutive cultivation seasons in a high-yield, non-fertilized, and pesticide-free paddy field, compared to a neighboring fertilized and pesticide-treated field. Our findings reveal that root microbial community shifts and diverges based on soil fertilization status and plant developmental stages. Notably, nitrogen-fixing bacteria such as Telmatospirillum, Bradyrhizobium and Rhizomicrobium were over-represented in rice grown in the non-fertilized field, implying that the assembly of these microbes supports rice adaptation to nutrient-deficient environments. A machine learning model trained on the microbiome data successfully predicted soil fertilization status, highlighting the potential of root microbiome analysis in forecasting soil nutrition levels. Additionally, we observed significant changes in the root microbiome of ccamk mutants, which lack a master regulator of mycorrhizal symbiosis, under laboratory conditions but not in the field, suggesting a condition-dependent role for CCaMK in establishing microbiomes in paddy rice.
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水稻根相关微生物群的动态变化与土壤营养有关
植物可容纳多种微生物群落(微生物组),这些微生物组在植物适应不同环境条件的过程中会发生动态变化。我们利用 16S rRNA 基因扩增片段和元基因组测序技术,对水稻(Oryza sativa L.)根部相关微生物组进行了研究。我们利用 16S rRNA 基因扩增片段和元基因组测序技术研究了水稻(Oryza sativa L.)根系相关微生物群落。值得注意的是,固氮菌(如 Telmatospirillum、Bradyrhizobium 和 Rhizomicrobium)在非施肥田中生长的水稻中比例过高,这意味着这些微生物的聚集有助于水稻适应养分缺乏的环境。根据微生物组数据训练的机器学习模型成功地预测了土壤施肥状况,凸显了根微生物组分析在预测土壤营养水平方面的潜力。此外,我们观察到缺乏菌根共生主调节因子的ccamk突变体的根微生物组在实验室条件下发生了显著变化,但在田间却没有,这表明CCaMK在水稻微生物组的建立过程中起着依赖条件的作用。
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