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Science China Life Sciences

, Volume 61, Issue 6, pp 613–621 | Cite as

Root microbiota shift in rice correlates with resident time in the field and developmental stage

  • Jingying Zhang
  • Na Zhang
  • Yong-Xin Liu
  • Xiaoning Zhang
  • Bin Hu
  • Yuan Qin
  • Haoran Xu
  • Hui Wang
  • Xiaoxuan Guo
  • Jingmei Qian
  • Wei Wang
  • Pengfan Zhang
  • Tao Jin
  • Chengcai Chu
  • Yang Bai
Cover Article

Abstract

Land plants in natural soil form intimate relationships with the diverse root bacterial microbiota. A growing body of evidence shows that these microbes are important for plant growth and health. Root microbiota composition has been widely studied in several model plants and crops; however, little is known about how root microbiota vary throughout the plant’s life cycle under field conditions. We performed longitudinal dense sampling in field trials to track the time-series shift of the root microbiota from two representative rice cultivars in two separate locations in China. We found that the rice root microbiota varied dramatically during the vegetative stages and stabilized from the beginning of the reproductive stage, after which the root microbiota underwent relatively minor changes until rice ripening. Notably, both rice genotype and geographical location influenced the patterns of root microbiota shift that occurred during plant growth. The relative abundance of Deltaproteobacteria in roots significantly increased overtime throughout the entire life cycle of rice, while that of Betaproteobacteria, Firmicutes, and Gammaproteobacteria decreased. By a machine learning approach, we identified biomarker taxa and established a model to correlate root microbiota with rice resident time in the field (e.g., Nitrospira accumulated from 5 weeks/tillering in field-grown rice). Our work provides insights into the process of rice root microbiota establishment.

Keywords

rice root microbiota time-series shift biomarker taxa residence time in the field developmental stages modeling machine learning 

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Notes

Acknowledgements

This work was supported by the “Strategic Priority Research Program” of the Chinese Academy of Sciences (XDB11020700), CPSF-CAS Joint Foundation for Excellent Postdoctoral Fellows (2016LH00012), Strategic Priority Research Program of the Chinese Academy of Sciences (QYZDB-SSW-SMC021) and the National Natural Science Foundation of China (31772400).

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Jingying Zhang
    • 1
    • 2
  • Na Zhang
    • 1
    • 2
    • 3
  • Yong-Xin Liu
    • 1
    • 2
  • Xiaoning Zhang
    • 1
    • 2
    • 3
  • Bin Hu
    • 1
  • Yuan Qin
    • 1
    • 2
    • 3
  • Haoran Xu
    • 1
    • 2
    • 3
  • Hui Wang
    • 1
    • 2
    • 3
  • Xiaoxuan Guo
    • 1
    • 2
  • Jingmei Qian
    • 1
    • 2
    • 3
  • Wei Wang
    • 1
  • Pengfan Zhang
    • 4
    • 5
  • Tao Jin
    • 4
    • 5
  • Chengcai Chu
    • 1
  • Yang Bai
    • 1
    • 2
  1. 1.State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijingChina
  2. 2.CAS-JIC Centre of Excellence for Plant and Microbial Science (CEPAMS), Institute of Genetics and Developmental BiologyChinese Academy of Sciences (CAS)BeijingChina
  3. 3.College of Life SciencesUniversity of Chinese Academy of SciencesBeijingChina
  4. 4.China National Genebank-ShenzhenBGI-ShenzhenShenzhenChina
  5. 5.BGI-QingdaoQingdaoChina

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