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

Overview of attention for article published in Science China Life Sciences, March 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

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171 Mendeley
Title
Root microbiota shift in rice correlates with resident time in the field and developmental stage
Published in
Science China Life Sciences, March 2018
DOI 10.1007/s11427-018-9284-4
Pubmed ID
Authors

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

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.

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The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 171 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 44 26%
Researcher 13 8%
Student > Master 10 6%
Student > Doctoral Student 8 5%
Other 7 4%
Other 23 13%
Unknown 66 39%
Readers by discipline Count As %
Agricultural and Biological Sciences 55 32%
Biochemistry, Genetics and Molecular Biology 12 7%
Environmental Science 8 5%
Immunology and Microbiology 4 2%
Computer Science 3 2%
Other 13 8%
Unknown 76 44%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 August 2021.
All research outputs
#2,305,675
of 23,031,582 outputs
Outputs from Science China Life Sciences
#107
of 1,009 outputs
Outputs of similar age
#52,192
of 331,443 outputs
Outputs of similar age from Science China Life Sciences
#1
of 23 outputs
Altmetric has tracked 23,031,582 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,009 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.4. This one has done well, scoring higher than 89% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,443 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.