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Microbiome Diversity in Cotton Rhizosphere Under Normal and Drought Conditions

  • Abid Ullah
  • Adnan Akbar
  • Qingqing Luo
  • Aamir Hamid Khan
  • Hakim Manghwar
  • Muhammad Shaban
  • Xiyan Yang
Plant Microbe Interactions

Abstract

Climate change contributes to drought stress and subsequently affects crop growth, development, and yield. The microbial community, such as fungi and bacteria in the rhizosphere, is of special importance to plant productivity. In this study, soil collected from a cotton research field was used to grow cotton plants (Gossypium hirsutum cv. Jin668) under controlled environment conditions. Drought stress was applied at flowering stage, while control plants were regularly watered. At the same time, the soil without plants was also subjected to drought, while control pots were regularly watered. The soil was collected in sterilized tubes and microbial DNA was isolated and high-throughput sequencing of 16S rRNA genes was carried out. The alpha diversity of bacteria community significantly increased in the soil with cotton plants compared to the soil without cotton plants. Taxonomic analysis revealed that the bacterial community structure of the cotton rhizosphere predominantly consisted of the phyla Proteobacteria (31.7%), Actinobacteria (29.6%), Gemmatimonadetes (9.8%), Chloroflexi (9%), Cyanobacteria (5.6%), and Acidobacteria. In the drought-treated rhizosphere, Chloroflexi and Gemmatimonadetes were the dominant phyla. This study reveals that the cotton rhizosphere has a rich pool of bacterial communities even under drought stress, and which may improve drought tolerance in plants. These data will underpin future improvement of drought tolerance of cotton via the soil microbial community.

Keywords

Cotton Drought Microbial diversity Plant-microbe interactions Rhizosphere 

Notes

Author Contributions

Xiyan Yang designed the project; Abid Ullah and Adnan Akbar conducted the experiments and wrote the paper; Qingqing Luo, Hakim, and Muhammad Shaban helped Abid Ullah in the experiments; Aamir Hamid Khan helped Abid Ullah in writing the paper; and Xiyan Yang critically reviewed the paper.

Funding Information

Funding was provided by the National Key Project of Research and Development Plan (2016YFD0101006) and National R&D Project of Transgenic Crops of Ministry of Science and Technology of China (2016ZX08005-004-002). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Compliance with Ethical Standards

Competing Interests

The authors declare that they have no competing interests.

Supplementary material

248_2018_1260_MOESM1_ESM.pdf (66 kb)
ESM 1 (PDF 66 kb)
248_2018_1260_MOESM2_ESM.pdf (590 kb)
Fig. S1 The abundance distribution of the top 20 taxa with the most significant differences among samples, Phylum (A) and Genus (B). The abscissa is the most significant first 20 taxa, and the ordinate is the sequence amount of each taxon in each sample (group). In the case of no grouping of samples, they are displayed in the form of scatter plots; in the case of grouping of samples, they are displayed in the form of a violin chart combined with a box plot: Among them, the violin plot visually display the distribution characteristics of the data, “violin”. The “skinny” reflects the density of the sample data distribution (the greater the width, the more samples corresponding to the sequence); the box plot borders represent the Interquartile range (IQR), horizontal lines Representing the median value, the upper and lower whiskers represent the 1.5-fold IQR range outside the upper and lower quartiles respectively, and the symbol “•” indicates the extreme value that exceeds the range. (PDF 590 kb)
248_2018_1260_MOESM3_ESM.pdf (22 kb)
Fig. S2 Affiliation network diagram. The nodes represent the dominant genus and are identified by different colors. The connection between the nodes indicates that there is a correlation between the two genera, the red line indicates a positive correlation, and the green line indicates a negative correlation. The more connections that pass through a node, the more associations the genus has with other members of the flora. (PDF 22 kb)
248_2018_1260_MOESM4_ESM.pdf (99 kb)
Fig. S3 PICRUSt predicted KEGG second-level distribution map. (A-D) Metabolic pathways divided into five categories, including metabolism, genetic information processing, environmental information processing, cellular processes, and organism systems. In the figure, the abscissa is the KEGG second-class functional group, and the ordinate is the relative abundance of each functional group within each sample (group). In the case of no grouping of samples, they are displayed in the form of a bar graph; in the case of grouping of samples, they are displayed in the form of violin plots combined with box plots: among them, violin plots can visually display the distribution characteristics of data, “violin”. The “skinny” reflects the density of the distribution of the sample data (the greater the width, the more the corresponding sample under the abundance); the box plot border represents the Interquartile range (IQR), horizontal line representing the median value, the upper and lower whiskers represent the 1.5-fold IQR range outside the upper and lower quartiles respectively, and the symbol “•” indicates the extreme value that exceeds the range. (PDF 98 kb)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Abid Ullah
    • 1
  • Adnan Akbar
    • 1
  • Qingqing Luo
    • 1
  • Aamir Hamid Khan
    • 1
  • Hakim Manghwar
    • 1
  • Muhammad Shaban
    • 1
  • Xiyan Yang
    • 1
  1. 1.National Key Laboratory of Crop Genetic ImprovementHuazhong Agricultural UniversityWuhanPeople’s Republic of China

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