The Impact of the Diurnal Cycle on the Microbial Transcriptome in the Rhizosphere of Barley

  • Divyashri Baraniya
  • Paolo Nannipieri
  • Susanne Kublik
  • Gisle Vestergaard
  • Michael Schloter
  • Anne Schöler
Note

Abstract

While root exudation follows diurnal rhythms, little is known about the consequences for the microbiome of the rhizosphere. In this study, we used a metatranscriptomic approach to analyze the active microbial communities, before and after sunrise, in the rhizosphere of barley. We detected increased activities of many prokaryotic microbial taxa and functions at the pre-dawn stage, compared to post-dawn. Actinomycetales, Planctomycetales, Rhizobiales, and Burkholderiales were the most abundant and therefore the most active orders in the barley rhizosphere. The latter two, as well as Xanthomonadales, Sphingomonadales, and Caulobacterales showed a significantly higher abundance in pre-dawn samples compared to post-dawn samples. These changes in taxonomy coincide with functional changes as genes involved in both carbohydrate and amino acid metabolism were more abundant in pre-dawn samples compared to post-dawn samples. This study significantly enhances our present knowledge on how rhizospheric microbiota perceives and responds to changes in the soil during dark and light periods.

Keywords

Rhizosphere Metatranscriptome Barley Diurnal cycle Microbial dynamics 

Supplementary material

248_2017_1101_MOESM1_ESM.pdf (362 kb)
ESM 1Supplementary Fig. 1 (S1): Coverage estimation. The achieved coverage of metatranscriptomic samples was analyzed using Nonpareil and shows similar coverage for all six samples. (PDF 361 kb)
248_2017_1101_MOESM2_ESM.pdf (362 kb)
ESM 2Supplementary Fig. 2 (S2): The most abundant bacterial orders of significantly different KEGG pathways. Depicted are the ten most abundant bacterial orders in percentages of reads for the pathways, which were more active at pre-dawn (carbohydrate metabolism, nucleotide metabolism, amino acid metabolism and metabolism of cofactors and vitamins). Significant differences between pre-dawn and post-dawn time points were determined by unpaired t-test statistics (∗P < 0.05, n = 3). (PDF 362 kb)
248_2017_1101_MOESM3_ESM.docx (24 kb)
ESM 3Supplementary Table 1 Pre-processing of Illumina mRNA reads. Supplementary Table 2 Relative abundance of reads belonging to taxonomic classes which were significantly different between day and night. Supplementary Table 3 Relative abundance of reads belonging to KEGG pathways which were significantly different between day and night (DOCX 24 kb)
248_2017_1101_MOESM4_ESM.docx (30 kb)
ESM 4Supplementary Methods (DOCX 30 kb)

References

  1. 1.
    Berg G, Grube M, Schloter M, Smalla K (2014) Unraveling the plant microbiome: looking back and future perspectives. Front Microbiol 5:148PubMedPubMedCentralGoogle Scholar
  2. 2.
    Bais HP, Weir TL, Perry LG, Gilroy S, Vivanco JM (2006) The role of root exudates in rhizosphere interactions with plants and other organisms. Annu Rev Plant Biol 57:233–266CrossRefPubMedGoogle Scholar
  3. 3.
    Brzostek ER, Greco A, Drake JE, Finzi AC (2013) Root carbon inputs to the rhizosphere stimulate extracellular enzyme activity and increase nitrogen availability in temperate forest soils. Biogeochemistry 115:65–76CrossRefGoogle Scholar
  4. 4.
    Badri DV, Vivanco JM (2009) Regulation and function of root exudates. Plant Cell Environ 32:666–681CrossRefPubMedGoogle Scholar
  5. 5.
    Kuzyakov Y, Cheng W (2004) Photosynthesis controls of CO2 efflux from maize rhizosphere. Plant Soil 263:85–99CrossRefGoogle Scholar
  6. 6.
    Töwe S, Wallisch S, Bannert A, Fischer D, Hai B, Haesler F, et al. (2011) Improved protocol for the simultaneous extraction and column-based separation of DNA and RNA from different soils. J Microbiol Methods 84:406–412CrossRefPubMedGoogle Scholar
  7. 7.
    Schubert M, Lindgreen S, Orlando L (2016) AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Res Notes 9:88CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Schmieder R, Edwards R (2011) Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS One 6:e17288CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Kopylova E, Noé L, Touzet H (2012) SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28:3211–3217CrossRefPubMedGoogle Scholar
  10. 10.
    Huson DH, Auch AF, Qi J, Schuster SC (2007) MEGAN analysis of metagenomic data. Genome Res. 17:377–386CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Rodriguez-R LM, Konstantinidis KT (2013) Nonpareil: a redundancy-based approach to assess the level of coverage in metagenomic datasets. Bioinformatics 30:629–635CrossRefPubMedGoogle Scholar
  12. 12.
    R Core Team (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, URL http://www.R-project.org/ Google Scholar
  13. 13.
    Bulgarelli D, Garrido-Oter R, Münch PC, Weiman A, Dröge J, Pan Y, et al. (2015) Structure and function of the bacterial root microbiota in wild and domesticated barley. Cell Host Microbe 17:392–403CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Poretsky RS, Hewson I, Sun S, Allen AE, Zehr JP, Moran MA (2009) Comparative day/night metatranscriptomic analysis of microbial communities in the North Pacific subtropical gyre. Environ Microbiol 11:1358–1375CrossRefPubMedGoogle Scholar
  15. 15.
    Knee EM, Gong FC, Gao M, Teplitski M, Jones AR, Foxworthy A, et al. (2001) Root mucilage from pea and its utilization by rhizosphere bacteria as a sole carbon source. Mol Plant-Microbe Interact 14:775–784CrossRefPubMedGoogle Scholar
  16. 16.
    Iijima M, Sako Y, Rao TP (2003) A new approach for the quantification of root-cap mucilage exudation in the soil. Plant Soil 225:399–407CrossRefGoogle Scholar
  17. 17.
    Lünsmann V, Kappelmeyer U, Taubert A, Nijenhuis I, Von Bergen M, Heipieper HJ, et al. (2016) Aerobic toluene degraders in the rhizosphere of a constructed wetland model show diurnal polyhydroxyalkanoate metabolism. Appl Environ Microbiol 82:4126–4132CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Ishikawa CM, Bledsoe CS (2000) Seasonal and diurnal patterns of soil water potential in the rhizosphere of blue oaks: evidence for hydraulic lift. Oecologia 125:459–465CrossRefPubMedGoogle Scholar
  19. 19.
    Matimati I, Anthony Verboom G, Cramer MD (2014) Do hydraulic redistribution and nocturnal transpiration facilitate nutrient acquisition in Aspalathus linearis? Oecologia 175:1129–1142CrossRefPubMedGoogle Scholar
  20. 20.
    Cardon ZG, Gage DJ (2006) Resource exchange in the rhizosphere: molecular tools and the microbial perspective. Annu Rev Ecol Evol Syst 37:459–488CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Divyashri Baraniya
    • 1
  • Paolo Nannipieri
    • 1
  • Susanne Kublik
    • 2
  • Gisle Vestergaard
    • 2
  • Michael Schloter
    • 2
  • Anne Schöler
    • 2
  1. 1.Department of Agrifood Production and Environmental SciencesUniversity of FlorenceFlorenceItaly
  2. 2.Research Unit for Comparative Microbiome AnalysisHelmholtz Zentrum MünchenNeuherbergGermany

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