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Biology and Fertility of Soils

, Volume 55, Issue 1, pp 79–88 | Cite as

Effect of LSU and ITS genetic markers and reference databases on analyses of fungal communities

  • Chao Xue
  • Yuewen Hao
  • Xiaowei Pu
  • C. Ryan Penton
  • Qiong Wang
  • Mengxin Zhao
  • Bangzhou Zhang
  • Wei Ran
  • Qiwei Huang
  • Qirong ShenEmail author
  • James M. TiedjeEmail author
Original Paper

Abstract

The effect of genetic markers and reference databases on analyses of fungal communities were estimated using fungal large subunit (LSU) and internal transcribed spacer (ITS) amplicon datasets in consecutive years of rhizosphere samples from three candidate biofuel crops, corn (Zea mays), switchgrass (Panicum virgatum), and miscanthus (Miscanthus × giganteus). These two marker genes were selected to contrast possible differences in biological conclusions. In addition, two ITS schemes based on two ITS reference databases were used to assess differences due to reference database composition. A taxonomy-supervised method was invoked using the Ribosomal Database Project naïve Bayesian classifier that accesses all three databases. The UNITE classification scheme had the highest number of classified taxa in the raw classification result; however, it also had the highest proportion of unknown taxa (sequences that were classified to “unclassified,” “unidentified,” incertae sedis or, in the case of Warcup, to matches containing two unique names). After removal of these unknown taxa, LSU had the highest classification rate followed by Warcup and UNITE. As expected, the communities resolved using the two ITS databases, based on the same sequences, were relatively more similar than those from the lower-coverage LSU classification scheme. The choice of marker gene or even the same reads with different classification databases revealed different community patterns due to database coverage, e.g., the relative abundance of the most abundant groups changed or were only detected in one or two of the classification schemes, such as for Mortierella, Fusarium, and Phoma. No marked difference in fungal beta-diversity was identified among the three methods. Differentiation between the three biofuel crops and between the drought and normal rainfall years was apparent, regardless of method. Though classification rates, taxonomic conflicts, and coverage differences within the high-abundance fungal groups varied according to classification scheme, there was no overall impact on beta diversity among the three methods.

Keywords

Fungal database LSU ITS Warcup UNITE 

Notes

Funding

This work was funded by the DOE Great Lakes Bioenergy Research Center, DOE BER Office of Science (DE-FC02-07ER64494 and DE-FG02-99ER62848), the Jiangsu Science and Technology Department (BK20160730), the China Postdoctoral Science Foundation (2017M621761 and 2018T110510), and the Fundamental Research Funds for the Central Universities (KYZ201720 and KYZ201877). We also acknowledge the support of the GLBRC field staff who maintains the field experiment, and for the funding that supports that staff and field operations by the National Science Foundation Long-term Ecological Research Program (DEB 1637653), and by Michigan State University AgBioResearch.

Supplementary material

374_2018_1331_MOESM1_ESM.docx (966 kb)
ESM 1 (DOCX 965 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Chao Xue
    • 1
    • 2
  • Yuewen Hao
    • 1
  • Xiaowei Pu
    • 1
  • C. Ryan Penton
    • 3
    • 4
  • Qiong Wang
    • 2
  • Mengxin Zhao
    • 2
    • 5
  • Bangzhou Zhang
    • 2
    • 6
  • Wei Ran
    • 1
  • Qiwei Huang
    • 1
  • Qirong Shen
    • 1
    Email author
  • James M. Tiedje
    • 2
    Email author
  1. 1.Jiangsu Provincial Key Lab for Solid Organic Waste Utilization, National Engineering Research Center for Organic-based Fertilizers, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource UtilizationNanjing Agricultural UniversityNanjingChina
  2. 2.Center of Microbial EcologyMichigan State UniversityEast LansingUSA
  3. 3.College of Integrative Sciences and Arts, Faculty of Science and MathematicsArizona State UniversityMesaUSA
  4. 4.Center for Fundamental and Applied Microbiomics, Biodesign InstituteArizona State UniversityTempeUSA
  5. 5.State Key Joint Laboratory of Environment Simulation and Pollution Control, School of EnvironmentTsinghua UniversityBeijingChina
  6. 6.State Key Laboratory of Marine Environmental Science and Key Laboratory of the Ministry of Education for Coast and Wetland Ecosystems, School of Life SciencesXiamen UniversityXiamenChina

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