Microbial Ecology

, Volume 68, Issue 2, pp 416–425 | Cite as

Phylogenetic and Functional Analysis of Gut Microbiota of a Fungus-Growing Higher Termite: Bacteroidetes from Higher Termites Are a Rich Source of β-Glucosidase Genes

  • Meiling Zhang
  • Ning Liu
  • Changli Qian
  • Qianfu Wang
  • Qian Wang
  • Yanhua Long
  • Yongping Huang
  • Zhihua Zhou
  • Xing Yan
Host Microbe Interactions


Fungus-growing termites, their symbiotic fungi, and microbiota inhibiting their intestinal tract comprise a highly efficient cellulose-hydrolyzing system; however, little is known about the role of gut microbiota in this system. Twelve fosmid clones with β-glucosidase activity were previously obtained by functionally screening a metagenomic library of a fungus-growing termite, Macrotermes annandalei. Ten contigs containing putative β-glucosidase genes (bgl110) were assembled by sequencing data of these fosmid clones. All these contigs were binned to Bacteroidetes, and all these β-glucosidase genes were phylogenetically closed to those from Bacteroides or Dysgonomonas. Six out of 10 β-glucosidase genes had predicted signal peptides, indicating a transmembrane capability of these enzymes to mediate cellulose hydrolysis within the gut of the termites. To confirm the activities of these β-glucosidase genes, three genes (bgl5, bgl7, and bgl9) were successfully expressed and purified. The optimal temperature and pH of these enzymes largely resembled the environment of the host’s gut. The gut microbiota composition of the fungus-growing termite was also determined by 454 pyrosequencing, showing that Bacteroidetes was the most dominant phylum. The diversity and the enzyme properties of β-glucosidases revealed in this study suggested that Bacteroidetes as the major member in fungus-growing termites contributed to cello-oligomer degradation in cellulose-hydrolyzing process and represented a rich source for β-glucosidase genes.


Cellulase Cellobiose Bacteroidetes Cellulose Hydrolysis Symbiotic Fungus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by grants of the National Natural Science Foundation of China (31070098). We appreciate Professor Shubiao Wu from the University of New England for his critical reading and kind suggestions. We also give our thanks to Prashanth Singanallur and Lauren Christine Radlinski from the University of Illinois at Urbana-Champaign for proofreading. We appreciated Dr. Haokui Zhou from Department of Microbiology, The Chinese University of Hongkong and Lei zhang from Logic Informatics Co.,Ltd., for their kind help in data analysis.

Conflict of Interest

The authors have no conflict of interest to declare.

Supplementary material

248_2014_388_MOESM1_ESM.pptx (47.3 mb)
ESM 1 (PPTX 48413 kb)


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Meiling Zhang
    • 1
  • Ning Liu
    • 3
    • 4
  • Changli Qian
    • 2
  • Qianfu Wang
    • 2
  • Qian Wang
    • 3
  • Yanhua Long
    • 5
  • Yongping Huang
    • 3
  • Zhihua Zhou
    • 2
  • Xing Yan
    • 2
    • 6
  1. 1.School of Life SciencesEast China Normal UniversityShanghaiChina
  2. 2.Key Laboratory of Synthetic Biology, Institute of Plant Physiology and EcologyShanghai Institutes for Biological Sciences, Chinese Academy of SciencesShanghaiChina
  3. 3.Key Laboratory of Insect Development and Evolutionary Biology, Institute of Plant Physiology and EcologyShanghai Institutes for Biological Sciences, Chinese Academy of SciencesShanghaiChina
  4. 4.Zhejiang Academy of Agricultural SciencesHangzhouChina
  5. 5.School of Life ScienceAnhui Agricultural UniversityHefeiChina
  6. 6.Key Laboratory of Synthetic Biology, Shanghai Institute of Plant Physiology and EcologyChinese Academy of SciencesShanghaiChina

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