Moss habitats distinctly affect their associated bacterial community structures as revealed by the high-throughput sequencing method
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To better understand the factors that influence the distribution of bacteria associated with mosses, the communities inhabiting in five moss species from two different habitats in Beijing Songshan National Nature Reserve were investigated using the high-throughput sequencing method. The sequencing was performed based on the bacterial 16S rRNA and 16S rDNA libraries. Results showed that there are abundant bacteria inhabiting in all the mosses sampled. The taxonomic analysis of these bacteria showed that they mainly consisted of those in the phyla Proteobacteria and Actinobacteria, and seldom were from phylum Armatimonadetes, Bacteroidetes and Firmicutes. The hierarchical cluster tree, based on the OTU level, divided the bacteria associated with all samples into two branches according to the habitat types of the host (terrestrial and aquatic). The PCoA diagram further divided the bacterial compositions into four groups according to both types of habitats and the data sources (DNA and RNA). There were larger differences in the bacterial community composition in the mosses collected from aquatic habitat than those of terrestrial one, whether at the DNA or RNA level. Thus, this survey supposed that the habitat where the host was growing was a relevant factor influencing bacterial community composition. In addition, the bacterial community detected at the RNA level was more sensitive to the habitat of the growing host, which could also be proved by the significantly differences in the predicted function by PICRUSt and the metabolically active dominant genera between different groups. This study expands the knowledge about the interactions between mosses and microbes.
KeywordsBacterial composition Aquatic and land mosses High-throughput DNA sequencing
We would like to thank Emily Drummond at the University of British Columbia for her assistance with English language and grammatical editing of the manuscript. We also thank Majorbio Co. for providing sequencing data and its I-Sanger platform for analyzing the sequences. This study was funded by the scientific research program of National Natural Science Foundation of China (No. 31470136).
- Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Tumbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7(5):335–336CrossRefPubMedPubMedCentralGoogle Scholar
- Chao A (1984) Nonparametric estimation of the number of classes in a population. Scand J Stat 11(4):265–270Google Scholar
- Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Thurber RLV, Knight R, Beiko RG, Huttenhower C (2013) Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 31(9):814–821CrossRefPubMedPubMedCentralGoogle Scholar
- Li XR, Zhang JG, Wang XP, Liu LC, Xiao HL (2000) Study on soil microbiotic crust and its influences on sand-fixing vegetation in arid desert region. Acta Botanica Sinica 42(9):965–970Google Scholar
- Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75(23):7537–7541CrossRefPubMedPubMedCentralGoogle Scholar