Mining environmental high‐throughput sequence data sets to identify divergent amplicon clusters for phylogenetic reconstruction and morphotype visualization

Environmental high‐throughput sequencing (envHTS) is a very powerful tool, which in protistan ecology is predominantly used for the exploration of diversity and its geographic and local patterns. We here used a pyrosequenced V4‐SSU rDNA data set from a solar saltern pond as test case to exploit such...

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Published in:Environmental microbiology reports Vol. 7; no. 4; pp. 679 - 686
Main Authors: Gimmler, Anna, Stoeck, Thorsten
Format: Journal Article
Language:English
Published: United States Society for Applied Microbiology and Blackwell Pub 01-08-2015
Blackwell Publishing Ltd
John Wiley & Sons, Inc
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Summary:Environmental high‐throughput sequencing (envHTS) is a very powerful tool, which in protistan ecology is predominantly used for the exploration of diversity and its geographic and local patterns. We here used a pyrosequenced V4‐SSU rDNA data set from a solar saltern pond as test case to exploit such massive protistan amplicon data sets beyond this descriptive purpose. Therefore, we combined a Swarm‐based blastn network including 11 579 ciliate V4 amplicons to identify divergent amplicon clusters with targeted polymerase chain reaction (PCR) primer design for full‐length small subunit of the ribosomal DNA retrieval and probe design for fluorescence in situ hybridization (FISH). This powerful strategy allows to benefit from envHTS data sets to (i) reveal the phylogenetic position of the taxon behind divergent amplicons; (ii) improve phylogenetic resolution and evolutionary history of specific taxon groups; (iii) solidly assess an amplicons (species') degree of similarity to its closest described relative; (iv) visualize the morphotype behind a divergent amplicons cluster; (v) rapidly FISH screen many environmental samples for geographic/habitat distribution and abundances of the respective organism and (vi) to monitor the success of enrichment strategies in live samples for cultivation and isolation of the respective organisms.
Bibliography:http://dx.doi.org/10.1111/1758-2229.12307
istex:7456352CC74C186E9B097CF267CE68425CF479DE
ArticleID:EMI412307
Fig. S1. SWARM 14 consists of 157 sequences in total which are represented by 24 unique amplicons (here displayed as nodes). Each edge represents a difference of one base pair. The seed sequence in the center of the swarm (here in red) comprises 129 sequences. The satellite sequences which are mostly singletons are one base pair different from the seed (with one exception which is two base pairs different). The mean sequence similarity of all sequences in the swarm is 99.62%. Fig. S2. Neighbour joining (NJ) evolutionary distance phylogenetic tree of the hypervariable V4 region of the SSU rDNA showing the phylgenetic placement of SWARM 14. NJ bootstrap values above 50 are given at the individual nodes (1000 replicates). Species names are followed by their GenBank accession numbers in the brackets. Fig. S3. Maximum likelihood (RAxML) phylogenetic tree of SSU rDNA sequences showing the phylogenetic placement of the full-length SSU rDNA obtained from SWARM 14. ML bootstrap values above 50 are given at the individual nodes for the maximum-likelihood (100 replicates). Species names are followed by their GenBank accession numbers in the brackets. The edited sequence alignment included 43357 characters and is available from the corresponding author upon request. The SWARM-14 sequence is deposited in the GenBank database under the accession number KR028987. Fig. S4. Epifluorescence microscopy imaging of the target cell hybridized with the specific probe SWARM 14-677, which was labeled with the red fluorochrome Cy3 at the 5′-end (A) and counterstained with DAPI to visualize DNA containing structures (macronucleus - Ma) and ingested bacteria in the oral field at the anterior end of the cell (B). Scale bar = 10 μm. Appendix S1. Experimental procedure.
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University of Kaiserslautern
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ISSN:1758-2229
1758-2229
DOI:10.1111/1758-2229.12307