Exploring Microbial Diversity and Taxonomy Using SSU rRNA Hypervariable Tag Sequencing

University of California Davis, United States of America
PLoS Genetics (Impact Factor: 7.53). 12/2008; 4(11):e1000255. DOI: 10.1371/journal.pgen.1000255
Source: PubMed

ABSTRACT Massively parallel pyrosequencing of hypervariable regions from small subunit ribosomal RNA (SSU rRNA) genes can sample a microbial community two or three orders of magnitude more deeply per dollar and per hour than capillary sequencing of full-length SSU rRNA. As with full-length rRNA surveys, each sequence read is a tag surrogate for a single microbe. However, rather than assigning taxonomy by creating gene trees de novo that include all experimental sequences and certain reference taxa, we compare the hypervariable region tags to an extensive database of rRNA sequences and assign taxonomy based on the best match in a Global Alignment for Sequence Taxonomy (GAST) process. The resulting taxonomic census provides information on both composition and diversity of the microbial community. To determine the effectiveness of using only hypervariable region tags for assessing microbial community membership, we compared the taxonomy assigned to the V3 and V6 hypervariable regions with the taxonomy assigned to full-length SSU rRNA sequences isolated from both the human gut and a deep-sea hydrothermal vent. The hypervariable region tags and full-length rRNA sequences provided equivalent taxonomy and measures of relative abundance of microbial communities, even for tags up to 15% divergent from their nearest reference match. The greater sampling depth per dollar afforded by massively parallel pyrosequencing reveals many more members of the "rare biosphere" than does capillary sequencing of the full-length gene. In addition, tag sequencing eliminates cloning bias and the sequences are short enough to be completely sequenced in a single read, maximizing the number of organisms sampled in a run while minimizing chimera formation. This technique allows the cost-effective exploration of changes in microbial community structure, including the rare biosphere, over space and time and can be applied immediately to initiatives, such as the Human Microbiome Project.

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Available from: David A Relman, Sep 26, 2015
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    • "ary Table S2 ) were trimmed to a conserved region ( read length of 371 or 362 base pairs , respectively ) . Subsequently , we used UCLUST ( Edgar , 2010 ) to cluster the sequences into OTUs based on a 97% similarity threshold ( Supplementary Table S3 ) . Representative sequences from each cluster were selected and taxonomy was assigned with GAST ( Huse et al . , 2008 ) . To determine diversity indexes ( Good ' s coverage , Shannon ' s Index , Richness , Evenness , Inverse Simp - son ) and Morisita – Horn indexes , we used R ( R - Core - Team , 2012 ) and the vegan package ( Oksanen et al . , 2013 ; Supplementary Table S4 ) . Two samples had substantially lower read counts ( 6 - DUR - MC335 , 8217 re"
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    • "Total DNA from sediment and strains was extracted and purified with a FastDNA Spin kit for soil (MP Biomedicals, California, USA) and a DNA purification kit (Tiangen, China), according to the manufacturers' recommendations. The V3 region of bacterial 16S rRNA gene was amplified using primers 338F (5=-ACTCCTACGGGAGGCAGCAG-3=) and 533R (5=- TTACCGCGGCTGCTGGCAC-3=) with identified barcodes (Huse et al. 2008). PCR amplifications were performed using a thermocycler (Eppendorf, Hamburg, "
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