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PMID: 19561178 Published · ppublish English Evaluation Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

Comparison of species richness estimates obtained using nearly complete fragments and simulated pyrosequencing-generated fragments in 16S rRNA gene-based environmental surveys.

Applied and environmental microbiology ·Vol. 75 ·No. 16 ·2009-08-00 ·Pages 5227-36

Youssef N, Sheik CS, Krumholz LR, Najar FZ, Roe BA, Elshahed MS

Abstract

Pyrosequencing-based 16S rRNA gene surveys are increasingly utilized to study highly diverse bacterial communities, with special emphasis on utilizing the large number of sequences obtained (tens to hundreds of thousands) for species richness estimation. However, it is not yet clear how the number of operational taxonomic units (OTUs) and, hence, species richness estimates determined using shorter fragments at different taxonomic cutoffs correlates with the number of OTUs assigned using longer, nearly complete 16S rRNA gene fragments. We constructed a 16S rRNA clone library from an undisturbed tallgrass prairie soil (1,132 clones) and used it to compare species richness estimates obtained using eight pyrosequencing candidate fragments (99 to 361 bp in length) and the nearly full-length fragment. Fragments encompassing the V1 and V2 (V1+V2) region and the V6 region (generated using primer pairs 8F-338R and 967F-1046R) overestimated species richness; fragments encompassing the V3, V7, and V7+V8 hypervariable regions (generated using primer pairs 338F-530R, 1046F-1220R, and 1046F-1392R) underestimated species richness; and fragments encompassing the V4, V5+V6, and V6+V7 regions (generated using primer pairs 530F-805R, 805F-1046R, and 967F-1220R) provided estimates comparable to those obtained with the nearly full-length fragment. These patterns were observed regardless of the alignment method utilized or the parameter used to gauge comparative levels of species richness (number of OTUs observed, slope of scatter plots of pairwise distance values for short and nearly complete fragments, and nonparametric and parametric species richness estimates). Similar results were obtained when analyzing three other datasets derived from soil, adult Zebrafish gut, and basaltic formations in the East Pacific Rise. Regression analysis indicated that these observed discrepancies in species richness estimates within various regions could readily be explained by the proportions of hypervariable, variable, and conserved base pairs within an examined fragment.

MeSH Terms
Bacteria/classification,genetics Ecosystem Genes, rRNA Molecular Sequence Data Oklahoma Poaceae RNA, Ribosomal, 16S/genetics Sequence Analysis, DNA/methods Soil Microbiology Species Specificity
Chemicals
RNA, Ribosomal, 16S
Authors & Affiliations
6 authors, click to expand affiliations / ORCID
Youssef Noha
Department of Microbiology and Molecular Genetics, Oklahoma State University, Stillwater, OK 74074, USA.
Sheik Cody S
Krumholz Lee R
Najar Fares Z
Roe Bruce A
Elshahed Mostafa S
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Article Info
Journal
Applied and environmental microbiology
Abbr.
Appl Environ Microbiol
ISSN
1098-5336
DOI
10.1128/AEM.00592-09
Published
2009-08-00
Epub
2009-00-26
Pages
5227-36
Language
English
Region
United States
NLM ID
7605801
PMCID
PMC2725448
Subset
IM
Databases
GENBANK
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FJ479273, FJ479274, FJ479275, FJ479276, FJ479277, FJ479278, FJ479279, FJ479280, FJ479281, FJ479282, FJ479283, FJ479284, FJ479285, FJ479286, FJ479287, FJ479288, FJ479289, FJ479290, FJ479291, FJ479292, FJ479293, FJ479294, FJ479295, FJ479296, FJ479297, FJ479298, FJ479299, FJ479300, FJ479301, FJ479302, FJ479303, FJ479304, FJ479305, FJ479306, FJ479307, FJ479308, FJ479309, FJ479310, FJ479311, FJ479312, FJ479313, FJ479314, FJ479315, FJ479316, FJ479317, FJ479318, FJ479319, FJ479320, FJ479321, FJ479322, FJ479323, FJ479324, FJ479325, FJ479326, FJ479327, FJ479328, FJ479329, FJ479330, FJ479331, FJ479332, FJ479333, FJ479334, FJ479335, FJ479336, FJ479337, FJ479338, FJ479339, FJ479340, FJ479341, FJ479342, FJ479343, FJ479344, FJ479345, FJ479346, FJ479347, FJ479348, FJ479349, FJ479350, FJ479351, FJ479352, FJ479353, FJ479354, FJ479355, FJ479356, FJ479357, FJ479358, FJ479359, FJ479360, FJ479361, FJ479362, FJ479363, FJ479364, FJ479365, FJ479366, FJ479367, FJ479368, FJ479369, FJ479370, FJ479371, FJ479372, FJ479373, FJ479374, FJ479375, FJ479376, FJ479377, FJ479378, FJ479379, FJ479380, FJ479381, FJ479382, FJ479383, FJ479384, FJ479385, FJ479386, FJ479387, FJ479388, FJ479389, FJ479390, FJ479391, FJ479392, FJ479393, FJ479394, FJ479395, FJ479396, FJ479397, FJ479398, FJ479399, FJ479400, FJ479401, FJ479402, FJ479403, FJ479404, FJ479405, FJ479406, FJ479407, FJ479408, FJ479409, FJ479410, FJ479411, FJ479412, FJ479413, FJ479414, FJ479415, FJ479416, FJ479417, FJ479418, FJ479419, FJ479420, FJ479421, FJ479422, FJ479423, FJ479424, FJ479425, FJ479426, FJ479427, FJ479428, FJ479429, FJ479430, FJ479431, FJ479432, FJ479433, FJ479434, FJ479435, FJ479436, FJ479437, FJ479438, FJ479439, FJ479440, FJ479441, FJ479442, FJ479443, FJ479444, FJ479445, FJ479446, FJ479447, FJ479448, FJ479449, FJ479450, FJ479451, FJ479452, FJ479453, FJ479454, FJ479455, FJ479456, FJ479457, FJ479458, FJ479459, FJ479460, FJ479461, FJ479462, FJ479463, FJ479464, FJ479465, FJ479466, FJ479467, FJ479468, FJ479469, FJ479470, FJ479471, FJ479472
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PubMed source DOI: 10.1128/AEM.00592-09 PMC Full Text View journal details
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