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      BactQuant: An enhanced broad-coverage bacterial quantitative real-time PCR assay

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          Abstract

          Background

          Bacterial load quantification is a critical component of bacterial community analysis, but a culture-independent method capable of detecting and quantifying diverse bacteria is needed. Based on our analysis of a diverse collection of 16 S rRNA gene sequences, we designed a broad-coverage quantitative real-time PCR (qPCR) assay—BactQuant—for quantifying 16 S rRNA gene copy number and estimating bacterial load. We further utilized in silico evaluation to complement laboratory-based qPCR characterization to validate BactQuant.

          Methods

          The aligned core set of 4,938 16 S rRNA gene sequences in the Greengenes database were analyzed for assay design. Cloned plasmid standards were generated and quantified using a qPCR-based approach. Coverage analysis was performed computationally using >670,000 sequences and further evaluated following the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines.

          Results

          A bacterial TaqMan® qPCR assay targeting a 466 bp region in V3-V4 was designed. Coverage analysis showed that 91% of the phyla, 96% of the genera, and >80% of the 89,537 species analyzed contained at least one perfect sequence match to the BactQuant assay. Of the 106 bacterial species evaluated, amplification efficiencies ranged from 81 to 120%, with r 2 -value of >0.99, including species with sequence mismatches. Inter- and intra-run coefficient of variance was <3% and <16% for Ct and copy number, respectively.

          Conclusions

          The BactQuant assay offers significantly broader coverage than a previously reported universal bacterial quantification assay BactQuant in vitro performance was better than the in silico predictions.

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          Most cited references20

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          OligoCalc: an online oligonucleotide properties calculator

          We developed OligoCalc as a web-accessible, client-based computational engine for reporting DNA and RNA single-stranded and double-stranded properties, including molecular weight, solution concentration, melting temperature, estimated absorbance coefficients, inter-molecular self-complementarity estimation and intra-molecular hairpin loop formation. OligoCalc has a familiar ‘calculator’ look and feel, making it readily understandable and usable. OligoCalc incorporates three common methods for calculating oligonucleotide-melting temperatures, including a nearest-neighbor thermodynamic model for melting temperature. Since it first came online in 1997, there have been more than 900 000 accesses of OligoCalc from nearly 200 000 distinct hosts, excluding search engines. OligoCalc is available at http://basic.northwestern.edu/biotools/OligoCalc.html, with links to the full source code, usage patterns and statistics at that link as well.
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            rRNA operon copy number reflects ecological strategies of bacteria.

            Although natural selection appears to favor the elimination of gene redundancy in prokaryotes, multiple copies of each rRNA-encoding gene are common on bacterial chromosomes. Despite this conspicuous deviation from single-copy genes, no phenotype has been consistently associated with rRNA gene copy number. We found that the number of rRNA genes correlates with the rate at which phylogenetically diverse bacteria respond to resource availability. Soil bacteria that formed colonies rapidly upon exposure to a nutritionally complex medium contained an average of 5.5 copies of the small subunit rRNA gene, whereas bacteria that responded slowly contained an average of 1.4 copies. In soil microcosms pulsed with the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D), indigenous populations of 2,4-D-degrading bacteria with multiple rRNA genes ( = 5.4) became dominant, whereas populations with fewer rRNA genes ( = 2.7) were favored in unamended controls. These findings demonstrate phenotypic effects associated with rRNA gene copy number that are indicative of ecological strategies influencing the structure of natural microbial communities.
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              Determination of bacterial load by real-time PCR using a broad-range (universal) probe and primers set.

              The design and evaluation of a set of universal primers and probe for the amplification of 16S rDNA from the Domain Bacteria to estimate total bacterial load by real-time PCR is reported. Broad specificity of the universal detection system was confirmed by testing DNA isolated from 34 bacterial species encompassing most of the groups of bacteria outlined in Bergey's Manual of Determinative Bacteriology. However, the nature of the chromosomal DNA used as a standard was critical. A DNA standard representing those bacteria most likely to predominate in a given habitat was important for a more accurate determination of total bacterial load due to variations in 16S rDNA copy number and the effect of generation time of the bacteria on this number, since rapid growth could result in multiple replication forks and hence, in effect, more than one copy of portions of the chromosome. The validity of applying these caveats to estimating bacterial load was confirmed by enumerating the number of bacteria in an artificial sample mixed in vitro and in clinical carious dentine samples. Taking these parameters into account, the number of anaerobic bacteria estimated by the universal probe and primers set in carious dentine was 40-fold greater than the total bacterial load detected by culture methods, demonstrating the utility of real-time PCR in the analysis of this environment.
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                Author and article information

                Contributors
                Journal
                BMC Microbiol
                BMC Microbiol
                BMC Microbiology
                BioMed Central
                1471-2180
                2012
                17 April 2012
                : 12
                : 56
                Affiliations
                [1 ]Division of Pathogen Genomics, Translational Genomics Research Institute, 3051 W. Shamrell Blvd., Suite 106, Flagstaff, AZ 86001, USA
                [2 ]Center for Microbial Genetics and Genomics, Applied Research & Development Building,, Northern Arizona University, 1298 S. Knoles Drive, Flagstaff, AZ, 86011, USA
                [3 ]Departments of Laboratory Medicine and Internal Medicine, National Taiwan University Hospital, National Taiwan University College of Medicine, No. 7, Chung-Shan South Road, Taipei, Taiwan
                [4 ]Department of Internal Medicine, Far Eastern Memorial Hospital, No.21, Nanya S. Rd., New Taipei City, Taiwan
                [5 ]Current Address: Ross University School of Medicine, 630 US Highway 1, North Brunswick, NJ, 08902, USA
                Article
                1471-2180-12-56
                10.1186/1471-2180-12-56
                3464140
                22510143
                6919857f-a6f9-477b-ae56-7b0907edd016
                Copyright ©2012 Liu et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 28 October 2011
                : 6 March 2012
                Categories
                Methodology Article

                Microbiology & Virology
                Microbiology & Virology

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