Literature DB >> 21989298

MPN estimation of qPCR target sequence recoveries from whole cell calibrator samples.

Mano Sivaganesan1, Shawn Siefring, Manju Varma, Richard A Haugland.   

Abstract

DNA extracts from enumerated target organism cells (calibrator samples) have been used for estimating Enterococcus cell equivalent densities in surface waters by a comparative cycle threshold (Ct) qPCR analysis method. To compare surface water Enterococcus density estimates from different studies by this approach, either a consistent source of calibrator cells must be used or the estimates must account for any differences in target sequence recoveries from different sources of calibrator cells. In this report we describe two methods for estimating target sequence recoveries from whole cell calibrator samples based on qPCR analyses of their serially diluted DNA extracts and most probable number (MPN) calculation. The first method employed a traditional MPN calculation approach. The second method employed a Bayesian hierarchical statistical modeling approach and a Monte Carlo Markov Chain (MCMC) simulation method to account for the uncertainty in these estimates associated with different individual samples of the cell preparations, different dilutions of the DNA extracts and different qPCR analytical runs. The two methods were applied to estimate mean target sequence recoveries per cell from two different lots of a commercially available source of enumerated Enterococcus cell preparations. The mean target sequence recovery estimates (and standard errors) per cell from Lot A and B cell preparations by the Bayesian method were 22.73 (3.4) and 11.76 (2.4), respectively, when the data were adjusted for potential false positive results. Means were similar for the traditional MPN approach which cannot comparably assess uncertainty in the estimates. Cell numbers and estimates of recoverable target sequences in calibrator samples prepared from the two cell sources were also used to estimate cell equivalent and target sequence quantities recovered from surface water samples in a comparative Ct method. Our results illustrate the utility of the Bayesian method in accounting for uncertainty, the high degree of precision attainable by the MPN approach and the need to account for the differences in target sequence recoveries from different calibrator sample cell sources when they are used in the comparative Ct method. Published by Elsevier B.V.

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Year:  2011        PMID: 21989298     DOI: 10.1016/j.mimet.2011.09.013

Source DB:  PubMed          Journal:  J Microbiol Methods        ISSN: 0167-7012            Impact factor:   2.363


  5 in total

1.  Quantification of plasmid DNA standards for U.S. EPA fecal indicator bacteria qPCR methods by droplet digital PCR analysis.

Authors:  Mano Sivaganesan; Manju Varma; Shawn Siefring; Richard Haugland
Journal:  J Microbiol Methods       Date:  2018-07-11       Impact factor: 2.363

2.  Microbial Source Tracking Using Quantitative and Digital PCR To Identify Sources of Fecal Contamination in Stormwater, River Water, and Beach Water in a Great Lakes Area of Concern.

Authors:  Zachery R Staley; Rachel J Boyd; Phoenix Shum; Thomas A Edge
Journal:  Appl Environ Microbiol       Date:  2018-10-01       Impact factor: 4.792

3.  A Duplex Digital PCR Assay for Simultaneous Quantification of the Enterococcus spp. and the Human Fecal-associated HF183 Marker in Waters.

Authors:  Yiping Cao; Meredith R Raith; John F Griffith
Journal:  J Vis Exp       Date:  2016-03-09       Impact factor: 1.355

4.  A human fecal contamination score for ranking recreational sites using the HF183/BacR287 quantitative real-time PCR method.

Authors:  Yiping Cao; Mano Sivaganesan; Catherine A Kelty; Dan Wang; Alexandria B Boehm; John F Griffith; Stephen B Weisberg; Orin C Shanks
Journal:  Water Res       Date:  2017-10-31       Impact factor: 11.236

5.  Smartphone-Based in-Gel Loop-Mediated Isothermal Amplification (gLAMP) System Enables Rapid Coliphage MS2 Quantification in Environmental Waters.

Authors:  Xiao Huang; Xingyu Lin; Katharina Urmann; Lijie Li; Xing Xie; Sunny Jiang; Michael R Hoffmann
Journal:  Environ Sci Technol       Date:  2018-05-16       Impact factor: 9.028

  5 in total

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