Literature DB >> 30137345

Dependency of DNA extraction efficiency on cell concentration confounds molecular quantification of microorganisms in groundwater.

Alison Ws Luk1, Sabrina Beckmann1, Mike Manefield1,2.   

Abstract

Quantification of microbes in water systems is essential to industrial practices ranging from drinking water and wastewater treatment to groundwater remediation. While quantification using DNA-based molecular methods is precise, the accuracy is dependent on DNA extraction efficiencies. We show that the DNA yield is strongly impacted by the cell concentration in groundwater samples (r = -0.92, P < 0.0001). This has major implications for industrial applications using quantitative polymerase chain reaction (qPCR) to determine cell concentrations in water, including bioremediation. We propose a simple normalization method using a DNA recovery ratio, calculated with the total cell count and DNA yield. Application of this method to enumeration of bacteria and archaea in groundwater samples targeting phylogenetic markers (16S rRNA) demonstrated an increased goodness of fit after normalization (7.04 vs 0.94 difference in Akaike's information criteria). Furthermore, normalization was applied to qPCR quantification of functional genes and combined with DNA sequencing of archaeal and bacterial 16S rRNA genes to monitor changes in abundance of methanogenic archaea and sulphate-reducing bacteria in groundwater. The integration of qPCR and DNA sequencing with appropriate normalization enables high-throughput quantification of microbial groups using increasingly affordable and accessible techniques. This research has implications for microbial ecology and engineering research as well as industrial practice.

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Year:  2018        PMID: 30137345     DOI: 10.1093/femsec/fiy146

Source DB:  PubMed          Journal:  FEMS Microbiol Ecol        ISSN: 0168-6496            Impact factor:   4.194


  1 in total

1.  Characterization of Mixed-Species Biofilm Formed by Vibrio parahaemolyticus and Listeria monocytogenes.

Authors:  Ping Chen; Jing Jing Wang; Bin Hong; Ling Tan; Jun Yan; Zhaohuan Zhang; Haiquan Liu; Yingjie Pan; Yong Zhao
Journal:  Front Microbiol       Date:  2019-11-08       Impact factor: 5.640

  1 in total

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