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Identification and removal of contaminating microbial DNA from PCR reagents: impact on low-biomass microbiome analyses.

Abstract：

:Reagent-derived contamination can compromise the integrity of microbiome data, particularly in low microbial biomass samples. This contamination has recently been attributed to the 'kitome' (contamination introduced by the DNA extraction kit), prior to which attention was mostly paid to potential contamination introduced by PCR reagents. In this study, we assessed the proportion to which our DNA extraction kit and PCR master mix introduce contaminating microbial DNA to bacterial microbial profiles generated by 16S rRNA gene sequencing. Utilizing a commercial dsDNase treatment protocol to decontaminate the PCR master mix, we demonstrated that the vast majority of contaminating DNA was derived from the PCR master mix. Importantly, this contamination was almost completely eliminated using the simple dsDNase treatment, resulting in a 99% reduction in contaminating bacterial reads. We suggest that dsDNase treatment of PCR reagents should be explored as a simple and effective way of reducing contamination in low-biomass microbiome studies and producing more robust and reliable data. SIGNIFICANCE AND IMPACT OF THE STUDY: Reagent contamination with microbial DNA is a major problem in microbiome studies of low microbial biomass samples. Levels of such contaminating DNA often outweigh what is present in the sample and heavily confound subsequent data analysis. Previous studies have suggested this contamination is primarily derived from DNA extraction kits. Here, we identified the PCR master mix as the primary source of contamination, and showed that enzymatic removal of the contamination drastically reduced the blank signal and improved precision. Decontamination of PCR master mixes may have the potential to improve the sensitivity and accuracy of low-biomass microbiome studies.

journal_name

Lett Appl Microbiol

journal_title

Letters in applied microbiology

authors

Stinson LF,Keelan JA,Payne MS

doi

10.1111/lam.13091

subject

Has Abstract

pub_date

2019-01-01 00:00:00

pages

2-8

issue

1

eissn

0266-8254

issn

1472-765X

journal_volume

68

pub_type

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