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Reagent and laboratory contamination can critically impact sequence-based microbiome analyses - PubMed

  • ️Wed Jan 01 2014

Reagent and laboratory contamination can critically impact sequence-based microbiome analyses

Susannah J Salter et al. BMC Biol. 2014.

Abstract

Background: The study of microbial communities has been revolutionised in recent years by the widespread adoption of culture independent analytical techniques such as 16S rRNA gene sequencing and metagenomics. One potential confounder of these sequence-based approaches is the presence of contamination in DNA extraction kits and other laboratory reagents.

Results: In this study we demonstrate that contaminating DNA is ubiquitous in commonly used DNA extraction kits and other laboratory reagents, varies greatly in composition between different kits and kit batches, and that this contamination critically impacts results obtained from samples containing a low microbial biomass. Contamination impacts both PCR-based 16S rRNA gene surveys and shotgun metagenomics. We provide an extensive list of potential contaminating genera, and guidelines on how to mitigate the effects of contamination.

Conclusions: These results suggest that caution should be advised when applying sequence-based techniques to the study of microbiota present in low biomass environments. Concurrent sequencing of negative control samples is strongly advised.

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Figures

Figure 1
Figure 1

Summary of 16S rRNA gene sequencing taxonomic assignment from ten-fold diluted pure cultures and controls. Undiluted DNA extractions contained approximately 108 cells, and controls (annotated in the Figure with 'con') were template-free PCRs. DNA was extracted at ICL, UB and WTSI laboratories and amplified with 40 PCR cycles. Each column represents a single sample; sections (a) and (b) describe the same samples at different taxonomic levels. a) Proportion of S. bongori sequence reads in black. The proportional abundance of non-Salmonella reads at the Class level is indicated by other colours. As the sample becomes more dilute, the proportion of the sequenced bacterial amplicons from the cultured microorganism decreases and contaminants become more dominant. b) Abundance of genera which make up >0.5% of the results from at least one laboratory, excluding S. bongori. The profiles of the non-Salmonella reads within each laboratory/kit batch are consistent but differ between sites.

Figure 2
Figure 2

Copy number of total 16S rRNA genes present in a dilution series of S. bongori culture. Total bacterial DNA present in serial ten-fold dilutions of a pure S. bongori culture was quantified using qPCR. While the copy number initially reduces in tandem with increased dilution, plateauing after four dilutions indicates consistent background levels of contaminating DNA. Error bars indicate standard deviation of triplicate reactions. The broken red line indicates the detection limit of 45 copies of 16S rRNA genes. The no template internal control for the qPCR reactions (shown in blue) was below the cycle threshold selected for interpreting the fluorescence values (that is, less than 0), indicating the contamination did not come from the qPCR reagents themselves.

Figure 3
Figure 3

Summary of the metagenomic data for the S. bongori ten-fold dilution series (initial undiluted samples contained approximately 10 8 cells), extracted with four different kits. Each column represents a single sample. A sample of ultrapure water, without DNA extraction, was also sequenced (labelled ‘water’). a) As the starting material becomes more diluted, the proportion of sequenced reads mapping to the S. bongori reference genome decreases for all kits and contamination becomes more prominent. b) The profile of the non-Salmonella reads (grouped by Family, only those comprising >1% of reads from at least one kit are shown) is different for each of the four kits.

Figure 4
Figure 4

Summary of the contaminant content of nasopharyngeal samples from Thailand. a) The PCoA plot appears to show age-related clustering; however, b) extraction kit lot explains the pattern better. c) When coloured by age, the plot shows the loss of the initial clustering pattern after excluding contaminant OTUs from ordination. d) The proportion of reads attributed to contaminant OTUs for each sample, demonstrating that the first two kits were the most heavily contaminated. e) Genus-level profile of contaminant OTUs for each kit used.

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References

    1. Kunin V, Engelbrektson A, Ochman H, Hugenholtz P. Wrinkles in the rare biosphere: pyrosequencing errors can lead to artificial inflation of diversity estimates. Environ Microbiol. 2010;12:118–123. doi: 10.1111/j.1462-2920.2009.02051.x. - DOI - PubMed
    1. Fv W, Göbel UB, Stackebrandt E. Determination of microbial diversity in environmental samples: pitfalls of PCR-based rRNA analysis. FEMS Microbiol Rev. 1997;21:213–229. doi: 10.1111/j.1574-6976.1997.tb00351.x. - DOI - PubMed
    1. Kulakov LA, McAlister MB, Ogden KL, Larkin MJ, O’Hanlon JF. Analysis of bacteria contaminating ultrapure water in industrial systems. Appl Environ Microbiol. 2002;68:1548–1555. doi: 10.1128/AEM.68.4.1548-1555.2002. - DOI - PMC - PubMed
    1. McAlister MB, Kulakov LA, O’Hanlon JF, Larkin MJ, Ogden KL. Survival and nutritional requirements of three bacteria isolated from ultrapure water. J Ind Microbiol Biotechnol. 2002;29:75–82. doi: 10.1038/sj.jim.7000273. - DOI - PubMed
    1. Kéki Z, Grébner K, Bohus V, Márialigeti K, Tóth EM. Application of special oligotrophic media for cultivation of bacterial communities originated from ultrapure water. Acta Microbiol Immunol Hung. 2013;60:345–357. doi: 10.1556/AMicr.60.2013.3.9. - DOI - PubMed

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