Decontamination of 16S rRNA gene amplicon sequence datasets based on bacterial load assessment by qPCR

Abstract

BackgroundIdentification of unexpected taxa in 16S rRNA surveys of low-density microbiota, diluted mock communities and cultures demonstrated that a variable fraction of sequence reads originated from exogenous DNA. The sources of these contaminants are reagents used in DNA extraction, PCR, and next-generation sequencing library preparation, and human (skin, oral and respiratory) microbiota from the investigators.ResultsFor in silico removal of reagent contaminants, a pipeline was used which combines the relative abundance of operational taxonomic units (OTUs) in V3–4 16S rRNA gene amplicon datasets with bacterial DNA quantification based on qPCR targeting of the V3 segment of the 16S rRNA gene. Serially diluted cultures of Escherichia coli and Staphylococcus aureus were used for 16S rDNA profiling, and DNA from each of these species was used as a qPCR standard. OTUs assigned to Escherichia or Staphylococcus were virtually unaffected by the decontamination procedure, whereas OTUs from Pseudomonas, which is a major reagent contaminant, were completely or nearly completely removed. The decontamination procedure also attenuated the trend of increase in OTU richness in serially diluted cultures.ConclusionsRemoval of contaminant sequences derived from reagents based on use of qPCR data may improve taxonomic representation in samples with low DNA concentration. Using the described pipeline, OTUs derived from cross-contamination of negative extraction controls were not recognized as contaminants and not removed from the sample dataset.Electronic supplementary materialThe online version of this article (doi:10.1186/s12866-016-0689-4) contains supplementary material, which is available to authorized users.

Bacterial microbiota assemblage in Aedes albopictus mosquitoes and its impacts on larval development

“…Quantification of total bacteria in the water samples followed the method of Lazarevic et al. (). Series dilutions of E. coli DH5α strain genomic DNA of known concentration were used for qPCR to build the reference curve.…”

“…We conducted qPCR to quantify the total bacteria load in mosquito samples used in the microbiota analysis described above, following the method of Lazarevic, Gaia, Girard, and Schrenzel (2016). In brief, the qPCR used the 515F/806R primer pair that targets the V4 region of the 16S rRNA gene, mosquito rpS7 gene, as the reference gene (Wang, Gilbreath, Kukutla, Yan, & Xu, 2011…”

Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data

“…DNA concentrations measured from prepared amplicon or shotgun libraries prior to sequencing, often in the form of standardized fluorescent intensities, works effectively with the frequency method in our experience. More effort-intensive methods, such as qPCR, may improve accuracy further if those methods more accurately quantify total DNA [31]. Typically, sufficient variation in DNA concentration for the frequency method to discriminate between contaminants and non-contaminants arises naturally during sample preparation and processing.…”

“…In silico contaminant removal can complement existing laboratory approaches, but distinguishing contaminating microbial DNA from true microbial sequences can be difficult and is not often performed [14,16]. Perhaps the most common in silico decontamination method in practice is the removal of sequences below an ad hoc abundance threshold [21,[29][30][31]. However, abundance thresholds remove rare features truly present in the sample, and do not remove abundant contaminants that are the most likely to interfere with subsequent analysis.…”

“…Sequences from contaminating taxa are likely to have frequencies that inversely correlate with sample DNA concentration [8,14,16,30], and 2) sequences from contaminating taxa are likely to have higher prevalence in control samples than in true samples [10,31,32]. Frequency-based contaminant identification relies on auxiliary DNA quantitation data that is in most cases intrinsic to MGS sample preparation.…”