Abstract
PCR amplification plays an integral role in the measurement of mixed microbial communities via high-throughput DNA sequencing of the 16S ribosomal RNA (rRNA) gene. Yet PCR is also known to introduce multiple forms of bias in 16S rRNA studies. Here we present a paired modeling and experimental approach to characterize and mitigate PCR NPM-bias (PCR bias from non-primer-mismatch sources) in microbiota surveys. We use experimental data from mock bacterial communities to validate our approach and human gut microbiota samples to characterize PCR NPM-bias under real-world conditions. Our results suggest that PCR NPM-bias can skew estimates of microbial relative abundances by a factor of 4 or more, but that this bias can be mitigated using log-ratio linear models.
Highlights
Polymerase Chain Reaction (PCR) amplification is an integral experimental step when profiling microbial communities by high-throughput DNA sequencing of the 16S ribosomal RNA (rRNA) gene [1]
PCR is known to introduce multiple forms of bias as DNA from some bacteria are more efficiently copied than others
We built a model of PCR NPM-bias in two stages: first, we considered a model for PCR amplification of a single template; second we extended this model to PCR NPM-bias in multi-template settings
Summary
Polymerase Chain Reaction (PCR) amplification is an integral experimental step when profiling microbial communities by high-throughput DNA sequencing of the 16S rRNA gene [1]. Bias introduced by differing amplification efficiencies between templates impedes evaluating community structure [2]. This bias has been repeatedly shown to be a substantial source of error for 16S rRNA studies [3,4,5,6,7,8] as well as in quantitative PCR (qPCR) studies [9, 10], environmental DNA studies [11], metabarcoding studies [12,13,14], and DNA methylation studies [15]. Even single nucleotide mismatches between primer and template have been shown to lead to preferential amplification of up to 10 fold [16]. Despite substantial experimental effort aimed at optimizing multi-template PCR, including limiting the number of PCR cycles [12], optimizing primers [17], and optimizing polymerases [13, 18], PCR bias remains both incompletely understood and a substantial source of error in microbiome studies [18]
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