Submitted to: Molecular Ecology Resources
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/19/2018
Publication Date: 2/1/2018
Citation: Bakker, M.G. 2018. A fungal mock community control for amplicon sequencing experiments. Molecular Ecology Resources. 18(3):541-556. https://doi.org/10.1111/1755-0998.12760.
Interpretive Summary: Plants, soils, and many other environments host diverse communities of microorganisms (often called microbiomes). Microbiomes can have substantial impacts on plant health and food safety. For instance, plant disease may be reduced where the plant microbiome is able to prevent pathogen colonization. Similarly, food safety may be protected by microbiomes that are able to limit the accumulation of mycotoxins in grain. Current research methods for studying microbiomes are typically based on analysis of DNA, which offers several advantages over older methods. However, there are still limitations to DNA-based methods, and these can lead to erroneous conclusions. In order to address this problem, we developed artificial mixtures of fungal DNA that can be used as an experimental control to identify sources of error in microbiome studies, so that methods can be improved and limitations can be considered when interpreting results. The resources developed in this research will be shared with other scientists to promote the adoption of best practices in microbiome studies. As a consequence, our understanding of the role of microbiomes in plant health and food safety will advance more quickly, leading to new strategies for supporting the production of a safe and abundant food supply.
Technical Abstract: Microbial ecology has been profoundly advanced by the ability to profile complex microbial communities by sequencing of marker genes amplified from environmental samples. However, inclusion of appropriate controls is vital to revealing the limitations and biases of this technique. “Mock community” samples, in which the composition and relative abundances of community members are known, are particularly valuable for guiding library preparation and data processing decisions. I generated a set of three mock communities using 19 different fungal taxa and demonstrate their utility by contrasting amplicon sequencing data obtained for the same communities under modifications to PCR conditions during library preparation. Increasing the number of PCR cycles elevated rates of chimera formation, and of errors in the final data set. Extension time during PCR had little impact on chimera formation, error rate or observed community structure. Polymerase fidelity impacted error rates significantly. Despite a high error rate, a master mix optimized to minimize amplification bias yielded profiles that were most similar to the true community structure. Bias against particular taxa differed among ITS1 vs. ITS2 loci. Preclustering nearly identical reads substantially reduced error rates, but did not improve similarity to the expected community structure. Inaccuracies in amplicon sequence-based estimates of fungal community structure were associated with amplification bias and size selection processes, as well as variable culling rates among reads from different taxa. In some cases, the numerically dominant taxon was completely absent from final data sets, highlighting the need for further methodological improvements to avoid biased observations of community profiles.