|CASTLE, SARAH - University Of Minnesota|
|SONG, ZEWEI - University Of Minnesota|
|GOHL, DARYL - University Of Minnesota|
|GUTKNECHT, JESSICA - University Of Minnesota|
|ROSEN, CARL - University Of Minnesota|
|SADOWSKY, MICHAEL - University Of Minnesota|
|Samac, Deborah - Debby|
|KINKEL, LINDA - University Of Minnesota|
Submitted to: Phytobiomes Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/15/2018
Publication Date: 7/11/2018
Citation: Castle, S.C., Song, Z., Gohl, D.M., Gutknecht, J., Rosen, C., Sadowsky, M.J., Samac, D.A., Kinkel, L.L. 2018. DNA template dilution greatly impacts amplicon-sequencing based estimates of soil fungal diversity. Phytobiomes Journal. 2:100-107. doi: 10.1094/pbiomes-09-17-0037-r.
Interpretive Summary: High throughput, inexpensive DNA sequencing methods are being used extensively to survey the microbial communities associated with plants as well as in soil and water environments. A number of experimental methods associated with DNA extraction and sequencing have been found that can affect the output, preventing an accurate identification of all community members. We found that dilution of DNA, which is commonly done to optimize the amplification step prior to sequencing, resulted in significantly under-estimating the relative abundance of rare fungi and over-estimating the abundance of common fungi leading to poor representations of the number and distribution of fungal species in the soil communities. Quantifying the number of DNA templates in each sample and then adjusting the number of sequences obtained was found to reduce this potential bottlenecking on estimates of sample diversity. This information will be useful in research to investigate microbial communities across diverse habitats and experimental treatments in which microbial DNA concentrations can vary widely.
Technical Abstract: Next generation sequencing of taxonomically-relevant marker genes has enabled researchers to sample the richness, diversity, and composition of environmental microbiomes at previously unattainable sequence depths. However, variable molecular methods may have unintended downstream consequences, and the inadvertent under sampling of the microbial community may be a significant pitfall in microbiome profiling. Dilution of the DNA template prior to polymerase chain reaction (PCR) may be done to improve marker gene amplification, reduce chimeric read formation, and decrease PCR inhibitor concentrations. However, dilution unavoidably reduces DNA template number per sample. We evaluated the effects of pre-PCR DNA template dilution on estimates of soil fungal microbiome diversity, composition, and species abundance distribution across a collection of 145 agricultural soil samples. Fungal DNA templates were serially diluted 10-, 100-, and 1000-fold and sequence data of diluted templates was compared to that of an identical set of undiluted templates. In addition, we serially diluted fungal DNA extracts from a set of three prairie soil samples in triplicate and sequenced undiluted and diluted samples. DNA template dilution significantly reduced estimates of fungal richness and diversity, as compared to undiluted samples. Dilution of DNA template resulted in reduced relative abundances of rare operational taxonomic units (OTUs) and elevated relative abundances of common OTUs. Collectively, changes in OTU abundance distributions following sample dilution produced substantial shifts in overall fungal community composition. Our results highlight risks associated with sample dilution and help to elucidate the potential utility of quantifying pre-PCR template concentration in the estimation of microbial communities. We urge researchers to reconsider routine dilution of pre-PCR DNA templates particularly for low diversity and low abundance microbiome samples. As efforts to profile the environmental microbiomes using molecular sequencing approaches accelerate, developing an adequate understanding of potential methodological bottlenecks will increase our ability to make meaningful connections among data sets.