Abstract
Both the host microbiome and the microbiome of the built environment can have profound impacts on human health. While prior studies have suggested that the variability introduced by DNA extraction method is less than typical biologic variation, most studies have focused on 16S rRNA amplicon sequencing or on high biomass fecal samples. Shotgun metagenomic sequencing provides advantages over amplicon sequencing for surveying the microbiome, but is a challenge to perform in lower microbial biomass samples with high human DNA content such as sputum or vacuumed dust. Here we systematically evaluate the impact of four different extraction methods (phenol:choloroform, and three high-throughput kit-based approaches, the Promega Maxwell gDNA, Qiagen MagAttract PowerSoil DNA, and ZymoBIOMICS 96 MagBead). We report the variation in microbial community structure and predicted microbial function assessed by shotgun metagenomics sequencing in human stool, sputum, and vacuumed dust obtained from ongoing cohort studies or clinical trials. The same beadbeating protocol was used for all samples to focus our evaluation on the impact of kit chemistries on sequencing results. DNA yield was overall highest in the phenol:choloroform and Promega approaches. Only the phenol:choloroform approach showed evidence of contamination in negative controls. Bias was evaluated using mock community controls, and was noted across all extraction methods, although Promega exhibited the least amount of bias. The extraction method did not impact the proportion of human reads, although stool had the lowest proportion of human reads (0.1%) as compared to dust (44.1%) and sputum (80%). We calculated Bray-Curtis dissimilarity and Aitchison distances to evaluate the impact of extraction method on microbial community structure by sample type. Extraction method had the lowest impact in stool (extraction method responsible for 3.0-3.9% of the variability), the most impact in vacuumed dust (12-16% of the variability) and intermediate values for sputum (9.2-12% variability). Similar differences were noted when evaluating microbial community function. Our results will inform investigators planning microbiome studies using diverse sample types in large clinical studies. A consistent DNA extraction approach across all sample types is recommended, particularly with lower microbial biomass samples that are more heavily influenced by extraction method.