Sediment-associated microbial community profiling: sample pre-processing through sequential membrane filtration for 16s rDNA amplicon sequencing

Abstract

Sequential membrane filtration as a pre-processing step for the isolation of microorganisms could provide good quality and integrity DNA that can be preserved and kept at ambient temperatures before community profiling through culture-independent molecular techniques, e.g., 16s rDNA amplicon sequencing. Here, we assessed the impact of pre-processing sediment samples by sequential membrane filtration (from 10, 5 to 0.22 m pore size membrane filters) for 16s rDNA-based community profiling of sediment-associated microorganisms. Specifically, we examined if there would be method-driven differences between non- and pre-processed sediment samples regarding the quality and quantity of extracted DNA, PCR amplicon, resulting high-throughput sequencing reads, microbial diversity, and community composition. We found no significant difference in the quality and quantity of extracted DNA and PCR amplicons between the two methods. Although we found a significant difference in raw and quality-filtered reads, read abundance after bioinformatics processing (i.e., denoising and the chimeric-read filtering steps) were not significantly different. These results suggest that read abundance after these read processing steps were not influenced by sediment processing or lack thereof. Although the non- and pre-processed sediment samples had more unique than shared amplicon sequence variants (ASVs), we report that their shared ASVs accounted for 74% of both methods absolute read abundance. More so at the genus level, the final collection filter identified most of the genera (95% of the reads) captured from the non-processed samples, with a total of 51 false-negative (2%) and 59 false-positive genera (3%). Accordingly, the diversity estimates and community composition were not significantly different between the non- and pre-processed samples. We demonstrate that while there were differences in shared and unique taxa, both methods revealed comparable microbial diversity and community composition. We also suggest the inclusion of sequential filters (i.e., pre- and mid-filters) in the community profiling, given the additional taxa not detected from the non-processed and the final collection filter. Our observations highlight the feasibility of pre-processing sediment samples for community analysis and the need to further assess sampling strategies to help conceptualize appropriate study designs for sediment-associated microbial community profiling.