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

Abstract

Background: Sequential membrane filtration as a pre-processing step for capturing sediment-associated 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. However, the effects of sample pre-processing via filtration on DNA-based profiling of sediment-associated microbial community diversity and composition are poorly understood. Specifically, the influences of pre-processing on the quality and quantity of extracted DNA, high-throughput DNA sequencing reads, and detected microbial taxa need further evaluation.

Results: We assessed the impact of pre-processing freshwater sediment samples by sequential membrane filtration (from 10, 5 to 0.22 μm pore size) for 16S rRNA-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 qualities and quantities of extracted DNA and PCR amplicons, and the read abundance after bioinformatics processing (i.e., denoising and chimeric-read filtering steps) between the two methods. 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%). We demonstrate that while there were differences in shared and unique taxa, both methods revealed comparable microbial diversity and community composition.

Conclusions: 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.

Keywords: 16S rRNA amplicon sequencing; River sediments; Sediment-associated microbial communities; Sequential membrane filtration.

Fig. 1

Schematic overview of the experimental procedure of the sediment-associated microbial community profiling employed in this study. A Collection of sediment samples. B Sequential membrane filtration from 10, 5 to 0.22 μm pore size filters as pre-processing step. C DNA extraction following the protocol of Zhou et al. (1996) (as employed in Solomon et al., 2016) with some modifications. D One-step PCR amplification of the 16S rRNA V4 hypervariable region. E Sequencing through the Illumina MiSeq Platform. F Bioinformatics and statistical data analysis were done in R (R Core Team, 2019)

Fig. 2

Microbial community composition. A Relative abundance of microorganisms identified by 16S rRNA amplicon sequencing. Compositions are illustrated at the phylum level. B The chord diagram indicating the log-transformed abundance of the top three Phylum detected for each filters. C Hierarchical clustering dendrogram of the similarity in community composition across the sampling sites. Color codes: blue for the non-processed (NP) sediments; green for the pre-filter (10 μm); teal for the mid-filter (5 μm); and red for the collection filter (0.22 μm)

Fig. 3

Shared and unique ASVs and genus presented in (A) venn diagrams and (B) UpSetR plots between the non-processed (NP) and pre-processed samples (represented by the collection filter, 0.22 μm), and between all groups (NP, 10, 5, and 0.22 μm) of sediment samples. Each column corresponds to number of ASV/genera that are present in each group denoted by the dark circles

Fig. 4

Linear Discriminant Analysis (LDA) Effect Size (LEfSe) plot of indicator taxa identified from non-processed (NP), and sequential filtered (10, 5, and 0.22 μm) sediment samples. A Cladogram representing the hierarchical structure of the indicator taxa identified between the non-processed and filtered samples (filter). Each filled circle represents one indicator taxa. Blue, indicator taxa statistically overrepresented in "NP"; red indicator taxa statistically overrepresented in "0.22"; green, indicator taxa statistically overrepresented in "10". B Identified indicator taxa grouped by filter and ranked by effect size. The threshold for LDA score was > 2.0. The letter before the taxa indicates taxonomic level: “p_” for phylum; “c_” for class; “o_” for order; “f_” for family; and “g_” for genus