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To explore the ability to diversify the community of soil microcosms, we compared the microbial communities in compost microcosms prepared by enriching the same initial soil, an industrial-grade compost available from the city of Berkeley, California, with different produce: apples, bell peppers, oranges, or potatoes (each set in triplicate). We further compared the microbial communities of each compost environment with the gut microbiome of wild-type C. elegans raised in the respective microcosm. Analysis was performed with DNA samples extracted from roughly 500 surface-sterilized adults per microcosm and from 250 mg compost samples of the respective microcosms.
Characterization of the environmental soil and worm gut microbiomes relied on next-generation sequencing of the V4 region of the bacterial 16S rRNA gene. Sequencing library preparation was achieved using the standard kits and performed according to the manufacturers' instructions, with sequencing performed on a commercial sequencer (see Table of Materials). Demultiplexed sequences were processed using DADA2, assigned taxonomy based on the SILVA v132 reference database, and analyzed with phyloseq16,17,18 (see Supplementary File 1, Supplementary Figure S1, Supplementary Figure S2, Supplementary Figure S3, Supplementary Table S1, and Supplementary Table S2 for a detailed description of the sequencing and analysis; the full computational pipeline is available in GitHub [https://github.com/kennytrang/CompostMicrocosms]). Raw data are available at the NCBI Sequence Read Archive (Bioproject ID PRJNA856419).
On average, 73,220 sequences were obtained per sample. These sequences represent 15,027 amplicon sequence variants (ASVs), spanning 27 phyla and 216 families, including families considered part of the core C. elegans gut microbiome13, such as Rhizobiaceae, Burkholderiaceae, and Bacillaceae. Enterobacteriaceae, and Pseudomonadaceae, which were previously found to be dominant members, were a minority this time, but were still enriched (2-10-fold) compared to their respective soil environments. Comparisons based on both unweighted and weighted UniFrac19,20 distances demonstrated good reproducibility among microcosm triplicates enriched with the same produce, as indicated by close clustering. In contrast, environmental soil microbiomes enriched with different produce clustered away from each other, demonstrating the ability to diversify an initial microbial community through the addition of different produce (Figure 2).
In comparisons of worm gut microbiomes and environmental communities, principal coordinate analysis (PCoA) with either unweighted or weighted UniFrac distances showed distinct clustering of worm gut microbiomes away from that of their respective environments for each microcosm type (Figure 2). While PCoA based on unweighted UniFrac distances did not distinguish between soil and worm microbiomes (Figure 2A), clustering based on weighted distances revealed a clear separation of worm gut and compost microbiomes (Figure 2B). These results support a process in which host filtering operates on environmental availability to shape a gut microbiome that is not completely distinct from its environmental source with regard to the presence of taxa but modulates their abundance by enriching for a subset of the available taxa, ultimately resulting in a core worm gut microbiome shared between worms raised in different environments.

Figure 2: Worm gut microbiomes clustering away from their respective produce-diversified microbial environments. Microbiome composition was determined with 16S sequencing, and communities from microcosms enriched with the designated produce or from worms raised in them were clustered using PCoA based on (A) unweighted or (B) weighted UniFrac distances. Axes shown are those that explain the greatest variation in community composition between samples (N = 3 for each microcosm type). Please click here to view a larger version of this figure.
Supplementary File 1: Next-generation sequencing and data analysis. Presented here are the steps for library preparation, in-lab sequencing, and data analysis. Please click here to download this File.
Supplementary Figure S1: An example of a quality control graph for the reverse reads from one sample. The X-axis (cycle) shows the nucleotide position along the sequence read. The left Y-axis shows the quality score. The greyscale heatmap represents the frequency of the quality score at each nucleotide position; the green line depicts the median quality score at each nucleotide position; the top orange line depicts the quartiles of the quality score distribution; the bottom red line depicts the percent of sequence reads that extended that nucleotide position (right Y-axis, here 100%). Please click here to download this File.
Supplementary Figure S2: Error rates for different samples. The error frequency in the different samples (black dots) should decrease with increasing quality score for each possible base pair substitution depicted, reflecting the expected trend. Please click here to download this File.
Supplementary Figure S3: An example of PCoA based on weighted UniFrac distances. The group names shown in the legend represent the produce used to enrich the compost used in the different microcosms. Please click here to download this File.
Supplementary Table S1: Sequential sequence filtering. aNumber of sequence reads before filtering. b-dEach column represents the number of sequence reads remaining after a filtration step: filtering out low quality reads (step 2.5), denoising algorithm performed by dada() (step 2.8), merging forward and reverse reads (step 2.9), and removing chimeras (step 2.11). Please click here to download this File.
Supplementary Table S2: Metadata table. Please click here to download this File.