Bacterial genomic DNA was isolated from mouse fecal pellets (31), and concentration was determined using a Qubit 2.0 fluorometer (Invitrogen). Variable regions 3 and 4 of the 16S rRNA gene were amplified by polymerase chain reaction using barcoded primers (32) before sequencing on an Illumina MiSeq in the Nicole Perkins Microbial Communities Core Laboratory at the University of Calgary. Primers and poor-quality reads were removed using Cutadapt v1.16 and then processed using DADA2 v1.6.0 (33). Taxonomy was assigned using the Silva Taxonomy Training Set v128 and the Silva Species Assignment Set v128 (34). Visual and statistical analyses of amplicon sequencing data were performed using the R packages Phyloseq (35) and Vegan v2.5-2 with support from dplyr 0.7.5 and ggplot2 v3.0.0. Reads of nonbacterial origin and samples with insufficient depth (<10,000 reads) were removed. Observed species, Shannon diversity index, and Simpson’s index were determined. Multiple indices were used to ensure that a potential bias did not arise from unequal numbers of reads between samples. β-Diversity was assessed using an NMDS plot of Bray-Curtis dissimilarity. The R package DESeq2 v1.18.1 and microbiomeSeq v0.1 were used to calculate significantly differentially abundant taxa in the vancomycin-treated group over time. Prior to further analysis and visualization of taxa-specific differences between samples or treatment groups, reads in each sample were normalized to relative abundance.

For input into PICRUSt v1.1.3, reads were reprocessed into operational taxonomic units (OTUs) (36). Read quality was assessed using FastQC v0.11.5, and paired-end reads were merged and filtered using PEAR v0.9.10. Chimeric sequences were removed using VSEARCH v1.11. Sequences were clustered into OTUs using a closed reference OTU picking pipeline using QIIME v1.91 (37) and mapped to the Greengenes Database v13_8. Within PICRUSt, the OTU table was 16S rRNA copy number normalized, and then functional predictions of KEGG orthologs were predicted using PICRUSt’s Hidden State Prediction algorithm.

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