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The taxonomic composition of each meal microbiome was assessed via amplification and sequencing of 16S rDNA from the homogenized meals. DNA was extracted from homogenized food samples with the Power Food Microbial DNA Isolation Kit (MoBio Laboratories, Inc.) according to the manufacturer’s protocol. Microbial DNA was amplified by a two-step PCR enrichment of the 16S rRNA gene (V4 region) using primers 515F and 806R, modified by addition of Illumina adaptor and barcodes sequences. All primer sequences are provided in Supplementary Table 2, and PCR conditions are provided in Supplementary Datafile 1. Libraries were sequenced using an Illumina MiSeq system, generating 150bp paired-end amplicon reads. The amplicon data was multiplexed using dual barcode combinations for each sample. We used a custom script (available on [github](https://github.com/gjospin/scripts/blob/master/Demul_trim_prep.pl), with code provided in Supplementary Datafile 2) to assign each pair of reads to their respective samples when parsing the raw data. This script allows for 1 base pair difference per barcode used (2 per sample) to accommodate for read errors from the machine. The paired reads were then aligned and a consensus was computed using FLASH \cite{21903629} with maximum overlap of 120 and a minimum overlap of 70 (other parameters were left as default). The custom script automatically demultiplexes the data into fastq files, executes FLASH, and parses its results to reformat the sequences with appropriate naming conventions for QIIME v. 1.8.0 \cite{20383131} in fasta format. The resulting consensus sequences were analyzed using the QIIME pipeline.  ##Statistical Analyses  Unless otherwise noted, all statistical analyses were performed using python scripts implemented in QIIME v. 1.8.0, and all python scripts referenced here are QIIME scripts. The IPython notebook file used for all QIIME analyses is available HERE. To explore the differences in overall microbial community composition across the 15 meals, both the phylogenetic weighted UniFrac distances \cite{20827291} and the taxonomic Bray-Curtis dissimilarities \cite{Bray_1957} were calculated using the beta\_diversity\_through\_plots.py script. This script also produced a principal coordinates analysis (PCoA) in which weighted Unifrac distances between samples to visualize differences among groups of samples (see Figure 2 for this type of visualization for the three Diet Types.)  To test for the significance of dietary pattern on the overall microbial community composition, we used a permutational multivariate ANOVA as implemented in the compare\_categories.py script. To test for significant differences in taxonomic richness across dietary patterns, we used the non-parametric Kruskal-Wallis test \cite{Kruskal_1952} with the FDR (false discovery rate) correction as implemented in compare\_alpha\_diversity.py. To test for the significant variation in frequency of individual OTUs across dietary patterns, we used the Kruskal-Wallis test with the FDR correction as implemented in the group\_significance.py script. To test for significant correlation between the relative abundance of a single taxonomic group and meal metadata categories (i.e. nutrient composition, whether a meal contains fermented foods, etc) at 5 taxonomic levels (phylum-genus,) Pearson correlation coefficients \cite{Pearson_1895} were calculated and tested for statistical significance using Stata (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX: StataCorp LP.) ##Metagenome Prediction with PICRUSt  A synthetic metagenome was generated based on the observed 16S rDNA sequences for each meal. To do this, the 16S rDNA sequences were clustered into a collection of OTUs sharing 99% sequence identity, using the pick\_closed\_reference\_otus.py. The resultant OTU table was normalized with respect to inferred 16S rRNA gene copy numbers using the normalize\_by\_copy\_number.py script distributed with PICRUSt v. 1.0.0 \cite{23975157}. The normalized OTU table was used to predict meal microbial metagenomes with PICRUSt's predict\_metagenomes.py script. The final predicted metagenome is output as a .biom table, which is suitable for analysis with a tool such as STAMP \cite{25061070}. We used STAMP to test for and visualize significant (predicted) functional differences in microbial communities between the three dietary patterns.