We performed a preliminary study designed to generate hypotheses about the microbes we eat, and how they vary in terms of total abundance and relative composition in different meals and dietary patterns typical of American dietary intakes. We have selected to characterize the microbiota of 15 meals that exemplify the typical meals consumed as part of three different dietary patterns in order to determine the average total amount of daily microbes ingested via food and beverages and their composition in the average American adult consuming these typical foods/diets: (1) the Average American dietary pattern (AMERICAN) focused on convenience foods, (2) the USDA recommended dietary pattern (USDA) emphasizing fruits and vegetables, lean meat, dairy, and whole grains, and (3) the Vegan (VEGAN) dietary pattern, which excludes all animal products. We used DNA sequencing, plate counting, and informatics methods to characterize microbes in these meals and dietary patterns.

Yet, little is known about the effects of ingested microorganisms on gut microbiota composition or function, and even the basic questions of which microbes, how many of them, and how much they vary from diet to diet and meal to meal, have not been answered. We do know about the microbial ecology of various specialty foods where fermentation, colonization, ripening, and/or aging are part of the preparation of these foods, for example pancetta ( Busconi, Zacconi & Scolari, 2014 ) and of course cheese ( Gatti et al., 2008 ; Button & Dutton, 2012 ). The microbial ecology of the surfaces of raw plant-derived foods such as fruits and vegetables has also been characterized ( Leff & Fierer, 2013 ). There is a large base of literature on food-borne pathogens ( Aboutaleb, Kuijper & Van Dissel, 2014 ). Furthermore, it is known that the microbial ecology of endemic microbes found on food surfaces can affect mechanisms by which pathogens colonize these foods ( Critzer & Doyle, 2010 ). A recent article showed that certain ingested microbes found in foods such as cheese and deli meats were detected in the stool of individuals who consumed them, and that furthermore they were culturable and thus survived transit through the upper intestinal tract ( David et al., 2013 ). However, the microbial ecology or microbial assemblages of different meals and diets, as well as the total number of live microorganisms ingested in these meals and diets are largely unknown. In fact, studies of the effects of diets and foods on the gut microbiota rely on dietary recalls and other dietary reporting instruments that were not designed to capture the potential variability in aspects of foods other than their basic macronutrient and micronutrient content. Specifically, current instruments for collecting individual dietary data do not capture the provenance of foods or their preparation, both of which would likely influence certain compositional aspects of the foods, especially the microbes on those foods.

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 script. 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 ( Langille et al., 2013 ). 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 ( Parks et al., 2014 ). We used STAMP to test for and visualize significant (predicted) functional differences in microbial communities between the three dietary patterns.

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 at http://nbviewer.ipython.org/gist/jennomics/c6fe5e113525c6aa8add . To explore the differences in overall microbial community composition across the 15 meals, both the phylogenetic weighted UniFrac distances ( Lozupone et al., 2011 ) and the taxonomic Bray–Curtis dissimilarities ( Bray & Curtis, 1957 ) were calculated using the beta_diversity_through_plots.py script. This script also produced a principal coordinates analysis (PCoA) plot in which the Bray–Curtis dissimilarities between samples were used to visualize differences among groups of samples (see Fig. 1 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 ( Kruskal & Allen Wallis, 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. We also used the biplot function of the make_emperor.py script to plot the family-level OTUs in PCoA space alongside each meal. 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 ( Pearson, 1895 ) were calculated and tested for statistical significance using Stata (Stata Statistical Software Release 13; StataCorp, College Station, TX). Figures 2 and 3 were produced with R ( R-project, 2014 ), using the phyloseq package ( McMurdie & Holmes, 2013 ).

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 and a detailed PCR protocol are provided in Table 2 and in a GitHub repository ( https://github.com/hollybik/protocols/blob/master/16S_rRNA_twostep_PCR.tex ), respectively. Libraries were sequenced using an Illumina MiSeq system, generating 250bp paired-end amplicon reads. The amplicon data was multiplexed using dual barcode combinations for each sample. We used a custom script (available in a GitHub repository ( https://github.com/gjospin/scripts/blob/master/Demul_trim_prep.pl ), 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. The paired reads were then aligned and a consensus was computed using FLASH ( Magoč & Salzberg, 2011 ) 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 ( Caporaso et al., 2010 ) in fasta format. The resulting consensus sequences were analyzed using the QIIME pipeline.

Microbial plate counts were performed by Covance Laboratories (Covance Inc., Madison, WI). Aerobic plate counts were performed according to SPCM:7 , anaerobic plate counts were performed according to APCM:5 and the yeast and mold counts were performed according to Chapter 23 of the FDA’s Bacteriological Analytical Manual . Plate counts were reported as colony forming units (CFU) per gram for each meal composite. The CFU/g values were multiplied by the total number of grams in each meal to obtain the CFU per meal, and the values for meals for each day were added to obtain the CFU per day for each dietary pattern ( Table 1 ).

Meal plans were created using the NutriHand program (Nutrihand Inc., Soraya, CA). Diet nutrient composition was calculated by the NutriHand program from reference nutrient data for individual foods using the USDA National Nutrient Database for Standard Reference . Three one-day meal plans were created to be representative of three typical dietary patterns that are consumed by Americans: (1) the Average American dietary pattern (AMERICAN), which includes meat and dairy and focuses on convenience foods, (2) the USDA recommended dietary pattern (USDA), which emphasizes fresh fruits and vegetables, lean meats, whole grains and whole grain products, and dairy, and (3) the Vegan dietary pattern (VEGAN), which excludes all animal products. The AMERICAN meal plan totaled 2,268 calories, which consisted of 35% fat, 53% carbohydrates of which 16.6 g was fiber, and 12% protein. The USDA meal plan totaled 2,260 calories, consisting of 25% fat, 49% carbohydrates of which 45 g was fiber, and 27% protein. The VEGAN meal plan totaled 2,264 calories and consisted of 31% fat, 54% carbohydrates of which 52 g was fiber, and 15% protein.

Diets were designed by a nutritional biologist to deliver the average number of calories consumed by an average American per day. The average American woman is 63 inches in height and weighs 166 pounds, and the average American man is 69 inches in height and weighs 195 pounds with an average age of 35, National Health and Nutrition Examination Survey , which translates to a total daily calorie intake range of 2,000–2,600 calories per day respectively to maintain weight, as determined using the USDA MyPlate SuperTracker tool . Therefore an intermediate daily calorie intake of about 2,200 calories was chosen as the target.

After food preparation, meals were plated on a clean plate, weighed on a digital scale (model 157W; Escali, Minneapolis, MN), and then transferred to a blender (model 5,200; Vita-Mix Corporation, Cleveland, OH) and processed until completely blended (approximately 1–3 min). Prepared, ready to eat foods that were purchased outside the home were simply weighed in their original packaging and then transferred to the blender. 4 mL aliquots of the blended meal composite were extracted from the blender, transported on dry ice and then stored at −80 °C until analysis. The following analyses were completed using these meal composite samples: (1) total aerobic bacterial plate counts, (2) total anaerobic bacterial plate counts, (3) yeast plate counts, (4) fungal plate counts, and (5) 16S rDNA analysis for microbial ecology.

We conducted a series of experiments consisting of food preparation followed by sample preparation and microbial analysis. Food was purchased and prepared in a standard American home kitchen by the same individual using typical kitchen cleaning practices including hand washing with non-antibacterial soap between food preparation steps, washing of dishes and cooking instruments with non-antibacterial dish washing detergent, and kitchen clean-up with a combination of anti-bacterial and non-antibacterial cleaning products. Anti-bacterial products had specific anti-bacterial molecules added to them whereas “non-antibacterial” products were simple surfactant-based formulations. The goal was to simulate a typical home kitchen rather than to artificially introduce sterile practices that would be atypical of how the average American prepares their meals at home. All meals were prepared according to specific recipes (from raw ingredient preparation such as washing and chopping, to cooking and mixing).

Results

Meal composition The detailed meal plans with all ingredients are shown in Table 3, food preparation descriptions (all steps prior to placing into blender and blending foods as described in “Methods” section) are shown in Table 4, and nutrient values based on the USDA nutrient database are shown in Table 5. The AMERICAN meal plan consisted of a large Starbucks Mocha Frapuccino for breakfast, a McDonald’s Big Mac, French fries, and Coca Cola for lunch, Stouffer’s lasagna for dinner, and Oreo cookies for a snack. The USDA meal plan consisted of cereal with milk and raspberries for breakfast, an apple and yogurt for a morning snack, a turkey sandwich on whole wheat bread with salad (including a hard-boiled egg, grapes, parmesan cheese, and Ceasar dressing) for lunch, carrots, cottage cheese and chocolate chips for an afternoon snack, and chicken, asparagus, peas and spinach on quinoa for dinner. The VEGAN meal plan consisted of oatmeal with banana, peanut butter, and almond milk for breakfast, a protein shake (including vegetable-based protein powder, soy milk, banana and blueberries) for a morning snack, a vegetable and tofu soup (including soba noodles, spinach, carrots, celery and onions in vegetable broth) for lunch, an apple and almonds with tea for an afternoon snack, a Portobello mushroom burger (including Portobello mushroom, avocado, tomato, lettuce, and a whole wheat bun) with steamed broccoli for dinner, and popcorn, hazelnuts and fig bars for an evening snack. Average American USDA Vegan Amount Item Amount Item Amount Item Breakfast 20 oz (566 g) Starbucks

Mocha Frappucino 2 cups (88 g) Kashi

GoLean cereal 0.5 each (60 g) large banana 1 cup (232 g) 1% milk 1 cup (250 g) Almond Breeze

almond milk 0.5 cup (58 g) raspberries 2 tsp (14 g) maple syrup 1 tbsp (28 g) peanut butter 0.5 cup (46 g) rolled oats Lunch 1 each (215 g) McDonald’s Big Mac 2 tbsp (26 g) Cesar dressing 6 oz (171 g) firm tofu 1 large (154 g) McDonald’s French Fries 20 each (125 g) green seedless grapes 2 oz (57 g) soba noodles 12 fl oz (380 g) McDonald’s Coke 1 each (78 g) Oroweat whole wheat

burger bun 1 cup (28 g) spinach 3 cups (72 g) green leaf lettuce 1 each (71 g) medium carrot 1 each (52 g) large hard boiled egg 2 cups (480 g) Pacific Foods

vegetable broth 3 tbsp (18 g) parmesan cheese, shredded 1 stalk (56 g) medium celery 2 slices (46 g) roasted turkey breast 0.25 cup (65 g) chopped yellow onion 1 tsp (5 g) extra virgin olive oil 0.25 tsp (2 g) toasted sesame oil Dinner 2 slices (515 g) Stouffer’s Lasagna 1 tbsp (12 g) extra virgin olive oil 0.25 each (38 g) avocado 1 cup (171 g) quinoa 1 each (159 g) portabella mushroom 0.33 cup (35 g) diced yellow onion 1 tbsp (14 g) balsamic vinegar 4 each (65 g) medium asparagus spears 1 tbsp (14 g) Vegenaise 0.5 cup (72 g) frozen green peas 1 slice (57 g) tomato 6 oz (165 g) boneless skinless

chicken poached 1 leaf (14 g) red lettuce 0.5 cup (13 g) spinach 1 cup (80 g) chopped broccoli 1 tsp (0.5 g) lemon juice 1 tsp (0.5 g) lemon juice 1.5 cup (435 g) water 1 clove (0.5 g) garlic 1 tbsp chopped basil 1 bun Oroweat whole wheat

burger bun Snack #1 1 each small Fuji apple 0.5 each large banana 6 oz Yoplait strawberry yogurt 1 cup soy milk 1 scoop Spirutein protein powder 1 cup blueberries (Chile) Snack #2 3 each Oreo cookies 10 each large baby carrots 1 bag green tea 1 cup 2% cottage cheese 1 cup water 2 tbsp semi-sweet chocolate chips 1 each medium Fuji apple 20 each almonds Snack #3 2 cups pop corn 17 each hazelnuts 3 each Newman’s Own fig bars DOI: 10.7717/peerj.659/table-3 Average American USDA Vegan Breakfast Used as purchased from Starbucks. Cereal poured directly from box into bowl. Milk poured into measuring cup, then into cereal bowl. Raspberries washed first in colander under running water then transferred on top of cereal. Almond milk brought to a boil, then oats added and cooked for 5 min on low heat. Peanut butter and maple syrup measured out then stirred into cooked oats. Banana peeled and sliced into slices on top of cooked oats. Snack #1 Cookies taken out of packaging. Apple washed and sliced, core discarded. Yogurt used as purchased. Soy milk measured into measuring cup, protein powder measured into scoop, banana peeled and cut in half, blueberries rinsed in colander under running water. Lunch Used as purchased from McDonald’s. Sliced roasted turkey breast deli meat taken out of packaging and placed into burger bun. Lettuce rinsed in colander under running water and dried on paper towel then cut into strips and tossed with premade Ceasar dressing. Egg boiled in water for 8 min then peeled and sliced in half and placed in top of dressed salad. Parmesan cheese shredded and added on top of salad. Grapes rinsed in colander under running water, then sliced in half and placed on top of salad. Carrot rinsed under running water, peeled, and sliced. Celery washed under running water and sliced. Onion outer layer peeled and diced. Sliced carrot, celery and onion sauteed in olive oil for 5 min, then vegetable broth measured out in measuring cup and added to vegetables, brought to a boil. Tofu taken out of packaging, excess water discarded, cut into cubes, added to broth. Spinach taken out of prepackaged, prerinsed bag and added to broth. Noodles and sesame oil added to broth. Soup cooked for 8 min on low heat. Snack #2 Baby carrots taken out of packaging and used. Cottage cheese measured out in measuring cup. Chocolate chips measured out and used. Water boiled and poured into cup with tea bag, steeped for 5 min. Apple rinsed under running water, sliced, and core discarded. Almonds taken out of packaging. Dinner Lasagna prepared according to manufacturer instructions (taken out of freezer and baked at 400F for 1 h and 45 min, cooled, then sliced. Chicken breast taken out of plastic packaging, and placed into pot with boiling water, boiled for 3 min, removed from heat, covered, let stand for 18 min, then sliced. Quinoa rinsed in colander under running water, added to water in pan and brought to a boil, simmered covered for 20 min. Oil heated in large skillet over medium heat, onion peeled and diced, asparagus spears rinsed and sliced, both added to oil and cooked for 5 min. Peas added from frozen packaging and cooked for 1 min. Spinach rinsed in colander under running water and added to skillet, cooked for 3 min. Quinoa, vegetables, and chicken combined with lemon juice. Mushroom destemmed and peeled, soaked in vinegar, then grilled in grill pan for 5 min on each side. Garlic peeled and grated into Vegenaise, lemon juice added. Basil rinsed under running water, chopped and added to Vegenaise mixture. Tomato rinsed under running water, then sliced. Lettuce leaf rinsed under running water. Broccoli rinsed under running water, then steamed in colander for 3 min, chopped. Burger assembled: Vegenaise mixture spread onto bottom of bun, topped with mushroom, lettuce leaf, tomato slice and top of bun. Snack #3 Popcorn (no salt, no oil) prepared in microwave bag as directed (placed in microwave for 4 min). Hazelnuts taken out of packaging. Fig bars taken out of packaging. DOI: 10.7717/peerj.659/table-4 Dietary

pattern/meal Energy

(kcal) Protein

(g) Total lipid

(fat) (g) Carbohydrate,

by difference

(g) Fiber, total

dietary (g) Sugars,

total (g) Calcium,

Ca (mg) Iron,

Fe (mg) AMERICAN breakfast 367 8.52 4.98 73.33 0 60 250 1.2 AMERICAN lunch 1,174 31.76 57.58 138.6 10 43.53 280 5.7 AMERICAN dinner 568 26.62 20.81 68.2 5.6 11.4 380 2.88 AMERICAN snack 160 1 7 25 1 14 20 1.8 USDA breakfast 414 34.96 4.85 79.52 24 27.41 466 4.09 USDA snack #1 256 8.91 3.07 52.29 3.6 20.45 268 0.3 USDA lunch 656 37.26 28.97 68.61 6 19.06 298 6.06 USDA snack #2 352 29.2 11.74 34.13 5.8 27.43 254 2.22 USDA dinner 581 48.44 19.22 56.41 5.6 6.97 58 3 VEGAN breakfast 367 10 12.78 58.19 6.7 28.31 311 0.9 VEGAN snack #1 373 23.76 4.95 62.8 7.7 40.46 373 6.65 VEGAN lunch 468 25.49 13.89 64.31 7 7.95 348 6.94 VEGAN snack #2 233 5.82 12.59 30.39 7.3 19.86 82 1.11 VEGAN dinner 444 15.4 20.19 55.62 16.3 13.74 176 3.5 VEGAN snack #3 378 7.41 18.69 50.46 6.8 23.42 59 3.02 Dietary

pattern/meal Sodium,

Na (mg) Vitamin C,

total ascorbic

acid (mg) Cholesterol

(mg) Carotene,

beta (mcg) Sucrose

(g) Glucose

(dextrose)

(g) Fructose

(g) Lactose

(g) AMERICAN breakfast 300 0 17 0 N/A N/A N/A N/A AMERICAN lunch 1,399 12.1 79 0 1.27 2.28 3.7 0.7 AMERICAN dinner 2,102 7.2 56 N/A N/A N/A N/A N/A AMERICAN snack 160 0 0 N/A N/A N/A N/A N/A USDA breakfast 278 16.1 12 61 0.12 1.14 1.45 12.69 USDA snack #1 100 8 10 47 3.08 3.62 8.79 0 USDA lunch 1,516 110 223 56 0.15 7.2 8.13 0 USDA snack #2 863 3.9 23 9,600 4.08 3.3 1.5 6.55 USDA dinner 446 25.6 90 1,953 3.78 1.58 1.56 0 VEGAN breakfast 312 6 85 18 3.14 8.49 9.03 0 VEGAN snack #1 266 80 0 69 1.79 10.47 10.51 0 VEGAN lunch 1,618 16.2 0 6,850 2.65 1.4 1.11 0 VEGAN snack #2 9 8.4 0 50 4.63 4.45 10.76 0 VEGAN dinner 499 102.3 0 1,018 0.11 6.96 3.23 0.19 VEGAN snack #3 169 1.6 0 17 1.12 0.03 0.03 0 Dietary

pattern/meal Maltose

(g) Galactose

(g) Starch

(g) Fatty acids, total

monounsaturated

(g) Fatty acids, total

polyunsaturated

(g) Vitamin A,

IU (IU) Fatty acids, total

saturated (g) AMERICAN breakfast N/A N/A N/A N/A N/A 0 2.64 AMERICAN lunch 1.07 0 90.32 19.63 7.86 412 11.52 AMERICAN dinner N/A N/A N/A N/A N/A 600 11.4 AMERICAN snack N/A N/A N/A N/A N/A 0 2 USDA breakfast 0 0 0 0.72 0.33 499 1.97 USDA snack #1 0 0 0.07 1.16 0.55 836 2.06 USDA lunch 0 0 0 7.71 11.43 5,179 7.29 USDA snack #2 0 0 0 1.01 0.27 20,852 5.99 USDA dinner 0.05 0.02 2.79 9.88 1.61 3,271 1.97 VEGAN breakfast 0.21 0.43 4.43 3.9 2.32 544 2.25 VEGAN snack #1 0.01 0 3.7 1.06 2.6 5,129 0.62 VEGAN lunch 0 0.29 0.87 5.9 5.82 13,184 1.79 VEGAN snack #2 0.01 0.01 0.27 7.99 3.48 102 1.48 VEGAN dinner 0.19 0.01 0 10.74 5.56 1,779 2.64 VEGAN snack #3 0 0 8.81 12.48 3.51 52 1.69 DOI: 10.7717/peerj.659/table-5 The following meals contained fermented foods that contained live active cultures according to the package and were prepared without heat treatment: USDA meal plan snack #1 (yogurt), lunch (parmesan cheese), and snack #2 (cottage cheese). The following meals contained fermented foods that were cooked as part of meal preparation: VEGAN meal plan lunch (tofu), and AMERICAN meal plan lunch and dinner (cheese). Meal ingredients were purchased at local grocery stores in Saint Helena, CA, and prepared meals were purchased in restaurants in Napa, CA.

Plate counts The aerobic, anaerobic, yeast and mold plate counts are shown in Table 1. The meals ranged in total numbers of microorganisms from CFU to CFU with the aerobic and anaerobic plate counts being among the highest and the yeast and mold plate counts being among the lowest across all meals. The USDA dietary pattern had the highest total microorganisms for the day at CFU mostly due to the higher amounts of anaerobic bacteria in the morning snack (CFU) and higher amounts of aerobic and anaerobic bacteria in the afternoon snack (5.5 × 108 and 6 × 108 CFU respectively). Not surprisingly, both of these meals contained fermented products, in the first case yogurt, and in the second case cottage cheese. The AMERICAN and VEGAN dietary patterns had 3 orders of magnitude fewer total microorganisms than the USDA dietary patterns, with total microorganisms of CFU and CFU respectively. Neither the AMERICAN nor the VEGAN dietary pattern meals contained fermented foods that were not heat treated as part of meal preparation. The AMERICAN lunch and dinner contained cheese that was either cooked on a grill or baked in the oven and the VEGAN lunch contained tofu, which was cooked in the vegetable broth. The USDA lunch also had the highest amounts of yeast and mold (and CFU respectively) of all the meals, and this meal also had relatively high amounts of aerobic bacteria (CFU). In the VEGAN dietary pattern, the morning snack had the highest amounts of aerobic and anaerobic bacteria (and CFU respectively).

Sequence processing and summary statistics The number of high-quality sequences per sample (i.e., meal) ranged from 168,669 to 318,956 (see Table 6). Sequences were clustered and clusters were assigned to a taxonomic group (when possible) using the pick_open_reference_otus.py script with a 97% similarity cutoff and the gg_13_8_otus reference taxonomy provided by the Greengenes Database Consortium (http://greengenes.secondgenome.com). After OTU assignment, mitochondrial and chloroplast sequences were filtered out, sequences that were observed only once across all samples were removed, and sequences that were Unassigned at the Domain taxonomic level were removed (these Unassigned sequences were verified via a manual BLAST search to be chloroplast sequences). After this filtration, the range of sequences per sample decreased to 771–244,597. All subsequent beta diversity analyses (comparisons across samples) were performed on samples that were rarefied to 771 sequences per sample. Meal # Sequences

pre-filtration # Sequences

post-filtration # OTUs (open reference,

97% similarity) AMERICAN breakfast 267,254 226,903 1,838 AMERICAN dinner 298,442 11,666 660 AMERICAN lunch 299,035 96,898 622 AMERICAN snack 311,311 279,136 969 USDA breakfast 318,956 5,002 502 USDA dinner 277,213 6,149 476 USDA lunch 270,166 16,456 607 USDA snack1 299,998 226,403 334 USDA snack2 238,057 104,114 333 VEGAN breakfast 274,360 7,310 399 VEGAN dinner 303,246 3,576 417 VEGAN lunch 291,459 13,874 480 VEGAN snack1 244,886 62,446 644 VEGAN snack2 288,319 974 1,053 VEGAN snack3 168,669 54,483 229 DOI: 10.7717/peerj.659/table-6

Taxonomic composition and diversity of the different dietary patterns In terms of taxonomic alpha diversity, there was no significant difference between dietary patterns (Fig. 2) (non-parametric Kruskal–Wallis test with compare_alpha_diversity.py, p > 0.6). This is the case for multiple diversity metrics, including a count of the absolute number of OTUs observed, as well as the Chao1 and Shannon–Weiner (parametric and nonparametric, respectively) diversity indices, which account for the relative abundance (evenness) of the OTUs observed. We also tested for the significant variation in frequency of individual OTUs between diet types using the Kruskal–Wallis test, as implemented in the group_significance.py script. This test is appropriate for comparing independent groups, with unequal sample sizes, that may not be normally distributed. None of the OTUs were significantly different between the three diet types. The most abundant 50 OTUs (clustered at 97% similarity) belong to 25 different bacterial families, including many that are commonly found in association with plants and animals (see Fig. 3). Figure 2: Alpha diversity measures for the three diet types. While some individual meals had higher alpha diversity (defined either by the number of OTUs observed or by the Chao1 and Shannon diversity measures) than others, there was no significant difference in diversity between the different dietary patterns (AMERICAN, USDA, and VEGAN). Figure 3: The cumulative relative abundance of Families representing the 50 most abundant OTUs. The 50 most abundant OTUs in this study (clustered at 97% similarity) belong to 25 different bacterial families, including many that are commonly found in association with plants and animals. None of them vary significantly with respect to diet type.