We have selected to characterize the micro-biota 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: the Average American dietary pattern focused on convenience foods, the USDA recommended dietary pattern emphasizing fruits and vegetables, lean meat, dairy, and whole grains, and the 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. 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 .
After food preparation,nft hydroponic meals were plated on a clean plate, weighed on a digital scale , and then transferred to a blender and processed until completely blended . 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: total aerobic bacterial plate counts, total anaerobic bacterial plate counts, yeast plate counts, fungal plate counts, and 16S rDNA analysis for microbial ecology.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. Meal plans were created using the NutriHand program . 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: the Average American dietary pattern , which includes meat and dairy and focuses on convenience foods, the USDA recommended dietary pattern , which emphasizes fresh fruits and vegetables, lean meats, whole grains and whole grain products, and dairy, and the Vegan dietary pattern , 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.Microbial plate counts were performed by Covance Laboratories . 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 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 . The taxonomic composition of each meal micro-biome 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 according to the manufacturer’s protocol. Microbial DNA was amplified by a two-step PCR enrichment of the 16S rRNA gene using primers515F 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 , 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 , 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 with maximum overlap of 120 and a minimum overlap of 70 .
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 in fasta format. The resulting consensus sequences were analyzed using the QIIME pipeline. 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. To explore the differences in overall microbial community composition across the 15 meals, both the phylogenetic weighted UniFrac distances and the taxonomic Bray–Curtis dissimilarities were calculated using the beta diversity through plots.py script. This script also produced a principal coordinates analysis plot in which the Bray–Curtis dissimilarities between samples were used to visualize differences among groups of samples 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 with the FDR 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 at 5 taxonomic levels Pearson correlation coefficients were calculated and tested for statistical significance using Stata . Figures 2 and 3 were produced with R , using the phyloseq package .A synthetic metagenome was generated based on the observed 16S rDNA sequences for each meal. To do this,hydroponic gutter 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 . 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 . We used STAMP to test for and visualize significant functional differences in microbial communities between the three dietary patterns. The detailed meal plans with all ingredients are shown in Table 3, food preparation descriptions 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 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 for a morning snack, a vegetable and tofu soup for lunch, an apple and almonds with tea for an afternoon snack, a Portobello mushroom burger with steamed broccoli for dinner, and popcorn, hazelnuts and fig bars for an evening snack.
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 , lunch , and snack #2 . The following meals contained fermented foods that were cooked as part of meal preparation: VEGAN meal plan lunch , and AMERICAN meal plan lunch and dinner . Meal ingredients were purchased at local grocery stores in Saint Helena, CA, and prepared meals were purchased in restaurants in Napa, CA.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 and higher amounts of aerobic and anaerobic bacteria in the afternoon snack . 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 of all the meals, and this meal also had relatively high amounts of aerobic bacteria . In the VEGAN dietary pattern, the morning snack had the highest amounts of aerobic and anaerobic bacteria . In terms of taxonomic alpha diversity, there was no significant difference between dietary patterns . 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 diversity indices, which account for the relative abundance 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. The most abundant 50 OTUs belong to 25 different bacterial families, including many that are commonly found in association with plants and animals . There was no effect of dietary pattern on the overall community composition within individual meals . There was no obvious clustering based on any potentially distinguishing feature tested, including whether the meals contained fermented foods, dairy, whether they ware raw or cooked, or the calculated nutritional content . However, different meals clustered together independent of dietary pattern. For example, meals that were relatively abundant in Prevotellaceae included the USDA dinner, VEGAN dinner, AMERICAN dinner, USDA breakfast, VEGAN snack 2, and VEGAN snack 3. Prevotellaceae includes organisms that tend to be very abundant in the guts of many animals, and have been associated with Inflammatory Bowel Disease in humans . The AMERICAN snack, AMERICAN lunch, USDA snack 2 and USDA snack 1 had a high relative abundance of Streptococcaceae . It is difficult to know what specific features of these meals made them similar in this regard. Possible contributing factors may be provenance of ingredients and/or individual meal components such as presence of a certain fruit or vegetable.