Abstract
Objectives
The response to a dietary pattern exhibits large interindividual variability—due in part to the uniqueness of the microbiome of each individual. However, specific attributes of the microbiome related to dietary intake may be directly related to the precise dietary pattern. To determine what attributes come solely from a dietary pattern, we must isolate the individual differences. To do this, we aim to establish the feasibility of assessing microbiome attributes related to disparate dietary patterns using pooled samples.
Methods
Samples from 4 self-identified vegans and omnivores were collected, pooled, homogenized and stored at −80°C until time of analysis. Dietary patterns were self-reported using DietID. Whole genome shotgun metagenomics was performed (Replicates: 10/diet, 4/individual). Metabolomic profiles were analyzed using NMR and MS/MS. Genomic and metabolomic data were analyzed to identify diet-specific signatures. As part of a comprehensive literature review, we developed a table predictive of the microbiomes of omnivores versus vegans. We compare our findings to this summary of emerging research to assess potential validity of pooled sample analysis.
Results
Key differences were detected between the vegan and omnivore samples individually and in pools including 10 genera significantly enriched in vegan and 15 in omnivore, e.g., Akkermansia, Bacteroides, Bifidobacterium, Clostridium, Faecalibacterium, Methanobrevibacter, and Ruminococcus. Fold changes were > 2 in 13 of 25 genera (4 vegan, 9 omnivore). Differences were comparable to the predictive table. Ongoing analyses include comparisons at additional taxonomic levels and diversity/richness assessments which will improve the resolution of the aforementioned relationships and the ability to detect them using pooled samples.
Conclusions
Even with a small cohort of pooled samples, we are able to reproduce the findings of the evidence-base to-date—the same variability and stability in microbiome composition.
Funding Sources
National Institute of Standards and Technology (NIST), The International Life Sciences Institute – North America (ILSI-NA).
Classifying Individuals Into a Dietary Pattern Based on Metabolomic Data
