BackgroundUntargeted metabolomics of host-associated samples has yielded insights into mechanisms by which microbes modulate health. However, data interpretation is challenged by the complexity of origins of the small molecules measured, which can come from the host, microbes that live within the host, or from other exposures such as diet or the environment.ResultsWe address this challenge through development of AMON: Annotation of Metabolite Origins via Networks. AMON is an open-source bioinformatics application that can be used to annotate which compounds in the metabolome could have been produced by bacteria present or the host, to evaluate pathway enrichment of host verses microbial metabolites, and to visualize which compounds may have been produced by host versus microbial enzymes in KEGG pathway maps.ConclusionsAMON empowers researchers to predict origins of metabolites via genomic information and to visualize potential host:microbe interplay. Additionally, the evaluation of enrichment of pathway metabolites of host versus microbial origin gives insight into the metabolic functionality that a microbial community adds to a host:microbe system. Through integrated analysis of microbiome and metabolome data, mechanistic relationships between microbial communities and host phenotypes can be better understood.

Although health benefits of the Dietary Approaches to Stop Hypertension (DASH) diet are established, it is not understood which food compounds result in these benefits. We used metabolomics to identify unique compounds from individual foods of a DASH-style diet and determined if these Food-Specific Compounds (FSC) are detectable in urine from participants in a DASH-style dietary study. We also examined relationships between urinary compounds and blood pressure (BP). Nineteen subjects were randomized into 6-week controlled DASH-style diet interventions. Mass spectrometry-based metabolomics was performed on 24-hour urine samples collected before and after each intervention and on 12 representative DASH-style foods. Between 66-969 compounds were catalogued as FSC; for example, 4-hydroxydiphenylamine was found to be unique to apple. Overall, 13-190 of these FSC were detected in urine, demonstrating that these unmetabolized food compounds can be discovered in urine using metabolomics. Although linear mixed effects models showed no FSC from the 12 profiled foods were significantly associated with BP, other endogenous and food-related compounds were associated with BP (N = 16) and changes in BP over time (N = 6). Overall, this proof of principle study demonstrates that metabolomics can be used to catalog FSC, which can be detected in participant urine following a dietary intervention. Human nutrition research includes controlled-feeding strategies to evaluate associations between consumption of specific foods or diets and health indicators. Recent advances in metabolomics make it possible to gather data on a multitude of foods and biosamples 1-4. Nutrimetabolomics, which represents the intersection of metabolomics and nutrition research, offers an opportunity to investigate the effects of whole diets, specific foods, and food components on the human metabolome 5. For example, Rebholz, et al. applied metabolomics to identify serum markers of participant adherence to consuming a Dietary Approaches to Stop Hypertension (DASH) diet 3. A novel aspect of the Rebholz, et al. study was their effort to define a panel of markers indicative of a DASH-style eating pattern. Similarly, Gordon-Dseagu, et al. used metabolomics to explore the relationship between plasma markers, sleep, and a DASH-style diet 6. These, and other studies 2,7,8 , support the proof-of-principle that metabolomics can discover and link biomarkers of food intake, from both whole diets and individual foods, to health outcomes. Controlled-feeding studies are essential for understanding how diets, individual foods, and food constituents are related to indices of human health. However, the complexity of diets, limited understanding of chemical compositions of foods, shortage of food-specific biomarkers, and personalized nature of human metabolism limit

Identifying and annotating the molecular composition of individual foods will improve scientific understanding of how foods impact human health and how much variation exists in the molecular composition of foods of the same species. The complexity of this task includes distinct varieties and variations in natural occurring pigments of foods. Lipidomics, a sub-field of metabolomics, has emerged as an effective tool to help decipher the molecular composition of foods. For this proof-of-principle research, we determined the lipidomic profiles of green, yellow and red bell peppers (Capsicum annuum) using liquid chromatography mass spectrometry and a novel tool for automated annotation of compounds following database searches. Among 23 samples analyzed from 6 peppers (2 green, 1 yellow, and 3 red), over 8000 lipid compounds were detected with 315 compounds (106 annotated) found in all three colors. Assessments of relationships between these compounds and pepper color, using linear mixed effects regression and false discovery rate (<0.05) statistical adjustment, revealed 11 compounds differing by color. The compound most strongly associated with color was the carotenoid, β-cryptoxanthin (p-value = 7.4 × 10−5; FDR adjusted p-value = 0.0080). These results support lipidomics as a viable analytical technique to identify molecular compounds that can be used for unique characterization of foods.

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