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Cited by 143Metabolomics and Mass Spectrometry StudiesGut microbiota and healthNutrition, Genetics, and Disease

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Human metabolome variation along the upper intestinal tract

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Folz, Jacob, Culver, Rebecca Neal, Morales, Juan Montes, Grembi, Jessica, Triadafilopoulos, George, Relman, David A., Huang, Kerwyn Casey, Shalon, Dari, Fiehn, Oliver·2023·Nature Metabolism
DOI 10.1038/s42255-023-00777-zPMC PMC10229427

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Abstract

Most processing of the human diet occurs in the small intestine. Metabolites in the small intestine originate from host secretions, plus the ingested exposome 1 and microbial transformations. Here we probe the spatiotemporal variation of upper intestinal luminal contents during routine daily digestion in 15 healthy male and female participants. For this, we use a non-invasive, ingestible sampling device to collect and analyse 274 intestinal samples and 60 corresponding stool homogenates by combining five mass spectrometry assays 2,3 and 16S rRNA sequencing. We identify 1,909 metabolites, including sulfonolipids and fatty acid esters of hydroxy fatty acids (FAHFA) lipids. We observe that stool and intestinal metabolomes differ dramatically. Food metabolites display trends in dietary biomarkers, unexpected increases in dicarboxylic acids along the intestinal tract and a positive association between luminal keto acids and fruit intake. Diet-derived and microbially linked metabolites account for the largest inter-individual differences. Notably, two individuals who had taken antibiotics within 6 months before sampling show large variation in levels of bioactive FAHFAs and sulfonolipids and other microbially related metabolites. From inter-individual variation, we identify Blautia species as a candidate to be involved in FAHFA metabolism. In conclusion, non-invasive, in vivo sampling of the human small intestine and ascending colon under physiological conditions reveals links between diet, host and microbial metabolism.

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Fig. 1 Comparison of proximal and distal upper intestinal metabolite levels reveals significant differences in a wide range of compounds. a, Study design for upper intestinal tract investigation. Four types of an intestinal sampling device were used to sample the proximal to distal upper intestines. Fifteen human participants swallowed at least 16 devices over 2 d after lunch and after dinner after an initial test on day 1. Devices were retrieved and analysed by targeted and non-targeted LC–MS/MS and GC–MS methods. b, Identified metabolites from the five metabolome assays used to analyse samples. Chemical class fractions are included based on automated ClassyFire chemical classification. c, Significance of differences between upper intestinal tract regions was calculated using LMM. Horizontal dashed-dotted line represents the significance threshold P < 0.05 (n = 1,182 metabolites). Circles indicate non-significance and diamond shapes indicate significance (P < 0.05) after FDR correction. Only metabolites detected in >50% of intestinal samples were included in this analysis (n = 1,182). Effect size coefficient is the slope estimated by LMM, with positive (negative) coefficient indicating higher (lower) levels in the distal compared to proximal upper intestine. Vertical dashed-dotted lines are ±0.2 effect size coefficient.
Fig. 1 Comparison of proximal and distal upper intestinal metabolite levels reveals significant differences in a wide range of compounds. a, Study design for upper intestinal tract investigation. Four types of an intestinal sampling device were used to sample the proximal to distal upper intestines. Fifteen human participants swallowed at least 16 devices over 2 d after lunch and after dinner after an initial test on day 1. Devices were retrieved and analysed by targeted and non-targeted LC–MS/MS and GC–MS methods. b, Identified metabolites from the five metabolome assays used to analyse samples. Chemical class fractions are included based on automated ClassyFire chemical classification. c, Significance of differences between upper intestinal tract regions was calculated using LMM. Horizontal dashed-dotted line represents the significance threshold P < 0.05 (n = 1,182 metabolites). Circles indicate non-significance and diamond shapes indicate significance (P < 0.05) after FDR correction. Only metabolites detected in >50% of intestinal samples were included in this analysis (n = 1,182). Effect size coefficient is the slope estimated by LMM, with positive (negative) coefficient indicating higher (lower) levels in the distal compared to proximal upper intestine. Vertical dashed-dotted lines are ±0.2 effect size coefficient.CC BY · PubMed Central
Fig. 2 Intestinal metabolite association with food types. a,b,d, Volcano plots show significance of each metabolite to food types of fruit (a), alcohol (b) and dessert (d) calculated by LMM. Consumption is defined as food eaten within 6 h of swallowing sample devices. Significance of P < 0.05 (n = 1,182 metabolites) is delimited by the lower dashed-dotted horizontal line. Circles indicate non-significance after FDR correction and diamonds indicate significance (P < 0.05) after FDR correction (n = 1,182). Metabolites detected in >50% of intestinal samples were included in this analysis. Effect size coefficient is the slope estimate calculated by LMM, with positive (negative) coefficient meaning the metabolite was higher (lower) after food consumption. Vertical dashed-dotted lines are ±0.2 effect size coefficient. c, Chemical enrichment statistics (ChemRICH) analysis revealed significant chemical classes after fruit consumption visualized by separating classes by chemical lipophilicity (logP) and chemical class significance level of −log10(P). Red circles indicate that the chemical class increased after fruit consumption and blue circle indicates that the chemical class decreased after fruit consumption. Circle size indicates the size of the chemical class. e, Theophylline and theobromine levels are strongly associated with caffeine levels. Circles represent measured levels in each sample for which both metabolites were detected. f, Chemical diagram of caffeine and known metabolic pathways with structures of detected metabolites and Spearman rank correlation coefficient (rs) for each structure (P < 1.0 × 10−13 for all metabolites; n = 1,182 metabolites).
Fig. 2 Intestinal metabolite association with food types. a,b,d, Volcano plots show significance of each metabolite to food types of fruit (a), alcohol (b) and dessert (d) calculated by LMM. Consumption is defined as food eaten within 6 h of swallowing sample devices. Significance of P < 0.05 (n = 1,182 metabolites) is delimited by the lower dashed-dotted horizontal line. Circles indicate non-significance after FDR correction and diamonds indicate significance (P < 0.05) after FDR correction (n = 1,182). Metabolites detected in >50% of intestinal samples were included in this analysis. Effect size coefficient is the slope estimate calculated by LMM, with positive (negative) coefficient meaning the metabolite was higher (lower) after food consumption. Vertical dashed-dotted lines are ±0.2 effect size coefficient. c, Chemical enrichment statistics (ChemRICH) analysis revealed significant chemical classes after fruit consumption visualized by separating classes by chemical lipophilicity (logP) and chemical class significance level of −log10(P). Red circles indicate that the chemical class increased after fruit consumption and blue circle indicates that the chemical class decreased after fruit consumption. Circle size indicates the size of the chemical class. e, Theophylline and theobromine levels are strongly associated with caffeine levels. Circles represent measured levels in each sample for which both metabolites were detected. f, Chemical diagram of caffeine and known metabolic pathways with structures of detected metabolites and Spearman rank correlation coefficient (rs) for each structure (P < 1.0 × 10−13 for all metabolites; n = 1,182 metabolites).CC BY · PubMed Central
Fig. 3 Heat map of metabolites with strong inter-participant differences. Metabolites include bile pigments, fatty acid esters of hydroxy fatty acids (FAHFAs and AAHFAs), SCFAs, sulfonolipids (SLs) and secondary bile acids. Samples are organized by participant and antibiotic consumption is indicated for the two participants who consumed antibiotics 1 and 5 months before this study. Colour of heat map ranges from low (blue) to high (red) of metabolite abundance (peak height) or concentration (in nanograms per millilitre) for bile acids. Minimum and maximum values are used to set colour scale for each metabolite (each row).
Fig. 3 Heat map of metabolites with strong inter-participant differences. Metabolites include bile pigments, fatty acid esters of hydroxy fatty acids (FAHFAs and AAHFAs), SCFAs, sulfonolipids (SLs) and secondary bile acids. Samples are organized by participant and antibiotic consumption is indicated for the two participants who consumed antibiotics 1 and 5 months before this study. Colour of heat map ranges from low (blue) to high (red) of metabolite abundance (peak height) or concentration (in nanograms per millilitre) for bile acids. Minimum and maximum values are used to set colour scale for each metabolite (each row).CC BY · PubMed Central
Extended Data Fig. 1 Summary visualizations. a, Sample pH for intestinal sampling device types. Boxes represent ±1 interquartile range, and the line extends to the sample furthest from the median sample with a maximum extension of 1.5 interquartile ranges away from the median. All samples are plotted as individual points. (n = 274 total devices, Device type 1 = 75, Device type 2 = 70, Device type 3 = 69, Device type 4 = 60) ****: p < 0.0001 (Wilcoxon signed-rank). b, Principal Component Analysis (PCA) of normalized peak heights from the metabolomics dataset. QC samples are pooled intestinal contents from human intestinal tract samples.
Extended Data Fig. 1 Summary visualizations. a, Sample pH for intestinal sampling device types. Boxes represent ±1 interquartile range, and the line extends to the sample furthest from the median sample with a maximum extension of 1.5 interquartile ranges away from the median. All samples are plotted as individual points. (n = 274 total devices, Device type 1 = 75, Device type 2 = 70, Device type 3 = 69, Device type 4 = 60) ****: p < 0.0001 (Wilcoxon signed-rank). b, Principal Component Analysis (PCA) of normalized peak heights from the metabolomics dataset. QC samples are pooled intestinal contents from human intestinal tract samples.CC BY · PubMed Central
Extended Data Fig. 2 The 80 metabolites most important for distinguishing intestinal device samples from stool samples as determined by partial least square discriminant analysis (PLS-DA). Samples and metabolites are organized on the x-axis and y-axis, respectively, by hierarchical clustering. Each sample is labeled by subject number and sample type (device type or stool). Color represents metabolite abundance, and the minimum and maximum values are set individually for each metabolite (row).
Extended Data Fig. 2 The 80 metabolites most important for distinguishing intestinal device samples from stool samples as determined by partial least square discriminant analysis (PLS-DA). Samples and metabolites are organized on the x-axis and y-axis, respectively, by hierarchical clustering. Each sample is labeled by subject number and sample type (device type or stool). Color represents metabolite abundance, and the minimum and maximum values are set individually for each metabolite (row).CC BY · PubMed Central
Extended Data Fig. 3 The top 80 metabolites most important for distinguishing device samples by type as determined by partial least square discriminant analysis (PLS-DA). Samples are organized on the x-axis by device type, and metabolites are organized on the y-axis according to hierarchical clustering. Color represents metabolite abundance, and the minimum and maximum values for color scale are set individually for each metabolite (row).
Extended Data Fig. 3 The top 80 metabolites most important for distinguishing device samples by type as determined by partial least square discriminant analysis (PLS-DA). Samples are organized on the x-axis by device type, and metabolites are organized on the y-axis according to hierarchical clustering. Color represents metabolite abundance, and the minimum and maximum values for color scale are set individually for each metabolite (row).CC BY · PubMed Central
Extended Data Fig. 4 Comparison of levels of unannotated features in the proximal and distal upper intestines. Data from devices 1 + 2 (proximal) compared to data from devices 3+4 (distal). Significance was calculated using a linear mixed effect model (LMM). The horizontal dashed line represents the significance threshold of p < 0.05 (For exact p values see Supplementary Table 4). Circles indicate metabolites with non-significant differences after false discovery rate (FDR) correction or an effect size coefficient < ± 0.2. Diamonds indicate metabolites with significant (p<0.05) differences after FDR correction (n = 9,317) and effect size coefficient > ± 0.2. Only features detected in >50% of intestinal samples were included in this analysis (n = 9,317 features). Effect size coefficient is the slope estimated by the LMM, with positive (negative) coefficient representing a metabolite that is higher (lower) in the distal compared to proximal upper intestine. Vertical dashed lines are ±0.20 times the effect size coefficient.
Extended Data Fig. 4 Comparison of levels of unannotated features in the proximal and distal upper intestines. Data from devices 1 + 2 (proximal) compared to data from devices 3+4 (distal). Significance was calculated using a linear mixed effect model (LMM). The horizontal dashed line represents the significance threshold of p < 0.05 (For exact p values see Supplementary Table 4). Circles indicate metabolites with non-significant differences after false discovery rate (FDR) correction or an effect size coefficient < ± 0.2. Diamonds indicate metabolites with significant (p<0.05) differences after FDR correction (n = 9,317) and effect size coefficient > ± 0.2. Only features detected in >50% of intestinal samples were included in this analysis (n = 9,317 features). Effect size coefficient is the slope estimated by the LMM, with positive (negative) coefficient representing a metabolite that is higher (lower) in the distal compared to proximal upper intestine. Vertical dashed lines are ±0.20 times the effect size coefficient.CC BY · PubMed Central
Extended Data Fig. 5 Chemical classes that differed significantly between the proximal and distal upper intestine. Chemical enrichment statistics were performed by ChemRICH. Metabolites detected in >50% of intestinal samples were included in this analysis (n = 1182 metabolites). These results were visualized by separating classes by chemical lipophilicity (logP) and chemical class significance level of -log10(p-value). Red circles indicate that the chemical class was higher in the distal compared to proximal upper intestine, and blue indicates that the chemical class was lower in the distal compared to the proximal upper intestine. Purple indicates the chemical cluster has metabolites that are significantly higher as well as metabolites that are significantly lower in the distal compared to proximal upper intestine. Circle size represents the size of the chemical class.
Extended Data Fig. 5 Chemical classes that differed significantly between the proximal and distal upper intestine. Chemical enrichment statistics were performed by ChemRICH. Metabolites detected in >50% of intestinal samples were included in this analysis (n = 1182 metabolites). These results were visualized by separating classes by chemical lipophilicity (logP) and chemical class significance level of -log10(p-value). Red circles indicate that the chemical class was higher in the distal compared to proximal upper intestine, and blue indicates that the chemical class was lower in the distal compared to the proximal upper intestine. Purple indicates the chemical cluster has metabolites that are significantly higher as well as metabolites that are significantly lower in the distal compared to proximal upper intestine. Circle size represents the size of the chemical class.CC BY · PubMed Central
Extended Data Fig. 6 Comparison of temporal and personal variation to spatial variation in the intestinal metabolome. a, The number of metabolites that differed significantly different between intestinal regions (device types) or between meals (sampling time points of four capsule devices) calculated for each subject by analysis of variance (Kruskal-Wallis). Only metabolites detected in >50% of samples for each subject were used for this analysis (n = 1182 metabolites). Non-FDR-corrected p < 0.05 was used as a significance threshold. b, Multivariate discriminant analysis (PLS-DA) was performed to identify metabolites that were most important for distinguishing between subjects, or between regions. The 100 metabolites most important for distinguishing these groups were ranked by variable importance in projection score (VIP) and are categorized by chemical subclass. Chemical subclasses with <3 metabolites are reported as ‘Other’.
Extended Data Fig. 6 Comparison of temporal and personal variation to spatial variation in the intestinal metabolome. a, The number of metabolites that differed significantly different between intestinal regions (device types) or between meals (sampling time points of four capsule devices) calculated for each subject by analysis of variance (Kruskal-Wallis). Only metabolites detected in >50% of samples for each subject were used for this analysis (n = 1182 metabolites). Non-FDR-corrected p < 0.05 was used as a significance threshold. b, Multivariate discriminant analysis (PLS-DA) was performed to identify metabolites that were most important for distinguishing between subjects, or between regions. The 100 metabolites most important for distinguishing these groups were ranked by variable importance in projection score (VIP) and are categorized by chemical subclass. Chemical subclasses with <3 metabolites are reported as ‘Other’.CC BY · PubMed Central
Extended Data Fig. 7 The number of metabolites that were significantly different between intestinal regions (device types) or between meals (sampling time points of sets of 4 devices) were calculated for each subject by analysis of variance (Kruskal-Wallis). Only metabolites detected in >50% of samples for each subject were used for this analysis (n = 1182 metabolites). Non-FDR-corrected p < 0.05 was used as a significance value cutoff. a, Metabolites with significantly different abundance between intestinal regions for each subject, grouped by chemical class and the proportion of each chemical class. b, Metabolites with significantly different abundance between sampling time points, grouped by chemical class and the proportion of each chemical class.
Extended Data Fig. 7 The number of metabolites that were significantly different between intestinal regions (device types) or between meals (sampling time points of sets of 4 devices) were calculated for each subject by analysis of variance (Kruskal-Wallis). Only metabolites detected in >50% of samples for each subject were used for this analysis (n = 1182 metabolites). Non-FDR-corrected p < 0.05 was used as a significance value cutoff. a, Metabolites with significantly different abundance between intestinal regions for each subject, grouped by chemical class and the proportion of each chemical class. b, Metabolites with significantly different abundance between sampling time points, grouped by chemical class and the proportion of each chemical class.CC BY · PubMed Central
Extended Data Fig. 8 Metabolites with strong inter-subject differences. Metabolites include bile pigments, fatty acid esters of hydroxy fatty acids (FAHFAs and AAHFAs), short chain fatty acids, sulfonolipids (SLs), and secondary bile acids. Data from all stool samples are shown, and samples are organized by subject. The two subjects that consumed antibiotics 1 and 5 months prior to this study are highlighted. Color bar represents metabolite abundance (peak height) or concentration (ng/mL) for bile acids. Minimum and maximum values were used to set the color scale for each metabolite (each row).
Extended Data Fig. 8 Metabolites with strong inter-subject differences. Metabolites include bile pigments, fatty acid esters of hydroxy fatty acids (FAHFAs and AAHFAs), short chain fatty acids, sulfonolipids (SLs), and secondary bile acids. Data from all stool samples are shown, and samples are organized by subject. The two subjects that consumed antibiotics 1 and 5 months prior to this study are highlighted. Color bar represents metabolite abundance (peak height) or concentration (ng/mL) for bile acids. Minimum and maximum values were used to set the color scale for each metabolite (each row).CC BY · PubMed Central
Extended Data Fig. 9 FAHFAs, AAHFAs, and fatty acids with strong inter-subject abundance differences. Metabolites are fatty acid esters of hydroxy fatty acids (FAHFAs and AAHFAs) and fatty acids detected >50% of all device samples. All device samples are shown, and are organized by subject. Within the top (FAHFA) and lower (fatty acid) sections, the metabolites are ordered based on hierarchical clustering. Color bar represents metabolite abundance (peak height) or concentration (ng/mL) for bile acids. Minimum and maximum values were used to set the color scale for each metabolite (each row).
Extended Data Fig. 9 FAHFAs, AAHFAs, and fatty acids with strong inter-subject abundance differences. Metabolites are fatty acid esters of hydroxy fatty acids (FAHFAs and AAHFAs) and fatty acids detected >50% of all device samples. All device samples are shown, and are organized by subject. Within the top (FAHFA) and lower (fatty acid) sections, the metabolites are ordered based on hierarchical clustering. Color bar represents metabolite abundance (peak height) or concentration (ng/mL) for bile acids. Minimum and maximum values were used to set the color scale for each metabolite (each row).CC BY · PubMed Central

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  1. Bhutia, Y. D. & Ganapathy, V. in Physiology of the Gastrointestinal Tract, Ch. 47 (Elsevier, 2018).
  2. Arnaud, M. J. in Caffeine, Coffee and Health (ed Garattini, S.) 43–95 (Raven Press, 1993).

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Authors
Folz, J., Culver, R. N., Morales, J. M., Grembi, J., Triadafilopoulos, G., Relman, D. A., Huang, K. C., Shalon, D., & Fiehn, O.
Affiliations
  • University of California, Davis (US)
  • Stanford Medicine (US)
  • VA Palo Alto Health Care System (US)
  • Chan Zuckerberg Biohub San Francisco (US)
Corresponding author
Fiehn, Oliver
ORCID
Venue
Nature Metabolism
Publisher
Springer Science and Business Media LLC
Published
2023-05-10
Volume
5
Issue
5
Pages
777-788
ISSN
2522-5812 (ISSN-L)
DOI
10.1038/s42255-023-00777-z
PMC
PMC10229427
OpenAlex
W4376132993
Cited by
143
References
99
Citations by year
2026: 27 · 2025: 52 · 2024: 47 · 2023: 17
FWCI
12.74
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Metabolism (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
Funders
  • National Science Foundation (1936687)
  • U.S. Department of Health & Human Services | National Institutes of Health (R01 AT010216, U19 AG023122)
  • U.S. Department of Agriculture (2021-67017-35783)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
Keywords
human metabolome, small intestine, hydroxy fatty acid ester, dietary biomarkers, dicarboxylic acids, keto acids, fruit consumption, antibiotic exposure, Blautia, gut microbiota, interindividual variability, exposome, host-microbe interactions
MeSH terms
Fatty Acids, Feces, Female, Humans, Intestine, Small, Male, RNA, Ribosomal, 16S, Metabolome

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