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Cited by 317Gut microbiota and healthFood composition and propertiesMicrobial Metabolites in Food Biotechnology

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Impact of Dietary Resistant Starch on the Human Gut Microbiome, Metaproteome, and Metabolome

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Maier, Tanja V., Lucio, Marianna, Lee, Lang Ho, VerBerkmoes, Nathan C., Brislawn, Colin J., Bernhardt, Jörg, Lamendella, Regina, McDermott, Jason E., Bergeron, Nathalie, Heinzmann, Silke S., Morton, James T., González, Antonio, Ackermann, Gail, Knight, Rob, Riedel, Katharina, Krauss, Ronald M., Schmitt-Kopplin, Philippe, Jansson, Janet K.·2017·mBio
DOI 10.1128/mbio.01343-17PMC PMC5646248
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Abstract

Diet can influence the composition of the human microbiome, and yet relatively few dietary ingredients have been systematically investigated with respect to their impact on the functional potential of the microbiome. Dietary resistant starch (RS) has been shown to have health benefits, but we lack a mechanistic understanding of the metabolic processes that occur in the gut during digestion of RS. Here, we collected samples during a dietary crossover study with diets containing large or small amounts of RS. We determined the impact of RS on the gut microbiome and metabolic pathways in the gut, using a combination of “omics” approaches, including 16S rRNA gene sequencing, metaproteomics, and metabolomics. This multiomics approach captured changes in the abundance of specific bacterial species, proteins, and metabolites after a diet high in resistant starch (HRS), providing key insights into the influence of dietary interventions on the gut microbiome. The combined data showed that a high-RS diet caused an increase in the ratio of Firmicutes to Bacteroidetes, including increases in relative abundances of some specific members of the Firmicutes and concurrent increases in enzymatic pathways and metabolites involved in lipid metabolism in the gut. IMPORTANCE This work was undertaken to obtain a mechanistic understanding of the complex interplay between diet and the microorganisms residing in the intestine. Although it is known that gut microbes play a key role in digestion of the food that we consume, the specific contributions of different microorganisms are not well understood. In addition, the metabolic pathways and resultant products of metabolism during digestion are highly complex. To address these knowledge gaps, we used a combination of molecular approaches to determine the identities of the microorganisms in the gut during digestion of dietary starch as well as the metabolic pathways that they carry out. Together, these data provide a more complete picture of the function of the gut microbiome in digestion, including links between an RS diet and lipid metabolism and novel linkages between specific gut microbes and their metabolites and proteins produced in the gut.

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FIG 1 Map of the microbiome corresponding to dietary resistant starch (RS) levels. Average relative abundances of taxa are based on 16S rRNA gene sequences from all samples: the larger the cells, the greater the overall abundance of operational taxonomic units (OTUs) in that particular taxonomic category averaged across all samples in this study. Maps on top from left to right specify phylum, order, and genus. The map on the bottom visualizes averaged Pearson correlation coefficients from all samples that were calculated from relative OTU abundances versus the approximate amount of RS in each diet: 0 (baseline), 0.05 (LRS), and 1 (HRS).
FIG 1 Map of the microbiome corresponding to dietary resistant starch (RS) levels. Average relative abundances of taxa are based on 16S rRNA gene sequences from all samples: the larger the cells, the greater the overall abundance of operational taxonomic units (OTUs) in that particular taxonomic category averaged across all samples in this study. Maps on top from left to right specify phylum, order, and genus. The map on the bottom visualizes averaged Pearson correlation coefficients from all samples that were calculated from relative OTU abundances versus the approximate amount of RS in each diet: 0 (baseline), 0.05 (LRS), and 1 (HRS).CC BY · PubMed Central
FIG 2 Map of the metaproteome corresponding to dietary resistant starch (RS) levels and assignment of proteins to bacterial phyla. (A) Functional assignments (Clusters of Orthologous Groups [COGs]) of all bacterial proteins across all samples. Cell sizes correspond to averaged protein abundances. (B) Protein functions are shown at a more detailed level. For panels A and B, averaged Pearson correlation coefficients from all individuals were calculated from relative protein abundances versus the approximate amount of RS in each diet: 0 (baseline), 0.05 (LRS), and 1 (HRS). The color scale for the Pearson correlation coefficients is as follows: −1, dark blue, negatively correlated with resistant starch concentration; 0, gray; 1, orange, positively correlated with resistant starch concentration. (C) Examples of specific proteins that significantly differed according to diet (post hoc Kruskal-Nemenyi test; *, P < 0.05; **, P < 0.01; ***, P < 0.001). (D) Bacterial taxa that were assigned to the same proteins as shown in panel B were partitioned and color coded according to bacterial phyla. (E) Correlation of the most common OTUs and corresponding proteins, labeled at the family level and colored at phylum level.
FIG 2 Map of the metaproteome corresponding to dietary resistant starch (RS) levels and assignment of proteins to bacterial phyla. (A) Functional assignments (Clusters of Orthologous Groups [COGs]) of all bacterial proteins across all samples. Cell sizes correspond to averaged protein abundances. (B) Protein functions are shown at a more detailed level. For panels A and B, averaged Pearson correlation coefficients from all individuals were calculated from relative protein abundances versus the approximate amount of RS in each diet: 0 (baseline), 0.05 (LRS), and 1 (HRS). The color scale for the Pearson correlation coefficients is as follows: −1, dark blue, negatively correlated with resistant starch concentration; 0, gray; 1, orange, positively correlated with resistant starch concentration. (C) Examples of specific proteins that significantly differed according to diet (post hoc Kruskal-Nemenyi test; *, P < 0.05; **, P < 0.01; ***, P < 0.001). (D) Bacterial taxa that were assigned to the same proteins as shown in panel B were partitioned and color coded according to bacterial phyla. (E) Correlation of the most common OTUs and corresponding proteins, labeled at the family level and colored at phylum level.CC BY · PubMed Central
FIG 3 Significant differences in fecal metabolite compositions between diets. (A) OPLS-DA score scatter plot comparing baseline diet (blue) versus HRS diet (red); Q²(cum) = 0.8 and R2Y(cum) = 1. (B) OPLS-DA score scatter plot comparing LRS diet (green) with HRS diet (red); Q²(cum) = 0.6 and R2Y(cum) = 0.9. (A and B) t[1] represents the first component; t0[1] expresses the variance orthogonal to the variable Y (class). (C) OPLS-DA loading scatter plot of metabolites assigned to biosynthesis of other secondary metabolites (cyan), lipid metabolism (purple), and metabolism of terpenoids and polyketides (yellow). (D) Alternating main pathways within different diets (HRS versus LRS); Q²(cum) = 0.6 and R2Y(cum) = 0.3. (E) Euclidean distance hierarchical clustering analysis visualizing the different intensity levels of compounds related to lipid metabolism related in specific diet classes.
FIG 3 Significant differences in fecal metabolite compositions between diets. (A) OPLS-DA score scatter plot comparing baseline diet (blue) versus HRS diet (red); Q²(cum) = 0.8 and R2Y(cum) = 1. (B) OPLS-DA score scatter plot comparing LRS diet (green) with HRS diet (red); Q²(cum) = 0.6 and R2Y(cum) = 0.9. (A and B) t[1] represents the first component; t0[1] expresses the variance orthogonal to the variable Y (class). (C) OPLS-DA loading scatter plot of metabolites assigned to biosynthesis of other secondary metabolites (cyan), lipid metabolism (purple), and metabolism of terpenoids and polyketides (yellow). (D) Alternating main pathways within different diets (HRS versus LRS); Q²(cum) = 0.6 and R2Y(cum) = 0.3. (E) Euclidean distance hierarchical clustering analysis visualizing the different intensity levels of compounds related to lipid metabolism related in specific diet classes.CC BY · PubMed Central
FIG 4 Multiomics data integration for different diet categories. (A) Network following HRS diet. Similarities (edges) within and between species, proteins, and metabolites (circles, squares, and triangles, respectively) across participants and time points, including only nodes significantly higher (red) or lower (blue) in HRS than baseline (two-sided t test; P < 0.05). (B) OPLS-DA plot of all data (features: metabolites, 5,552; proteome, 57,397; OTUs, 1,107) for baseline (blue, negative x axis) versus HRS (red, positive x axis); P = 8.3 × 10−6 (CV-ANOVA); R2Y(cum) = 0.96; Q2(cum) = 0.88. (C) OPLS-DA plot for HRS (red, negative x axis) versus LRS (green, positive x axis); P = 0.026 (CV-ANOVA); R2Y(cum) = 0.883; Q2(cum) = 0.534.
FIG 4 Multiomics data integration for different diet categories. (A) Network following HRS diet. Similarities (edges) within and between species, proteins, and metabolites (circles, squares, and triangles, respectively) across participants and time points, including only nodes significantly higher (red) or lower (blue) in HRS than baseline (two-sided t test; P < 0.05). (B) OPLS-DA plot of all data (features: metabolites, 5,552; proteome, 57,397; OTUs, 1,107) for baseline (blue, negative x axis) versus HRS (red, positive x axis); P = 8.3 × 10−6 (CV-ANOVA); R2Y(cum) = 0.96; Q2(cum) = 0.88. (C) OPLS-DA plot for HRS (red, negative x axis) versus LRS (green, positive x axis); P = 0.026 (CV-ANOVA); R2Y(cum) = 0.883; Q2(cum) = 0.534.CC BY · PubMed Central
FIG 5 Overview of detected enzymes, pathways, species, and metabolites that were significantly impacted by a resistant starch diet. Red arrows/frames, increased in HRS; blue frames, decreased in HRS; black arrows, not detected or not increased in HRS over baseline; green arrows/frames, increased in LRS. 1, starch and sucrose metabolism; 2, glycolysis from glucose to pyruvate; 3, 3-oxoacyl-(acyl carrier protein) synthase; 4, acetyl-CoA acetyltransferase; 5, 3-hydroxyacyl-CoA dehydrogenase; 6, enoyl-CoA hydratase; 7, enoyl-(acyl carrier protein) reductase (NADH); 8, acetate CoA-transferase; 9, butyrate kinase; 10, citrate synthase; 11, aconitate hydratase; 12 and 13, isocitrate dehydrogenase; 14, 2-ketoglutarate ferredoxin oxidoreductase; 15, succinyl-CoA synthetase; 16, succinate dehydrogenase/fumarate reductase; 17, fumarate hydratase; 18, malate dehydrogenase; 19, human enzymes.
FIG 5 Overview of detected enzymes, pathways, species, and metabolites that were significantly impacted by a resistant starch diet. Red arrows/frames, increased in HRS; blue frames, decreased in HRS; black arrows, not detected or not increased in HRS over baseline; green arrows/frames, increased in LRS. 1, starch and sucrose metabolism; 2, glycolysis from glucose to pyruvate; 3, 3-oxoacyl-(acyl carrier protein) synthase; 4, acetyl-CoA acetyltransferase; 5, 3-hydroxyacyl-CoA dehydrogenase; 6, enoyl-CoA hydratase; 7, enoyl-(acyl carrier protein) reductase (NADH); 8, acetate CoA-transferase; 9, butyrate kinase; 10, citrate synthase; 11, aconitate hydratase; 12 and 13, isocitrate dehydrogenase; 14, 2-ketoglutarate ferredoxin oxidoreductase; 15, succinyl-CoA synthetase; 16, succinate dehydrogenase/fumarate reductase; 17, fumarate hydratase; 18, malate dehydrogenase; 19, human enzymes.CC BY · PubMed Central

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Metadata

Authors
Maier, T. V., Lucio, M., Lee, L. H., VerBerkmoes, N. C., Brislawn, C. J., Bernhardt, J., Lamendella, R., McDermott, J. E., Bergeron, N., Heinzmann, S. S., Morton, J. T., González, A., Ackermann, G., Knight, R., Riedel, K., Krauss, R. M., Schmitt-Kopplin, P., & Jansson, J. K.
Affiliations
  • Helmholtz Munich (DE)
  • Brigham and Women's Hospital (US)
  • The University of Texas at El Paso (US)
  • Pacific Northwest National Laboratory (US)
  • Universität Greifswald (DE)
  • Juniata College (US)
  • Touro University California (US)
  • University of California San Diego (US)
  • Children’s Hospital Oakland Research Institute, Oakland, California, USA
  • Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Neuherberg, Germany
Corresponding author
Jansson, Janet K.
ORCID
Venue
mBio
Publisher
American Society for Microbiology
Published
2017-11-08
Volume
8
Issue
5
ISSN
  • 2150-7511 (ISSN-L)
  • 2161-2129
DOI
10.1128/mbio.01343-17
PMC
PMC5646248
OpenAlex
W2765206455
Cited by
316
References
61
Citations by year
2026: 18 · 2025: 32 · 2024: 32 · 2023: 33 · 2022: 45 · 2021: 57 · 2020: 39 · 2019: 38 · 2018: 20 · 2017: 1
FWCI
10.94
Type
journal article
Language
en
Open access
Licence
Locations
  • mBio (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion, cc-by)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • Touro Scholar (Touro College) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • Site cant be reached (submittedVersion, cc-by)
  • OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
Funders
  • Ingredion Incorporated
  • HHS | National Institutes of Health (DK086472)
  • DOE | Laboratory Directed Research and Development
  • General Mills
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Health Sciences › Nursing › Nutrition and Dietetics › Food composition and properties
  • Health Sciences › Nursing › Nutrition and Dietetics › Microbial Metabolites in Food Biotechnology
Keywords
resistant starch, gut microbiota, metaproteomics, metabolomics, Firmicutes, Bacteroidetes, lipid metabolism, microbial metabolic pathways, diet-microbiome interactions, gut microbiota metabolites
MeSH terms
Gastrointestinal Microbiome, Bacteria, Diet, DNA, Bacterial, DNA, Ribosomal, Humans, RNA, Ribosomal, 16S, Starch, Sequence Analysis, DNA, Proteome, Proteomics, Metabolomics, Metabolome, Microbiota

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