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

Abstract

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 ofFirmicutestoBacteroidetes, including increases in relative abundances of some specific members of theFirmicutesand concurrent increases in enzymatic pathways and metabolites involved in lipid metabolism in the gut. IMPORTANCEThis work was undertaken to obtain a mechanistic understanding of the complex interplay betweenmore » 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.« less

Authors:
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1406729
Report Number(s):
PNNL-SA-126751
Journal ID: ISSN 2150-7511
DOE Contract Number:
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: mBio (Online); Journal Volume: 8; Journal Issue: 5
Country of Publication:
United States
Language:
English
Subject:
Clinical; proteomics; metabolomics; resistant starch; human microbiome; diet

Citation Formats

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., and Moran, Mary Ann. Impact of Dietary Resistant Starch on the Human Gut Microbiome, Metaproteome, and Metabolome. United States: N. p., 2017. Web. doi:10.1128/mBio.01343-17.
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., & Moran, Mary Ann. Impact of Dietary Resistant Starch on the Human Gut Microbiome, Metaproteome, and Metabolome. United States. doi:10.1128/mBio.01343-17.
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., and Moran, Mary Ann. 2017. "Impact of Dietary Resistant Starch on the Human Gut Microbiome, Metaproteome, and Metabolome". United States. doi:10.1128/mBio.01343-17.
@article{osti_1406729,
title = {Impact of Dietary Resistant Starch on the Human Gut Microbiome, Metaproteome, and Metabolome},
author = {Maier, Tanja V. and Lucio, Marianna and Lee, Lang Ho and VerBerkmoes, Nathan C. and Brislawn, Colin J. and Bernhardt, Jörg and Lamendella, Regina and McDermott, Jason E. and Bergeron, Nathalie and Heinzmann, Silke S. and Morton, James T. and González, Antonio and Ackermann, Gail and Knight, Rob and Riedel, Katharina and Krauss, Ronald M. and Schmitt-Kopplin, Philippe and Jansson, Janet K. and Moran, Mary Ann},
abstractNote = {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 ofFirmicutestoBacteroidetes, including increases in relative abundances of some specific members of theFirmicutesand concurrent increases in enzymatic pathways and metabolites involved in lipid metabolism in the gut. IMPORTANCEThis 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.},
doi = {10.1128/mBio.01343-17},
journal = {mBio (Online)},
number = 5,
volume = 8,
place = {United States},
year = 2017,
month =
}
  • Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. The community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependent onmore » its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.« less
  • Background: Humans live in constant and vital symbiosis with a closely linked bacterial ecosystem called the microbiome, which influences many aspects of human health. When this microbial ecosystem becomes disrupted, the health of the human host can suffer; a condition called dysbiosis. However, the community compositions of human microbiomes also vary dramatically from individual to individual, and over time, making it difficult to uncover the underlying mechanisms linking the microbiome to human health. We propose that a microbiome’s interaction with its human host is not necessarily dependent upon the presence or absence of particular bacterial species, but instead is dependentmore » on its community metabolome; an emergent property of the microbiome. Results: Using data from a previously published, longitudinal study of microbiome populations of the human gut, we extrapolated information about microbiome community enzyme profiles and metabolome models. Using machine learning techniques, we demonstrated that the aggregate predicted community enzyme function profiles and modeled metabolomes of a microbiome are more predictive of dysbiosis than either observed microbiome community composition or predicted enzyme function profiles. Conclusions: Specific enzyme functions and metabolites predictive of dysbiosis provide insights into the molecular mechanisms of microbiome–host interactions. The ability to use machine learning to predict dysbiosis from microbiome community interaction data provides a potentially powerful tool for understanding the links between the human microbiome and human health, pointing to potential microbiome-based diagnostics and therapeutic interventions.« less
  • Production of trimethylamine-N-oxide (TMAO), a biomarker of CVD risk, is dependent on intestinal microbiota, but little is known of dietary conditions promoting changes in gut microbial communities. Resistant starches (RS) alter the human microbiota. We sought to determine whether diets varying in RS and carbohydrate (CHO) content affect plasma TMAO levels. We also assessed postprandial glucose and insulin responses and plasma lipid changes to diets high and low in RS. In a cross-over trial, fifty-two men and women consumed a 2-week baseline diet (41 percentage of energy (%E) CHO, 40 % fat, 19 % protein), followed by 2-week high- andmore » low-RS diets separated by 2-week washouts. RS diets were assigned at random within the context of higher (51–53 %E)v. lower CHO (39–40 %E) intake. Measurements were obtained in the fasting state and, for glucose and insulin, during a meal test matching the composition of the assigned diet. With lower CHO intake, plasma TMAO, carnitine, betaine andγ-butyrobetaine concentrations were higher after the high-v. low-RS diet (P<0·01 each). These metabolites were not differentially affected by highv. low RS when CHO intake was high. Although the high-RS meal reduced postprandial insulin and glucose responses when CHO intake was low (P<0·01 each), RS did not affect fasting lipids, lipoproteins, glucose or insulin irrespective of dietary CHO content. In conclusion, a lower-CHO diet high in RS was associated with higher plasma TMAO levels. These findings, together with the absence of change in fasting lipids, suggest that short-term high-RS diets do not improve markers of cardiometabolic health.« less
  • Although the gut microbiome plays important roles in host physiology, health and disease1, we lack understanding of the complex interplay between host genetics and early life environment on the microbial and metabolic composition of the gut.We used the genetically diverse Collaborative Cross mouse system2 to discover that early life history impacts themicrobiome composition, whereas dietary changes have only a moderate effect. By contrast, the gut metabolome was shaped mostly by diet, with specific non-dietary metabolites explained by microbial metabolism. Quantitative trait analysis identified mouse genetic trait loci (QTL) that impact the abundances of specific microbes. Human orthologues of genes inmore » the mouse QTL are implicated in gastrointestinal cancer. Additionally, genes located in mouse QTL for Lactobacillales abundance are implicated in arthritis, rheumatic disease and diabetes. Furthermore, Lactobacillales abundance was predictive of higher host T-helper cell counts, suggesting an important link between Lactobacillales and host adaptive immunity.« less
  • Although the gut microbiome plays important roles in host physiology, health and disease1, we lack understanding of the complex interplay between host genetics and early life environment on the microbial and metabolic composition of the gut.We used the genetically diverse Collaborative Cross mouse system2 to discover that early life history impacts themicrobiome composition, whereas dietary changes have only a moderate effect. By contrast, the gut metabolome was shaped mostly by diet, with specific non-dietary metabolites explained by microbial metabolism. Quantitative trait analysis identified mouse genetic trait loci (QTL) that impact the abundances of specific microbes. Human orthologues of genes inmore » the mouse QTL are implicated in gastrointestinal cancer. Additionally, genes located in mouse QTL for Lactobacillales abundance are implicated in arthritis, rheumatic disease and diabetes. Furthermore, Lactobacillales abundance was predictive of higher host T-helper cell counts, suggesting an important link between Lactobacillales and host adaptive immunity.« less