Dominant and diet-responsive groups of bacteria within the human colonic microbiota
Open accessAbstract
The populations of dominant species within the human colonic microbiota can potentially be modified by dietary intake with consequences for health. Here we examined the influence of precisely controlled diets in 14 overweight men. Volunteers were provided successively with a control diet, diets high in resistant starch (RS) or non-starch polysaccharides (NSPs) and a reduced carbohydrate weight loss (WL) diet, over 10 weeks. Analysis of 16S rRNA sequences in stool samples of six volunteers detected 320 phylotypes (defined at >98% identity) of which 26, including 19 cultured species, each accounted for >1% of sequences. Although samples clustered more strongly by individual than by diet, time courses obtained by targeted qPCR revealed that ‘blooms’ in specific bacterial groups occurred rapidly after a dietary change. These were rapidly reversed by the subsequent diet. Relatives of Ruminococcus bromii (R-ruminococci) increased in most volunteers on the RS diet, accounting for a mean of 17% of total bacteria compared with 3.8% on the NSP diet, whereas the uncultured Oscillibacter group increased on the RS and WL diets. Relatives of Eubacterium rectale increased on RS (to mean 10.1%) but decreased, along with Collinsella aerofaciens, on WL. Inter-individual variation was marked, however, with >60% of RS remaining unfermented in two volunteers on the RS diet, compared to <4% in the other 12 volunteers; these two individuals also showed low numbers of R-ruminococci (<1%). Dietary non-digestible carbohydrate can produce marked changes in the gut microbiota, but these depend on the initial composition of an individual's gut microbiota.
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Individuals on a resistant starch diet show an increase in relative abundance of uncultured Oscillibacter and Eubacterium rectale.
“Furthermore, individuals on a resistant starch diet showed an increase in relative abundance of uncultured Oscillibacter and Eubacterium rectale (Walker et al., 2011).”
From The Microbiota-Gut-Brain Axis (2019) · cites this paper as Walker et al., 2011
Human dietary fiber interventions have altered the microbiota and improved health markers.
“Dietary interventions that specifically alter dietary fiber, such as increases in total carbohydrates, whole grains, and resistant starch versus wheat bran consumption, have shown impacts on the microbiota along with improvements in health markers of the study participants (Duncan et al., 2007; Martínez et al., 2013; Walker et al., 2011).”
From Gut-microbiota-targeted diets modulate human immune status (2021) · cites this paper as Walker et al., 2011
Some dietary intervention studies associated weight loss with reduced abundance of short-chain fatty acid-producing bacteria.
“Some dietary intervention studies, paradoxically, associated weight loss with reduced abundance of SCFA-producing bacteria.84, 90–93”
From Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer (2020) · cites this paper as [93]
Several studies of different diets associated weight loss with enrichment of Akkermansia muciniphila.
“Several studies of different diets associated weight loss with enrichment of A muciniphila.86, 93, 98, 99”
From Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer (2020) · cites this paper as [93]
Mouse models have indicated that manipulation of macronutrient intake can consistently shift the composition of the gut microbiota within the span of a day.
“Likewise, mouse models have indicated that manipulation of macronutrient intake can consistently shift the composition of the gut microbiota within the span of a day [5,6,18,19].”
From Effect of Diet on the Gut Microbiota: Rethinking Intervention Duration (2019) · cites this paper as [19]
In controlled human dietary experiments, variations in intake of resistant starch or non-starch polysaccharide altered levels of specific bacterial taxa such as Ruminococuccus bromii and Eubacterium rectale.
“In controlled dietary experiments in humans, variations in intake of resistant starch or non-starch polysaccharide altered levels of specific bacterial taxa such as Ruminococuccus bromii and Eubacterium rectale32.”
From Diet and the Intestinal Microbiome: Associations, Functions, and Implications for Health and Disease (2014) · cites this paper as [32]
Diet modulates the composition and function of the gut microbiota in humans and other mammals.
“diet modulates the composition and function of this community of microbes in humans and other mammals18–25”
From Diet–microbiota interactions as moderators of human metabolism (2016) · cites this paper as [23]
The human gut microbiota responds rapidly to large dietary changes, with composition and function shifting within 1-2 days.
“the composition and function of the microbiota shifted over 1–2 days18,20,23”
From Diet–microbiota interactions as moderators of human metabolism (2016) · cites this paper as [23]
A given dietary change can have highly variable effects across individuals because of individualized gut microbiota.
“a particular change in diet can have a highly variable effect on different people owing to the individualized nature of their gut microbiota”
From Diet–microbiota interactions as moderators of human metabolism (2016) · cites this paper as [23]
Ruminococcus bromii-related taxa bloomed in response to resistant starch in most of 14 obese men; non-response may reflect absence of those taxa.
“Ruminococcus bromii-related taxa bloomed in response to resistant-starch intervention in most of the 14 obese men in one study; the lack of response in the other individuals might reflect an absence of such taxa in those people23.”
From Diet–microbiota interactions as moderators of human metabolism (2016) · cites this paper as [23]
In overweight and obese individuals during weight-loss strategies with resistant-starch-carbohydrate reductions, Eubacterium rectale and Bifidobacteria were decreased.
“decreased Eubacterium rectale and Bifidobacteria have been observed during weight-loss strategies in overweight and obese individuals in which further resistant-starch-carbohydrates reductions were common [240,241].”
From Impact of Protein Intake in Older Adults with Sarcopenia and Obesity: A Gut Microbiota Perspective (2020) · cites this paper as [241]
Nutrient availability significantly impacts the diversity and abundance of the micro-organisms that colonize the intestine.
“Nutrient availability significantly impacts the diversity and abundance of the micro-organisms that colonize the intestine [58–63].”
From Role of the gut microbiota in nutrient competition and protection against intestinal pathogen colonization (2023) · cites this paper as [59]
Consumption of a high-fiber diet promotes an increase in bacterial diversity and leads to a bloom in the growth of beneficial bacteria (Bifidobacterium sp., Lactobacillus sp., Akkermansia sp., Faecalibacterium sp., Roseburia sp., Bacteroides sp., and Prevotella) as well as a reduction in potentially pathogenic bacteria (e.g., Enterobacteriaceae).
“It is generally accepted that the consumption of a highfiber diet promotes an increase in bacterial diversity and leads to a bloom in the growth of beneficial bacteria (i.e., Bifidobacterium sp., Lactobacillus sp., Akkermansia sp., Faecalibacterium sp., Roseburia sp., Bacteroides sp., and Prevotella) as well as a reduction in potentially pathogenic bacteria (e.g., Enterobacteriaceae) (63)(64)(65)(”
From Diet and the Microbiota–Gut–Brain Axis: Sowing the Seeds of Good Mental Health (2021) · cites this paper as [64]
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Biomass Utilization by Gut Microbiomes
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- Authors
- Walker, A. W., Ince, J., Duncan, S. H., Webster, L. M., Holtrop, G., Ze, X., Brown, D., Stares, M. D., Scott, P., Bergerat, A., Louis, P., McIntosh, F., Johnstone, A. M., Lobley, G. E., Parkhill, J., & Flint, H. J.
- Affiliations
- Wellcome Sanger Institute (GB)
- University of Aberdeen (GB)
- Biomathematics and Statistics Scotland (GB)
- Corresponding author
- Flint, Harry J
- ORCID
- Venue
- The ISME Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-08-05
- Volume
- 5
- Issue
- 2
- Pages
- 220-230
- ISSN
- 1751-7362 (ISSN-L)
- 1751-7370
- PMC
- PMC3105703
- OpenAlex
- W2033239478
- Cited by
- 1,632
- References
- 59
- Citations by year
- 2026: 35 · 2025: 65 · 2024: 93 · 2023: 92 · 2022: 155 · 2021: 135 · 2020: 163 · 2019: 122 · 2018: 124 · 2017: 139 · 2016: 118 · 2015: 101 · 2014: 93 · 2013: 85 · 2012: 66
- FWCI
- 25.26
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The ISME Journal (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (WT 76964, 076964)
- Scottish Government
- University of Aberdeen
- World Cancer Research Fund
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Life Sciences › Agricultural and Biological Sciences › Food Science › Probiotics and Fermented Foods
- Health Sciences › Nursing › Nutrition and Dietetics › Microbial Metabolites in Food Biotechnology
- Keywords
- resistant starch, non-starch polysaccharides, Ruminococcus bromii, Oscillibacter, Eubacterium rectale, Collinsella aerofaciens, interindividual variability, non-digestible carbohydrates, gut microbiota composition, overweight men
- MeSH terms
- Archaea, Bacteria, Colon, Diet, Diet, Reducing, Dietary Carbohydrates, Dietary Proteins, Feces, Humans, Male, RNA, Ribosomal, 16S, Starch, Cross-Over Studies, Biodiversity, Metagenome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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