CoChat
Sign Up
Cited by 178Gut microbiota and healthOral microbiology and periodontitis researchProbiotics and Fermented Foods

The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.

Potential and active functions in the gut microbiota of a healthy human cohort

Open access
Tanca, Alessandro, Abbondio, Marcello, Palomba, Antonio, Fraumene, Cristina, Manghina, Valeria, Cucca, Francesco, Fiorillo, Edoardo, Uzzau, Sergio·2017·Microbiome
DOI 10.1186/s40168-017-0293-3PMC PMC5513205
Open this paper in CoChat — free accountOpen PDF

Abstract

BACKGROUND: The study of the gut microbiota (GM) is rapidly moving towards its functional characterization by means of shotgun meta-omics. In this context, there is still no consensus on which microbial functions are consistently and constitutively expressed in the human gut in physiological conditions. Here, we selected a cohort of 15 healthy subjects from a native and highly monitored Sardinian population and analyzed their GMs using shotgun metaproteomics, with the aim of investigating GM functions actually expressed in a healthy human population. In addition, shotgun metagenomics was employed to reveal GM functional potential and to compare metagenome and metaproteome profiles in a combined taxonomic and functional fashion. RESULTS: Metagenomic and metaproteomic data concerning the taxonomic structure of the GM under study were globally comparable. On the contrary, a considerable divergence between genetic potential and functional activity of the human healthy GM was observed, with the metaproteome displaying a higher plasticity, compared to the lower inter-individual variability of metagenome profiles. The taxon-specific contribution to functional activities and metabolic tasks was also examined, giving insights into the peculiar role of several GM members in carbohydrate metabolism (including polysaccharide degradation, glycan transport, glycolysis, and short-chain fatty acid production). Noteworthy, Firmicutes-driven butyrogenesis (mainly due to Faecalibacterium spp.) was shown to be the metabolic activity with the highest expression rate and the lowest inter-individual variability in the study cohort, in line with the previously reported importance of the biosynthesis of this microbial product for the gut homeostasis. CONCLUSIONS: Our results provide detailed and taxon-specific information regarding functions and pathways actively working in a healthy GM. The reported discrepancy between expressed functions and functional potential suggests that caution should be used before drawing functional conclusions from metagenomic data, further supporting metaproteomics as a fundamental approach to characterize the human GM metabolic functions and activities.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s40168-017-0293-3

Cites

67 of 67 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Schematic illustrating the experimental design of the study. Fifteen healthy adult subjects (7 males and 8 females) were selected from a clinically monitored Sardinian population. Stool samples were collected from each individual and subjected in parallel to Illumina shotgun DNA sequencing (metagenome profiling) and LTQ-Orbitrap shotgun mass spectrometry analysis (metaproteome profiling). The metagenomes were also employed as sequences databases, in order to allow a rigorous metaproteome/metagenome comparison, and subjected to taxonomic and functional annotation
Fig. 1 Schematic illustrating the experimental design of the study. Fifteen healthy adult subjects (7 males and 8 females) were selected from a clinically monitored Sardinian population. Stool samples were collected from each individual and subjected in parallel to Illumina shotgun DNA sequencing (metagenome profiling) and LTQ-Orbitrap shotgun mass spectrometry analysis (metaproteome profiling). The metagenomes were also employed as sequences databases, in order to allow a rigorous metaproteome/metagenome comparison, and subjected to taxonomic and functional annotationCC BY · PubMed Central
Fig. 2 Main metagenome and metaproteome features of the gut microbiota. MG data are in blue (left), while MP data are in red (right). Data are ordered by decreasing median of the relative abundance among subjects. a Tukey’s boxplots showing the top 5 microbial phyla. b Tukey’s boxplots showing the top 10 microbial genera. c Tukey’s boxplots showing the top 10 gene/protein functions (KEGG orthology groups). Subunit names are shown into brackets. GAPDH glyceraldehyde 3-phosphate dehydrogenase, PEP phosphoenolpyruvate, P phosphate, BP bisphosphate, OH hydroxy
Fig. 2 Main metagenome and metaproteome features of the gut microbiota. MG data are in blue (left), while MP data are in red (right). Data are ordered by decreasing median of the relative abundance among subjects. a Tukey’s boxplots showing the top 5 microbial phyla. b Tukey’s boxplots showing the top 10 microbial genera. c Tukey’s boxplots showing the top 10 gene/protein functions (KEGG orthology groups). Subunit names are shown into brackets. GAPDH glyceraldehyde 3-phosphate dehydrogenase, PEP phosphoenolpyruvate, P phosphate, BP bisphosphate, OH hydroxyCC BY · PubMed Central
Fig. 3 Features with significantly differential abundance between gut metaproteome and metagenome. Data were filtered based on the mean relative abundance of each feature in the sample cohort (threshold >0.01%). a Cladogram illustrating differentially abundant taxa (blue more abundant in MG, red more abundant in MP). Dot size is proportional to the mean relative abundance of the corresponding taxon. 1 Azospirillum, 2 Acinetobacter, 3 Escherichia, 4 Enterobacter, 5 Oxalobacter, 6 Parasutterella, 7 Alistipes, 8 Mucinivorans, 9 Prevotella, 10 Alloprevotella, 11 Paraprevotella, 12 Porphyromonas, 13 Barnesiella, 14 Odoribacter, 15 Tannerella, 16 Parabacteroides, 17 Butyricimonas, 18 Holdemania, 19 Paenibacillus, 20 Bacillus, 21 Streptococcus, 22 Lactobacillus, 23 Megasphaera, 24 Veillonella, 25 Oscillibacter, 26 Peptoclostridium, 27 Butyricicoccus, 28 Pseudoflavonifractor, 29 Intestinimonas, 30 Flavonifractor, 31 Ruminiclostridium, 32 Anaerotruncus, 33 Ruminococcus, 34 Faecalibacterium, 35 Lachnoclostridium, 36 Butyrivibrio, 37 Coprococcus, 38 Tyzzerella, 39 Akkermansia, 40 Brachyspira, 41 Treponema, 42 Propionibacterium, 43 Actinomyces, 44 Eggerthella, 45 Gordonibacter. b Bar graphs showing the KEGG orthology functional groups with higher MP/MG log ratio (top 20, left) and those with lower MP/MG log ratio (top 20, right). Subunit names are shown into brackets. OH hydroxy, GAPDH glyceraldehyde 3-phosphate dehydrogenase, 6P-5DH-2DO-gluconate 6-phospho-5-dehydro-2-deoxy-d-gluconate, PEPCK phosphoenolpyruvate carboxykinase, U-5CMAM modifier uridine 5-carboxymethylaminomethyl modification enzyme, 2MT-N6-DMAA 2-methylthio-N6-dimethylallyladenosine, 1DO-xylulose-5P 1-deoxy-d-xylulose-5-phosphate, CF coupling factor
Fig. 3 Features with significantly differential abundance between gut metaproteome and metagenome. Data were filtered based on the mean relative abundance of each feature in the sample cohort (threshold >0.01%). a Cladogram illustrating differentially abundant taxa (blue more abundant in MG, red more abundant in MP). Dot size is proportional to the mean relative abundance of the corresponding taxon. 1 Azospirillum, 2 Acinetobacter, 3 Escherichia, 4 Enterobacter, 5 Oxalobacter, 6 Parasutterella, 7 Alistipes, 8 Mucinivorans, 9 Prevotella, 10 Alloprevotella, 11 Paraprevotella, 12 Porphyromonas, 13 Barnesiella, 14 Odoribacter, 15 Tannerella, 16 Parabacteroides, 17 Butyricimonas, 18 Holdemania, 19 Paenibacillus, 20 Bacillus, 21 Streptococcus, 22 Lactobacillus, 23 Megasphaera, 24 Veillonella, 25 Oscillibacter, 26 Peptoclostridium, 27 Butyricicoccus, 28 Pseudoflavonifractor, 29 Intestinimonas, 30 Flavonifractor, 31 Ruminiclostridium, 32 Anaerotruncus, 33 Ruminococcus, 34 Faecalibacterium, 35 Lachnoclostridium, 36 Butyrivibrio, 37 Coprococcus, 38 Tyzzerella, 39 Akkermansia, 40 Brachyspira, 41 Treponema, 42 Propionibacterium, 43 Actinomyces, 44 Eggerthella, 45 Gordonibacter. b Bar graphs showing the KEGG orthology functional groups with higher MP/MG log ratio (top 20, left) and those with lower MP/MG log ratio (top 20, right). Subunit names are shown into brackets. OH hydroxy, GAPDH glyceraldehyde 3-phosphate dehydrogenase, 6P-5DH-2DO-gluconate 6-phospho-5-dehydro-2-deoxy-d-gluconate, PEPCK phosphoenolpyruvate carboxykinase, U-5CMAM modifier uridine 5-carboxymethylaminomethyl modification enzyme, 2MT-N6-DMAA 2-methylthio-N6-dimethylallyladenosine, 1DO-xylulose-5P 1-deoxy-d-xylulose-5-phosphate, CF coupling factorCC BY · PubMed Central
Fig. 4 Inter-individual variability of gut microbiota features. Data were filtered based on the mean relative abundance of the features in the sample cohort (threshold >0.01%). a Cladogram illustrating taxa with CV >150% (variable, darker color) or <60% (conserved, lighter color) across subjects, according to MG (blue) and MP (red) data. Green dots represent taxa found conserved (dark) or variable (light) based on both MG and MP data. Dot size is proportional to the mean relative abundance of the corresponding taxon. 1 Azospirillum, 2 Acetobacter, 3 Escherichia, 4 Enterobacter, 5 Parasutterella, 6 Sutterella, 7 Bacteroides, 8 Alistipes, 9 Mucinivorans, 10 Prevotella, 11 Alloprevotella, 12 Paraprevotella, 13 Porphyromonas, 14 Barnesiella, 15 Odoribacter, 16 Tannerella, 17 Butyricimonas, 18 Holdemanella, 19 Bacillus, 20 Streptococcus, 21 Acidaminococcus, 22 Phascolarctobacterium, 23 Megasphaera, 24 Dialister, 25 Peptoclostridium, 26 Intestinimonas, 27 Flavonifractor, 28 Anaerotruncus, 29 Ruminococcus, 30 Subdoligranulum, 31 Faecalibacterium, 32 Lachnoclostridium, 33 Dorea, 34 Butyrivibrio, 35 Coprococcus, 36 Marvinbryantia, 37 Akkermansia, 38 Brachyspira, 39 Bifidobacterium, 40 Gordonibacter, 41 Desulfovibrio. b Bar graphs showing the 10 KEGG orthology functional groups with higher CV (variable, darker color) and the 10 with lower CV (conserved, lighter color) across subjects, according to MG (left, blue) and MP (right, red) data. Subunit names are shown into brackets. Only functional groups detected in at least half of the subjects are shown. MO monooxygenase, IF initiation factor, PRN polyribonucleotide, GMP guanosine monophosphate, LL-DAP l,l-diaminopimelate, 5P 5-phosphate
Fig. 4 Inter-individual variability of gut microbiota features. Data were filtered based on the mean relative abundance of the features in the sample cohort (threshold >0.01%). a Cladogram illustrating taxa with CV >150% (variable, darker color) or <60% (conserved, lighter color) across subjects, according to MG (blue) and MP (red) data. Green dots represent taxa found conserved (dark) or variable (light) based on both MG and MP data. Dot size is proportional to the mean relative abundance of the corresponding taxon. 1 Azospirillum, 2 Acetobacter, 3 Escherichia, 4 Enterobacter, 5 Parasutterella, 6 Sutterella, 7 Bacteroides, 8 Alistipes, 9 Mucinivorans, 10 Prevotella, 11 Alloprevotella, 12 Paraprevotella, 13 Porphyromonas, 14 Barnesiella, 15 Odoribacter, 16 Tannerella, 17 Butyricimonas, 18 Holdemanella, 19 Bacillus, 20 Streptococcus, 21 Acidaminococcus, 22 Phascolarctobacterium, 23 Megasphaera, 24 Dialister, 25 Peptoclostridium, 26 Intestinimonas, 27 Flavonifractor, 28 Anaerotruncus, 29 Ruminococcus, 30 Subdoligranulum, 31 Faecalibacterium, 32 Lachnoclostridium, 33 Dorea, 34 Butyrivibrio, 35 Coprococcus, 36 Marvinbryantia, 37 Akkermansia, 38 Brachyspira, 39 Bifidobacterium, 40 Gordonibacter, 41 Desulfovibrio. b Bar graphs showing the 10 KEGG orthology functional groups with higher CV (variable, darker color) and the 10 with lower CV (conserved, lighter color) across subjects, according to MG (left, blue) and MP (right, red) data. Subunit names are shown into brackets. Only functional groups detected in at least half of the subjects are shown. MO monooxygenase, IF initiation factor, PRN polyribonucleotide, GMP guanosine monophosphate, LL-DAP l,l-diaminopimelate, 5P 5-phosphateCC BY · PubMed Central
Fig. 5 KEGG orthology functional groups with significantly differential abundance between Firmicutes and Bacteroidetes. Data were filtered based on the mean relative abundance of the features in the sample cohort (threshold >0.01%). Functions with higher (top 10) and lower (top 10) Firmicutes/Bacteroidetes (F/B) log ratio according to MG data are shown in the left bar graph (blue); functions with higher (top 10) and lower (top 10) F/B log ratio according to MP data are shown in the right bar graph (red). Subunit names are shown into brackets. MTHF-tRNA-U5 methylenetetrahydrofolate-tRNA-(uracil-5), 1P 1-phosphate, 1,3-β G-NAHA 1,3-beta-galactosyl-N-acetylhexosamine, 5P 5-phosphate, NADH-Q NADH-quinone, PRGA phosphoribosylglycinamide, NS-ornithine N-succinyl-l-ornithine, OMRP outermembrane receptor protein, OH hydroxy, MS-transport multiple sugar transport, ATP-BP adenosine triphosphate-binding protein, 6P-5DH-2DO-gluconate 6-phospho-5-dehydro-2-deoxy-d-gluconate, AICAR 5-aminoimidazole-4-carboxamide ribonucleotide
Fig. 5 KEGG orthology functional groups with significantly differential abundance between Firmicutes and Bacteroidetes. Data were filtered based on the mean relative abundance of the features in the sample cohort (threshold >0.01%). Functions with higher (top 10) and lower (top 10) Firmicutes/Bacteroidetes (F/B) log ratio according to MG data are shown in the left bar graph (blue); functions with higher (top 10) and lower (top 10) F/B log ratio according to MP data are shown in the right bar graph (red). Subunit names are shown into brackets. MTHF-tRNA-U5 methylenetetrahydrofolate-tRNA-(uracil-5), 1P 1-phosphate, 1,3-β G-NAHA 1,3-beta-galactosyl-N-acetylhexosamine, 5P 5-phosphate, NADH-Q NADH-quinone, PRGA phosphoribosylglycinamide, NS-ornithine N-succinyl-l-ornithine, OMRP outermembrane receptor protein, OH hydroxy, MS-transport multiple sugar transport, ATP-BP adenosine triphosphate-binding protein, 6P-5DH-2DO-gluconate 6-phospho-5-dehydro-2-deoxy-d-gluconate, AICAR 5-aminoimidazole-4-carboxamide ribonucleotideCC BY · PubMed Central
Fig. 6 Active carbohydrate metabolism pathways and related taxonomic assignments. a Schematic overview of gut microbiota metabolic pathways from carbohydrate uptake and degradation to the production of short-chain fatty acids (in bold). Numbers in bold correspond to the metabolic pathways listed in b. GH glycosyl hydrolase. b Combination of carbohydrate metabolism pathways/enzymes (rows) and specific gut microbiota phyla/genera (columns) found by MP analysis. Heatmap color scale is based on the logarithmized relative abundance (average of 15 subjects) of each function-taxon combination. For each pathway (rows), only enzymes detected in at least half of the subjects are reported, while the top row (in bold, corresponding to black-bordered squares) accounts for the total abundance of all enzymes (found in at least one subject) belonging to the pathway. For each phylum (columns), only genera expressing a function in at least two subjects are reported, and the phylum column (in bold, corresponding to black-bordered squares) accounts for the total abundance of all functions assigned to that given phylum. “Carbohydrate metabolism” and “microbiota” report the total of rows and columns, respectively. GH glycosyl hydrolase, ABC ATP-binding cassette, MsmK multiple sugar-binding transport ATP-binding protein MsmK, MsmX maltodextrin import ATP-binding protein MsmX, UgpC sn-glycerol-3-phosphate import ATP-binding protein UgpC, YcjV uncharacterized ABC transporter ATP-binding protein YcjV, YtfQ ABC transporter periplasmic-binding protein YtfQ, YurJ uncharacterized ABC transporter ATP-binding protein YurJ, DKI 4-deoxy-l-threo-5-hexosulose-uronate ketol, Ru5P ribose-5-phosphate, GAPDH glyceraldehyde 3-phosphate dehydrogenase, PEPCK phosphoenolpyruvate carboxykinase, OH hydroxy
Fig. 6 Active carbohydrate metabolism pathways and related taxonomic assignments. a Schematic overview of gut microbiota metabolic pathways from carbohydrate uptake and degradation to the production of short-chain fatty acids (in bold). Numbers in bold correspond to the metabolic pathways listed in b. GH glycosyl hydrolase. b Combination of carbohydrate metabolism pathways/enzymes (rows) and specific gut microbiota phyla/genera (columns) found by MP analysis. Heatmap color scale is based on the logarithmized relative abundance (average of 15 subjects) of each function-taxon combination. For each pathway (rows), only enzymes detected in at least half of the subjects are reported, while the top row (in bold, corresponding to black-bordered squares) accounts for the total abundance of all enzymes (found in at least one subject) belonging to the pathway. For each phylum (columns), only genera expressing a function in at least two subjects are reported, and the phylum column (in bold, corresponding to black-bordered squares) accounts for the total abundance of all functions assigned to that given phylum. “Carbohydrate metabolism” and “microbiota” report the total of rows and columns, respectively. GH glycosyl hydrolase, ABC ATP-binding cassette, MsmK multiple sugar-binding transport ATP-binding protein MsmK, MsmX maltodextrin import ATP-binding protein MsmX, UgpC sn-glycerol-3-phosphate import ATP-binding protein UgpC, YcjV uncharacterized ABC transporter ATP-binding protein YcjV, YtfQ ABC transporter periplasmic-binding protein YtfQ, YurJ uncharacterized ABC transporter ATP-binding protein YurJ, DKI 4-deoxy-l-threo-5-hexosulose-uronate ketol, Ru5P ribose-5-phosphate, GAPDH glyceraldehyde 3-phosphate dehydrogenase, PEPCK phosphoenolpyruvate carboxykinase, OH hydroxyCC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 67 references

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Tanca, A., Abbondio, M., Palomba, A., Fraumene, C., Manghina, V., Cucca, F., Fiorillo, E., & Uzzau, S.
Affiliations
  • University of Sassari (IT)
  • Institute of Genetic and Biomedical Research (IT)
Corresponding author
Uzzau, Sergio
ORCID
Venue
Microbiome
Publisher
Springer Science and Business Media LLC
Published
2017-07-14
Volume
5
Issue
1
Pages
79-79
ISSN
2049-2618 (ISSN-L)
DOI
10.1186/s40168-017-0293-3
PMC
PMC5513205
OpenAlex
W2735061408
Cited by
172
References
67
Citations by year
2026: 10 · 2025: 12 · 2024: 20 · 2023: 21 · 2022: 23 · 2021: 18 · 2020: 12 · 2019: 37 · 2018: 16 · 2017: 3
FWCI
7.60
Type
journal article
Language
en
Open access
Licence
Locations
  • Microbiome (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UnissResearch (Università degli Studi di Sassari) (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Sardegna Ricerche (ex art. 26 LR 37/98 - 2014)
  • Università degli Studi di Sassari (Doctoral Fellowships from the International PhD Course in Life Sciences and Biotechnologies)
  • National Institute on Aging (N01-AG-1-2109 and HHSN271201100005C)
  • Fondazione Banco di Sardegna (U1301.2015/AI.1157.BE)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
  • Life Sciences › Agricultural and Biological Sciences › Food Science › Probiotics and Fermented Foods
Keywords
gut microbiota, interindividual variability, carbohydrate metabolism, polysaccharide degradation, glycolysis, short-chain fatty acids, Faecalibacterium, Firmicutes, Sardinian population, intestinal homeostasis
MeSH terms
Bacillota, Firmicutes, Gastrointestinal Microbiome, Faecalibacterium, Adult, Bacteria, Fatty Acids, Volatile, Female, Homeostasis, Humans, Italy, Male, Middle Aged, Cohort Studies, Proteomics, Carbohydrate Metabolism, Biosynthetic Pathways, Metagenome, Young Adult, Metagenomics, Healthy Volunteers

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 ›

Are you an author, or do you see a problem with this page? Request a review