CoChat
Sign Up
Cited by 386Oral microbiology and periodontitis researchGut microbiota and healthGenomics and Phylogenetic Studies

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Metatranscriptomics of the Human Oral Microbiome during Health and Disease

Open access
Jorth, Peter, Turner, Keith H., Gumus, Pinar, Nizam, Nejat, Buduneli, Nurcan, Whiteley, Marvin·2014·mBio
DOI 10.1128/mbio.01012-14PMC PMC3977359
Open this paper in CoChat — free account

Abstract

The human microbiome plays important roles in health, but when disrupted, these same indigenous microbes can cause disease. The composition of the microbiome changes during the transition from health to disease; however, these changes are often not conserved among patients. Since microbiome-associated diseases like periodontitis cause similar patient symptoms despite interpatient variability in microbial community composition, we hypothesized that human-associated microbial communities undergo conserved changes in metabolism during disease. Here, we used patient-matched healthy and diseased samples to compare gene expression of 160,000 genes in healthy and diseased periodontal communities. We show that health- and disease-associated communities exhibit defined differences in metabolism that are conserved between patients. In contrast, the metabolic gene expression of individual species was highly variable between patients. These results demonstrate that despite high interpatient variability in microbial composition, disease-associated communities display conserved metabolic profiles that are generally accomplished by a patient-specific cohort of microbes. IMPORTANCE The human microbiome project has shown that shifts in our microbiota are associated with many diseases, including obesity, Crohn’s disease, diabetes, and periodontitis. While changes in microbial populations are apparent during these diseases, the species associated with each disease can vary from patient to patient. Taking into account this interpatient variability, we hypothesized that specific microbiota-associated diseases would be marked by conserved microbial community behaviors. Here, we use gene expression analyses of patient-matched healthy and diseased human periodontal plaque to show that microbial communities have highly conserved metabolic gene expression profiles, whereas individual species within the community do not. Furthermore, disease-associated communities exhibit conserved changes in metabolic and virulence gene expression.

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 · 10.1128/mbio.01012-14

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Figures

FIG 1 Ribosome quantification reveals that disease-associated periodontal microbiota are less diverse and contain fewer low-abundance species than do health-associated populations. (A) Number of distinct 16S rRNA sequences (OTUs) observed in healthy (blue) and diseased (red) samples with increasing numbers of sequences sampled from each population. Error bars indicate standard errors of the means (n = 10). (B) Shannon indices show that health-associated populations are more species rich than diseased populations (*, P = 0.03, paired two-tailed Student t test). (C) Beta diversity was measured using the unweighted Unifrac method to calculate relatedness of paired health-associated (blue) and disease-associated (red) microbial populations by assessment of shared and unique species in each community. Principal coordinates 1 and 2 are plotted. Mean diseased and healthy centroids (mean ± standard deviation) are indicated by ellipses. Distances between samples and corresponding centroids are shown as blue and red lines, respectively. Black lines show distances between paired populations from the same patient. (D) Mean Euclidean distance (mean ± standard deviation) from each sample to corresponding centroids and corresponding paired sample from same patient (**, P = 0.0005, paired two-tailed Student t test).
FIG 1 Ribosome quantification reveals that disease-associated periodontal microbiota are less diverse and contain fewer low-abundance species than do health-associated populations. (A) Number of distinct 16S rRNA sequences (OTUs) observed in healthy (blue) and diseased (red) samples with increasing numbers of sequences sampled from each population. Error bars indicate standard errors of the means (n = 10). (B) Shannon indices show that health-associated populations are more species rich than diseased populations (*, P = 0.03, paired two-tailed Student t test). (C) Beta diversity was measured using the unweighted Unifrac method to calculate relatedness of paired health-associated (blue) and disease-associated (red) microbial populations by assessment of shared and unique species in each community. Principal coordinates 1 and 2 are plotted. Mean diseased and healthy centroids (mean ± standard deviation) are indicated by ellipses. Distances between samples and corresponding centroids are shown as blue and red lines, respectively. Black lines show distances between paired populations from the same patient. (D) Mean Euclidean distance (mean ± standard deviation) from each sample to corresponding centroids and corresponding paired sample from same patient (**, P = 0.0005, paired two-tailed Student t test).CC BY-NC-SA · PubMed Central
FIG 2 Differential expression of enzyme gene families in health and disease. Log2 fold change during disease is plotted against the log2 mean read counts per million total reads for each EC enzyme-encoding gene family. Gene families upregulated in health are shown in blue, while gene families upregulated in disease are shown in red.
FIG 2 Differential expression of enzyme gene families in health and disease. Log2 fold change during disease is plotted against the log2 mean read counts per million total reads for each EC enzyme-encoding gene family. Gene families upregulated in health are shown in blue, while gene families upregulated in disease are shown in red.CC BY-NC-SA · PubMed Central
FIG 3 Differential metabolic gene expression in the diseased periodontal microbiome. Metabolic network reconstruction. Black lines indicate enzyme-encoding genes that were expressed and unchanged in health and disease, red lines indicate genes upregulated during disease, and blue lines indicate genes upregulated during health. Colored regions identify different sections of the metabolic pathway map. Those highlighted in yellow represent important pathways that were upregulated in disease. Complete data showing all differentially regulated genes are available in supplementary files 2 and 3 at http://web.biosci.utexas.edu/whiteley_lab/pages/resources.html. THF, tetrahydrofolate metabolism; TCA, tricarboxylic acid.
FIG 3 Differential metabolic gene expression in the diseased periodontal microbiome. Metabolic network reconstruction. Black lines indicate enzyme-encoding genes that were expressed and unchanged in health and disease, red lines indicate genes upregulated during disease, and blue lines indicate genes upregulated during health. Colored regions identify different sections of the metabolic pathway map. Those highlighted in yellow represent important pathways that were upregulated in disease. Complete data showing all differentially regulated genes are available in supplementary files 2 and 3 at http://web.biosci.utexas.edu/whiteley_lab/pages/resources.html. THF, tetrahydrofolate metabolism; TCA, tricarboxylic acid.CC BY-NC-SA · PubMed Central
FIG 4 Metabolic niche dynamics in diseased populations. (A) Production of butyrate is primarily due to F. nucleatum lysine fermentation. (B) Multiple species that vary among patients fill histidine degradation and tetrahydrofolate (THF) metabolic niches. (C) Multiple species that vary among patients carry out pyruvate fermentation. For panels A to C, community fold changes of EC enzyme-encoding gene expression are indicated at each arrow. (D) Different organisms fill virulence niches in diseased periodontal communities. In patient 1, Tannerella forsythia is the major source of collagenase expression, whereas collagenase expression is augmented by Prevotella tannerae in patient 2 and by Porphyromonas gingivalis in patient 3. Protease production follows similar patterns, whereby combinations of different species express proteases in each patient. In panels A to D, heat maps indicate relative normalized expression (log2 reads per million reads in each sample) of different enzyme-encoding genes or virulence genes by species in each patient. Abbreviations of species names and the color scale for heat maps are indicated. CoA, coenzyme A.
FIG 4 Metabolic niche dynamics in diseased populations. (A) Production of butyrate is primarily due to F. nucleatum lysine fermentation. (B) Multiple species that vary among patients fill histidine degradation and tetrahydrofolate (THF) metabolic niches. (C) Multiple species that vary among patients carry out pyruvate fermentation. For panels A to C, community fold changes of EC enzyme-encoding gene expression are indicated at each arrow. (D) Different organisms fill virulence niches in diseased periodontal communities. In patient 1, Tannerella forsythia is the major source of collagenase expression, whereas collagenase expression is augmented by Prevotella tannerae in patient 2 and by Porphyromonas gingivalis in patient 3. Protease production follows similar patterns, whereby combinations of different species express proteases in each patient. In panels A to D, heat maps indicate relative normalized expression (log2 reads per million reads in each sample) of different enzyme-encoding genes or virulence genes by species in each patient. Abbreviations of species names and the color scale for heat maps are indicated. CoA, coenzyme A.CC BY-NC-SA · PubMed Central
FIG 5 EC expression is less variable than individual gene expression. (A) Variance estimations for genes in the metagenome determined in edgeR analyses. (B) Variance estimations for EC expression determined in edgeR analyses. (C) Variance estimations for genes randomly binned into 1,137 gene groups determined in edgeR analyses. Blue lines in panels A to C indicate the tagwise dispersion, while red lines show the common dispersions calculated with edgeR. BCV, biological coefficient of variance.
FIG 5 EC expression is less variable than individual gene expression. (A) Variance estimations for genes in the metagenome determined in edgeR analyses. (B) Variance estimations for EC expression determined in edgeR analyses. (C) Variance estimations for genes randomly binned into 1,137 gene groups determined in edgeR analyses. Blue lines in panels A to C indicate the tagwise dispersion, while red lines show the common dispersions calculated with edgeR. BCV, biological coefficient of variance.CC BY-NC-SA · 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 33 references

Cited by

Sorted by citations

See all 386

Related

Metadata

Authors
Jorth, P., Turner, K. H., Gumus, P., Nizam, N., Buduneli, N., & Whiteley, M.
Affiliations
  • The University of Texas at Austin (US)
  • Department of Periodontology, School of Dentistry, Ege University, Izmir, Turkey
  • Ege University (TR)
ORCID
Venue
mBio
Publisher
American Society for Microbiology
Published
2014-05
Volume
5
Issue
2
Pages
e01012-14
ISSN
  • 2150-7511 (ISSN-L)
  • 2161-2129
DOI
10.1128/mbio.01012-14
PMC
PMC3977359
OpenAlex
W2146975236
Cited by
386
References
33
Citations by year
2026: 18 · 2025: 30 · 2024: 24 · 2023: 13 · 2022: 25 · 2021: 33 · 2020: 45 · 2019: 39 · 2018: 33 · 2017: 40 · 2016: 33 · 2015: 39 · 2014: 14
FWCI
56.09
Type
journal article
Language
en
Open access
Licence
Locations
  • mBio (publishedVersion, cc-by-nc-sa)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-sa)
  • PubMed Central (publishedVersion, cc-by-nc-sa)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institutes of Health (5f31de021633-02)
  • National Institute of Dental and Craniofacial Research (F31 DE021633, R01DE023193, 5F31DE021633-02, R01DE020100)
Topics
  • Health Sciences › Dentistry › Periodontics › Oral microbiology and periodontitis research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
oral microbiota, periodontitis, metatranscriptomics, microbial community metabolism, interpatient variability, periodontal plaque, virulence gene expression
MeSH terms
Adult, Female, Humans, Male, Mouth, Periodontitis, Metagenome, Transcriptome, Microbiota, Dysbiosis

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