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Functional signatures of oral dysbiosis during periodontitis progression revealed by microbial metatranscriptome analysis

Overview of attention for article published in Genome Medicine, April 2015
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

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1 news outlet
blogs
1 blog
twitter
23 X users
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3 patents
facebook
2 Facebook pages
wikipedia
2 Wikipedia pages

Citations

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248 Dimensions

Readers on

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261 Mendeley
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Title
Functional signatures of oral dysbiosis during periodontitis progression revealed by microbial metatranscriptome analysis
Published in
Genome Medicine, April 2015
DOI 10.1186/s13073-015-0153-3
Pubmed ID
Authors

Susan Yost, Ana E Duran-Pinedo, Ricardo Teles, Keerthana Krishnan, Jorge Frias-Lopez

Abstract

Periodontitis is a polymicrobial biofilm-induced inflammatory disease that affects 743 million people worldwide. The current model to explain periodontitis progression proposes that changes in the relative abundance of members of the oral microbiome lead to dysbiosis in the host-microbiome crosstalk and then to inflammation and bone loss. Using combined metagenome/metatranscriptome analysis of the subgingival microbiome in progressing and non-progressing sites, we have characterized the distinct molecular signatures of periodontitis progression. Metatranscriptome analysis was conducted on samples from subgingival biofilms from progressing and stable sites from periodontitis patients. Community-wide expression profiles were obtained using Next Generation Sequencing (Illumina). Sequences were aligned using 'bowtie2' against a constructed oral microbiome database. Differential expression analysis was performed using the non-parametric algorithm implemented on the R package 'NOISeqBio'. We summarized global functional activities of the oral microbial community by set enrichment analysis based on the Gene Ontology (GO) orthology. Gene ontology enrichment analysis showed an over-representation in the baseline of active sites of terms related to cell motility, lipid A and peptidoglycan biosynthesis, and transport of iron, potassium, and amino acids. Periodontal pathogens (Tannerella forsythia and Porphyromonas gingivalis) upregulated different TonB-dependent receptors, peptidases, proteases, aerotolerance genes, iron transport genes, hemolysins, and CRISPR-associated genes. Surprisingly, organisms that have not been usually associated with the disease (Streptococcus oralis, Streptococcus mutans, Streptococcus intermedius, Streptococcus mitis, Veillonella parvula, and Pseudomonas fluorenscens) were highly active transcribing putative virulence factors. We detected patterns of activities associated with progression of clinical traits. Among those we found that the profiles of expression of cobalamin biosynthesis, proteolysis, and potassium transport were associated with the evolution towards disease. We identified metabolic changes in the microbial community associated with the initial stages of dysbiosis. Regardless of the overall composition of the community, certain metabolic signatures are consistent with disease progression. Our results suggest that the whole community, and not just a handful of oral pathogens, is responsible for an increase in virulence that leads to progression. NCT01489839, 6 December 2011.

X Demographics

X Demographics

The data shown below were collected from the profiles of 23 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 261 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 1%
France 1 <1%
United Kingdom 1 <1%
Brazil 1 <1%
Japan 1 <1%
Spain 1 <1%
Unknown 253 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 54 21%
Researcher 40 15%
Student > Master 27 10%
Student > Bachelor 22 8%
Professor > Associate Professor 13 5%
Other 39 15%
Unknown 66 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 59 23%
Medicine and Dentistry 42 16%
Biochemistry, Genetics and Molecular Biology 34 13%
Immunology and Microbiology 17 7%
Computer Science 6 2%
Other 28 11%
Unknown 75 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 40. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 April 2022.
All research outputs
#1,052,359
of 25,779,988 outputs
Outputs from Genome Medicine
#201
of 1,613 outputs
Outputs of similar age
#12,656
of 280,784 outputs
Outputs of similar age from Genome Medicine
#5
of 28 outputs
Altmetric has tracked 25,779,988 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,613 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.1. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 280,784 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.