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Reverse Microbiomics: A New Reverse Dysbiosis Analysis Strategy and Its Usage in Prediction of Autoantigens and Virulent Factors in Dysbiotic Gut Microbiomes From Rheumatoid Arthritis Patients

Overview of attention for article published in Frontiers in Microbiology, February 2021
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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Title
Reverse Microbiomics: A New Reverse Dysbiosis Analysis Strategy and Its Usage in Prediction of Autoantigens and Virulent Factors in Dysbiotic Gut Microbiomes From Rheumatoid Arthritis Patients
Published in
Frontiers in Microbiology, February 2021
DOI 10.3389/fmicb.2021.633732
Pubmed ID
Authors

Haihe Wang, Edison Ong, John Y. Kao, Duxin Sun, Yongqun He

Abstract

Alterations in the gut microbiome have been associated with various human diseases. Most existing gut microbiome studies stopped at the stage of identifying microbial alterations between diseased or healthy conditions. As inspired by reverse vaccinology (RV), we developed a new strategy called Reverse Microbiomics (RM) that turns this process around: based on the identified microbial alternations, reverse-predicting the molecular mechanisms underlying the disease and microbial alternations. Our RM methodology starts by identifying significantly altered microbiota profiles, performing bioinformatics analysis on the proteomes of the microbiota identified, and finally predicting potential virulence or protective factors relevant to a microbiome-associated disease. As a use case study, this reverse methodology was applied to study the molecular pathogenesis of rheumatoid arthritis (RA), a common autoimmune and inflammatory disease. Those bacteria differentially associated with RA were first identified and annotated from published data and then modeled and classified using the Ontology of Host-Microbiome Interactions (OHMI). Our study identified 14 species increased and 9 species depleted in the gut microbiota of RA patients. Vaxign was used to comparatively analyze 15 genome sequences of the two pairs of species: Gram-negative Prevotella copri (increased) and Prevotella histicola (depleted), as well as Gram-positive Bifidobacterium dentium (increased) and Bifidobacterium bifidum (depleted). In total, 21 auto-antigens were predicted to be related to RA, and five of them were previously reported to be associated with RA with experimental evidence. Furthermore, we identified 94 potential adhesive virulence factors including 24 microbial ABC transporters. While eukaryotic ABC transporters are key RA diagnosis markers and drug targets, we identified, for the first-time, RA-associated microbial ABC transporters and provided a novel hypothesis of RA pathogenesis. Our study showed that RM, by broadening the scope of RV, is a novel and effective strategy to study from bacterial level to molecular level factors and gain further insight into how these factors possibly contribute to the development of microbial alterations under specific diseases.

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X Demographics

The data shown below were collected from the profiles of 6 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 19%
Student > Bachelor 2 6%
Student > Ph. D. Student 2 6%
Student > Doctoral Student 1 3%
Lecturer 1 3%
Other 4 13%
Unknown 16 50%
Readers by discipline Count As %
Medicine and Dentistry 4 13%
Immunology and Microbiology 4 13%
Nursing and Health Professions 2 6%
Agricultural and Biological Sciences 2 6%
Engineering 2 6%
Other 3 9%
Unknown 15 47%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 16 March 2021.
All research outputs
#14,586,397
of 25,443,857 outputs
Outputs from Frontiers in Microbiology
#10,500
of 29,374 outputs
Outputs of similar age
#211,579
of 451,069 outputs
Outputs of similar age from Frontiers in Microbiology
#383
of 980 outputs
Altmetric has tracked 25,443,857 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 29,374 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has gotten more attention than average, scoring higher than 63% 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 451,069 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 980 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.