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Gut microbiota in experimental murine model of Graves’ orbitopathy established in different environments may modulate clinical presentation of disease

Overview of attention for article published in Microbiome, May 2018
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Gut microbiota in experimental murine model of Graves’ orbitopathy established in different environments may modulate clinical presentation of disease
Published in
Microbiome, May 2018
DOI 10.1186/s40168-018-0478-4
Pubmed ID

Giulia Masetti, Sajad Moshkelgosha, Hedda-Luise Köhling, Danila Covelli, Jasvinder Paul Banga, Utta Berchner-Pfannschmidt, Mareike Horstmann, Salvador Diaz-Cano, Gina-Eva Goertz, Sue Plummer, Anja Eckstein, Marian Ludgate, Filippo Biscarini, Julian Roberto Marchesi


Variation in induced models of autoimmunity has been attributed to the housing environment and its effect on the gut microbiota. In Graves' disease (GD), autoantibodies to the thyrotropin receptor (TSHR) cause autoimmune hyperthyroidism. Many GD patients develop Graves' orbitopathy or ophthalmopathy (GO) characterized by orbital tissue remodeling including adipogenesis. Murine models of GD/GO would help delineate pathogenetic mechanisms, and although several have been reported, most lack reproducibility. A model comprising immunization of female BALBc mice with a TSHR expression plasmid using in vivo electroporation was reproduced in two independent laboratories. Similar orbital disease was induced in both centers, but differences were apparent (e.g., hyperthyroidism in Center 1 but not Center 2). We hypothesized a role for the gut microbiota influencing the outcome and reproducibility of induced GO. We combined metataxonomics (16S rRNA gene sequencing) and traditional microbial culture of the intestinal contents from the GO murine model, to analyze the gut microbiota in the two centers. We observed significant differences in alpha and beta diversity and in the taxonomic profiles, e.g., operational taxonomic units (OTUs) from the genus Lactobacillus were more abundant in Center 2, and Bacteroides and Bifidobacterium counts were more abundant in Center 1 where we also observed a negative correlation between the OTUs of the genus Intestinimonas and TSHR autoantibodies. Traditional microbiology largely confirmed the metataxonomics data and indicated significantly higher yeast counts in Center 1 TSHR-immunized mice. We also compared the gut microbiota between immunization groups within Center 2, comprising the TSHR- or βgal control-immunized mice and naïve untreated mice. We observed a shift of the TSHR-immunized mice bacterial communities described by the beta diversity weighted Unifrac. Furthermore, we observed a significant positive correlation between the presence of Firmicutes and orbital-adipogenesis specifically in TSHR-immunized mice. The significant differences observed in microbiota composition from BALBc mice undergoing the same immunization protocol in comparable specific-pathogen-free (SPF) units in different centers support a role for the gut microbiota in modulating the induced response. The gut microbiota might also contribute to the heterogeneity of induced response since we report potential disease-associated microbial taxonomies and correlation with ocular disease.

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Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 51 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 20%
Student > Ph. D. Student 8 16%
Other 5 10%
Professor > Associate Professor 5 10%
Student > Bachelor 4 8%
Other 10 20%
Unknown 9 18%
Readers by discipline Count As %
Medicine and Dentistry 15 29%
Biochemistry, Genetics and Molecular Biology 9 18%
Immunology and Microbiology 7 14%
Veterinary Science and Veterinary Medicine 2 4%
Computer Science 2 4%
Other 4 8%
Unknown 12 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 01 June 2018.
All research outputs
of 13,020,439 outputs
Outputs from Microbiome
of 701 outputs
Outputs of similar age
of 271,415 outputs
Outputs of similar age from Microbiome
of 22 outputs
Altmetric has tracked 13,020,439 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 701 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 37.1. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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 271,415 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 50% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.