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Molecular mimicry and autoimmune thyroid disease

Overview of attention for article published in Reviews in Endocrine and Metabolic Disorders, June 2016
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126 Mendeley
Title
Molecular mimicry and autoimmune thyroid disease
Published in
Reviews in Endocrine and Metabolic Disorders, June 2016
DOI 10.1007/s11154-016-9363-2
Pubmed ID
Authors

Salvatore Benvenga, Fabrizio Guarneri

Abstract

Hypothesized 40 years ago, molecular mimicry has been thereafter demonstrated as an extremely common mechanism by which microbes elude immune response and modulate biosynthetic/metabolic pathways of the host. In genetically predisposed persons and under particular conditions, molecular mimicry between microbial and human antigens can turn a defensive immune response into autoimmunity. Such triggering role and its pathogenetic importance have been investigated and demonstrated for many autoimmune diseases. However, this is not the case for autoimmune thyroid disease, which appears relatively neglected by this field of research. Here we review the available literature on the possible role of molecular mimicry as a trigger of autoimmune thyroid disease. Additionally, we present the results of in silico search for amino acid sequence homologies between some microbial proteins and thyroid autoantigens, and the potential pathogenetic relevance of such homologies. Relevance stems from the overlap with known autoepitopes and the occurrence of specific HLA-DR binding motifs. Bioinformatics data published by our group support and explain the triggering role of Borrelia, Yersinia, Clostridium botulinum, Rickettsia prowazekii and Helicobacter pylori. Our new data suggest the potential pathogenic importance of Toxoplasma gondii, some Bifidobacteria and Lactobacilli, Candida albicans, Treponema pallidum and hepatitis C virus in autoimmune thyroid disease, indicating specific molecular targets for future research. Additionally, the consistency between in silico prediction of cross-reactivity and experimental results shows the reliability and usefulness of bioinformatics tools to precisely identify candidate molecules for in vitro and/or in vivo experiments, or at least narrow down their number.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 126 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Germany 1 <1%
Brazil 1 <1%
Unknown 123 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 16%
Other 15 12%
Student > Ph. D. Student 14 11%
Student > Bachelor 13 10%
Student > Master 13 10%
Other 29 23%
Unknown 22 17%
Readers by discipline Count As %
Medicine and Dentistry 30 24%
Agricultural and Biological Sciences 23 18%
Biochemistry, Genetics and Molecular Biology 16 13%
Nursing and Health Professions 12 10%
Immunology and Microbiology 10 8%
Other 9 7%
Unknown 26 21%
Attention Score in Context

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 20 October 2016.
All research outputs
#17,003,716
of 25,766,791 outputs
Outputs from Reviews in Endocrine and Metabolic Disorders
#390
of 556 outputs
Outputs of similar age
#228,462
of 369,097 outputs
Outputs of similar age from Reviews in Endocrine and Metabolic Disorders
#9
of 17 outputs
Altmetric has tracked 25,766,791 research outputs across all sources so far. This one is in the 31st percentile – i.e., 31% of other outputs scored the same or lower than it.
So far Altmetric has tracked 556 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.3. This one is in the 26th percentile – i.e., 26% 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 369,097 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.