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In silico analysis of autoimmune diseases and genetic relationships to vaccination against infectious diseases

Overview of attention for article published in BMC Immunology, December 2014
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

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96 Mendeley
Title
In silico analysis of autoimmune diseases and genetic relationships to vaccination against infectious diseases
Published in
BMC Immunology, December 2014
DOI 10.1186/s12865-014-0061-0
Pubmed ID
Authors

Peter B McGarvey, Baris E Suzek, James N Baraniuk, Shruti Rao, Brian Conkright, Samir Lababidi, Andrea Sutherland, Richard Forshee, Subha Madhavan

Abstract

BackgroundNear universal administration of vaccines mandates intense pharmacovigilance for vaccine safety and a stringently low tolerance for adverse events. Reports of autoimmune diseases (AID) following vaccination have been challenging to evaluate given the high rates of vaccination, background incidence of autoimmunity, and low incidence and variable times for onset of AID after vaccinations. In order to identify biologically plausible pathways to adverse autoimmune events of vaccine-related AID, we used a systems biology approach to create a matrix of innate and adaptive immune mechanisms active in specific diseases, responses to vaccine antigens, adjuvants, preservatives and stabilizers, for the most common vaccine-associated AID found in the Vaccine Adverse Event Reporting System.ResultsThis report focuses on Guillain-Barre Syndrome (GBS), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Idiopathic (or immune) Thrombocytopenic Purpura (ITP). Multiple curated databases and automated text mining of PubMed literature identified 667 genes associated with RA, 448 with SLE, 49 with ITP and 73 with GBS. While all data sources provided valuable and unique gene associations, text mining using natural language processing (NLP) algorithms provided the most information but required curation to remove incorrect associations. Six genes were associated with all four AIDs. Thirty-three pathways were shared by the four AIDs. Classification of genes into twelve immune system related categories identified more ¿Th17 T-cell subtype¿ genes in RA than the other AIDs, and more ¿Chemokine plus Receptors¿ genes associated with RA than SLE. Gene networks were visualized and clustered into interconnected modules with specific gene clusters for each AID, including one in RA with ten C-X-C motif chemokines. The intersection of genes associated with GBS, GBS peptide auto-antigens, influenza A infection, and influenza vaccination created a subnetwork of genes that inferred a possible role for the MAPK signaling pathway in influenza vaccine related GBS.ConclusionsResults showing unique and common gene sets, pathways, immune system categories and functional clusters of genes in four autoimmune diseases suggest it is possible to develop molecular classifications of autoimmune and inflammatory events. Combining this information with cellular and other disease responses should greatly aid in the assessment of potential immune-mediated adverse events following vaccination.

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 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 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 1 1%
Germany 1 1%
Italy 1 1%
Singapore 1 1%
Spain 1 1%
Unknown 91 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 15%
Student > Master 11 11%
Student > Ph. D. Student 10 10%
Student > Bachelor 9 9%
Other 8 8%
Other 23 24%
Unknown 21 22%
Readers by discipline Count As %
Medicine and Dentistry 32 33%
Agricultural and Biological Sciences 11 11%
Biochemistry, Genetics and Molecular Biology 5 5%
Nursing and Health Professions 3 3%
Computer Science 3 3%
Other 14 15%
Unknown 28 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 02 May 2020.
All research outputs
#5,323,250
of 25,383,278 outputs
Outputs from BMC Immunology
#79
of 623 outputs
Outputs of similar age
#70,335
of 371,352 outputs
Outputs of similar age from BMC Immunology
#1
of 13 outputs
Altmetric has tracked 25,383,278 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 623 research outputs from this source. They receive a mean Attention Score of 3.8. 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 371,352 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.