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Bioinformatics Analysis of the Factors Controlling Type I IFN Gene Expression in Autoimmune Disease and Virus-Induced Immunity

Overview of attention for article published in Frontiers in immunology, January 2013
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Title
Bioinformatics Analysis of the Factors Controlling Type I IFN Gene Expression in Autoimmune Disease and Virus-Induced Immunity
Published in
Frontiers in immunology, January 2013
DOI 10.3389/fimmu.2013.00291
Pubmed ID
Authors

Di Feng, Betsy J. Barnes

Abstract

Patients with systemic lupus erythematosus (SLE) and Sjögren's syndrome (SS) display increased levels of type I interferon (IFN)-induced genes. Plasmacytoid dendritic cells (PDCs) are natural interferon producing cells and considered to be a primary source of IFN-α in these two diseases. Differential expression patterns of type I IFN-inducible transcripts can be found in different immune cell subsets and in patients with both active and inactive autoimmune disease. A type I IFN gene signature generally consists of three groups of IFN-induced genes - those regulated in response to virus-induced type I IFN, those regulated by the IFN-induced mitogen-activated protein kinase/extracellular-regulated kinase (MAPK/ERK) pathway, and those by the IFN-induced phosphoinositide-3 kinase (PI-3K) pathway. These three groups of type I IFN-regulated genes control important cellular processes such as apoptosis, survival, adhesion, and chemotaxis, that when dysregulated, contribute to autoimmunity. With the recent generation of large datasets in the public domain from next-generation sequencing and DNA microarray experiments, one can perform detailed analyses of cell-type specific gene signatures as well as identify distinct transcription factors (TFs) that differentially regulate these gene signatures. We have performed bioinformatics analysis of data in the public domain and experimental data from our lab to gain insight into the regulation of type I IFN gene expression. We have found that the genetic landscape of the IFNA and IFNB genes are occupied by TFs, such as insulators CTCF and cohesin, that negatively regulate transcription, as well as interferon regulatory factor (IRF)5 and IRF7, that positively and distinctly regulate IFNA subtypes. A detailed understanding of the factors controlling type I IFN gene transcription will significantly aid in the identification and development of new therapeutic strategies targeting the IFN pathway in autoimmune disease.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Germany 1 2%
Unknown 51 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 25%
Researcher 13 25%
Student > Bachelor 6 11%
Student > Master 4 8%
Student > Doctoral Student 3 6%
Other 9 17%
Unknown 5 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 26%
Medicine and Dentistry 9 17%
Biochemistry, Genetics and Molecular Biology 9 17%
Immunology and Microbiology 9 17%
Engineering 2 4%
Other 5 9%
Unknown 5 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 25 September 2013.
All research outputs
#19,945,185
of 25,374,917 outputs
Outputs from Frontiers in immunology
#22,575
of 31,520 outputs
Outputs of similar age
#221,306
of 289,007 outputs
Outputs of similar age from Frontiers in immunology
#240
of 503 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 31,520 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one is in the 21st percentile – i.e., 21% 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 289,007 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 503 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.