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Network analysis of psoriasis reveals biological pathways and roles for coding and long non-coding RNAs

Overview of attention for article published in BMC Genomics, October 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

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Title
Network analysis of psoriasis reveals biological pathways and roles for coding and long non-coding RNAs
Published in
BMC Genomics, October 2016
DOI 10.1186/s12864-016-3188-y
Pubmed ID
Authors

Richard Ahn, Rashmi Gupta, Kevin Lai, Nitin Chopra, Sarah T. Arron, Wilson Liao

Abstract

Psoriasis is an immune-mediated, inflammatory disorder of the skin characterized by chronic inflammation and hyperproliferation of the epidermis. Differential expression analysis of microarray or RNA-seq data have shown that thousands of coding and non-coding genes are differentially expressed between psoriatic and healthy control skin. However, differential expression analysis may fail to detect perturbations in gene coexpression networks. Sensitive detection of such networks may provide additional insight into important disease-associated pathways. In this study, we applied weighted gene coexpression network analysis (WGCNA) on RNA-seq data from psoriasis patients and healthy controls. RNA-seq was performed on skin samples from 18 psoriasis patients (pre-treatment and post-treatment with the TNF-α inhibitor adalimumab) and 16 healthy controls, generating an average of 52.3 million 100-bp paired-end reads per sample. Using WGCNA, we identified 3 network modules that were significantly correlated with psoriasis and 6 network modules significantly correlated with biologic treatment, with only 16 % of the psoriasis-associated and 5 % of the treatment-associated coexpressed genes being identified by differential expression analysis. In a majority of these correlated modules, more than 50 % of coexpressed genes were long non-coding RNAs (lncRNA). Enrichment analysis of these correlated modules revealed that short-chain fatty acid metabolism and olfactory signaling are amongst the top pathways enriched for in modules associated with psoriasis, while regulation of leukocyte mediated cytotoxicity and regulation of cell killing are amongst the top pathways enriched for in modules associated with biologic treatment. A putative autoantigen, LL37, was coexpressed in the module most correlated with psoriasis. This study has identified several networks of coding and non-coding genes associated with psoriasis and biologic drug treatment, including networks enriched for short-chain fatty acid metabolism and olfactory receptor activity, pathways that were not previously identified through differential expression analysis and may be dysregulated in psoriatic skin. As these networks are comprised mostly of non-coding genes, it is likely that non-coding genes play critical roles in the regulation of pathways involved in the pathogenesis of psoriasis.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 88 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 23%
Student > Master 15 17%
Researcher 14 16%
Student > Doctoral Student 8 9%
Student > Bachelor 5 6%
Other 12 14%
Unknown 14 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 19 22%
Medicine and Dentistry 13 15%
Agricultural and Biological Sciences 12 14%
Immunology and Microbiology 10 11%
Computer Science 4 5%
Other 12 14%
Unknown 18 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 30 November 2016.
All research outputs
#4,267,723
of 23,652,325 outputs
Outputs from BMC Genomics
#1,727
of 10,778 outputs
Outputs of similar age
#69,287
of 315,714 outputs
Outputs of similar age from BMC Genomics
#38
of 224 outputs
Altmetric has tracked 23,652,325 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,778 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 83% 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 315,714 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 77% of its contemporaries.
We're also able to compare this research output to 224 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.