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Transcriptional landscape of psoriasis identifies the involvement of IL36 and IL36RN

Overview of attention for article published in BMC Genomics, April 2015
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Title
Transcriptional landscape of psoriasis identifies the involvement of IL36 and IL36RN
Published in
BMC Genomics, April 2015
DOI 10.1186/s12864-015-1508-2
Pubmed ID
Authors

Maris Keermann, Sulev Kõks, Ene Reimann, Ele Prans, Kristi Abram, Külli Kingo

Abstract

In present study we performed whole transcriptome analysis in plaque psoriasis patients and compared lesional skin with non-lesional skin and with the skin from healthy controls. We sequenced total RNA from 12 lesional (LP), 12 non-lesional (NLP) and from 12 normal (C) skin biopsies. Compared with previous gene expression profiling studies we had three groups under analysis - LP, NLP and C. Using NLP samples allows to see the transcriptome of visually normal skin from psoriasis patient. In LP skin S100A12, S100A7A, LCE3E, DEFB4A, IL19 were found up regulated. In addition to already these well-described genes, we also found several other genes related to psoriasis. Namely, KLK9, OAS2, OAS3, PLA2G, IL36G, IL36RN were found to be significantly and consistently related to the psoriatic lesions and this finding is supported also by previous studies. The genes up-regulated in the LP samples were related to the innate immunity, IL17 and IL10 networks. In NLP samples innate immunity and IL17 network were activated, but activation of IL10 network was not evident. The transcriptional changes characteristic in the NLP samples can be considered as a molecular signature of "dormant psoriasis". Taken together, our study described the transcriptome profile characteristic for LP and NLP psoriatic skin. RNA profile of the NLP skin is in between the lesional and healthy skin, with its own specific pattern. We found that both LP and NLP have up-regulated IL17 network, whereas LP skin has up regulated IL10 related cytokines (IL19, IL20, IL24). Moreover, IL36G and IL36RN were identified as strong regulators of skin pathology in both LP and NLP skin samples, with stronger influence in LP samples.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 63 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 27%
Student > Bachelor 9 14%
Researcher 8 13%
Student > Doctoral Student 7 11%
Student > Master 7 11%
Other 10 16%
Unknown 5 8%
Readers by discipline Count As %
Medicine and Dentistry 15 24%
Biochemistry, Genetics and Molecular Biology 15 24%
Agricultural and Biological Sciences 9 14%
Immunology and Microbiology 8 13%
Engineering 3 5%
Other 7 11%
Unknown 6 10%
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 23 April 2015.
All research outputs
#18,405,972
of 22,800,560 outputs
Outputs from BMC Genomics
#8,175
of 10,649 outputs
Outputs of similar age
#193,446
of 265,270 outputs
Outputs of similar age from BMC Genomics
#216
of 267 outputs
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