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A transcriptome-driven analysis of epithelial brushings and bronchial biopsies to define asthma phenotypes in U-BIOPRED

Kuo, Chih-Hsi Scott (author)
Pavlidis, Stelios (author)
Loza, Matthew (author)
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Baribaud, Fred (author)
Rowe, Anthony (author)
Pandis, Ioannis (author)
Hoda, Uruj (author)
Rossios, Christos (author)
Sousa, Ana (author)
Wilson, Susan J (author)
Howarth, Peter (author)
Dahlen, Barbro (author)
Karolinska Institutet
Dahlen, Sven-Erik (author)
Karolinska Institutet
Chanez, Pascal (author)
Shaw, Dominick (author)
Krug, Norbert (author)
Sandström, Thomas (author)
Umeå universitet,Medicin
De Meulder, Bertrand (author)
Lefaudeux, Diane (author)
Fowler, Stephen (author)
Fleming, Louise (author)
Corfield, Julie (author)
Auffray, Charles (author)
Sterk, Peter J (author)
Djukanovic, Ratko (author)
Guo, Yike (author)
Adcock, Ian M (author)
Chung, Kian Fan (author)
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 (creator_code:org_t)
2017
2017
English.
In: American Journal of Respiratory and Critical Care Medicine. - 1073-449X .- 1535-4970. ; 194:4, s. 443-455
Related links:
https://urn.kb.se/re...
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  • Journal article (peer-reviewed)
Abstract Subject headings
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  • RATIONALE AND OBJECTIVES: Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms. We used transcriptomic profiling of airway tissues to help define asthma phenotypes.METHODS: The transcriptome from bronchial biopsies and epithelial brushings of 107 moderate-to-severe asthmatics were annotated by gene-set variation analysis (GSVA) using 42 gene-signatures relevant to asthma, inflammation and immune function. Topological data analysis (TDA) of clinical and histological data was used to derive clusters and the nearest shrunken centroid algorithm used for signature refinement.RESULTS: 9 GSVA signatures expressed in bronchial biopsies and airway epithelial brushings distinguished two distinct asthma subtypes associated with high expression of T-helper type 2 (Th-2) cytokines and lack of corticosteroid response (Group 1 and Group 3). Group 1 had the highest submucosal eosinophils, high exhaled nitric oxide (FeNO) levels, exacerbation rates and oral corticosteroid (OCS) use whilst Group 3 patients showed the highest levels of sputum eosinophils and had a high BMI. In contrast, Group 2 and Group 4 patients had an 86% and 64% probability of having non-eosinophilic inflammation. Using machine-learning tools, we describe an inference scheme using the currently-available inflammatory biomarkers sputum eosinophilia and exhaled nitric oxide levels along with OCS use that could predict the subtypes of gene expression within bronchial biopsies and epithelial cells with good sensitivity and specificity.CONCLUSION: This analysis demonstrates the usefulness of a transcriptomic-driven approach to phenotyping that segments patients who may benefit the most from specific agents that target Th2-mediated inflammation and/or corticosteroid insensitivity.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Lungmedicin och allergi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Respiratory Medicine and Allergy (hsv//eng)

Keyword

severe asthma
corticosteroid insensitivity
T-helper type 2
exhaled nitric oxide
gene set variation analysis

Publication and Content Type

ref (subject category)
art (subject category)

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