Application of text mining to develop AOP-based mucus hypersecretion genesets and confirmation with in vitro and clinical samples

Abstract Mucus hypersecretion contributes to lung function impairment observed in COPD (chronic obstructive pulmonary disease), a tobacco smoking-related disease. A detailed mucus hypersecretion adverse outcome pathway (AOP) has been constructed from literature reviews, experimental and clinical dat...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Emmanuel Minet, Linsey E. Haswell, Sarah Corke, Anisha Banerjee, Andrew Baxter, Ivan Verrastro, Francisco De Abreu e Lima, Tomasz Jaunky, Simone Santopietro, Damien Breheny, Marianna D. Gaça
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/085c7701b32246ae85c6953e73f896e5
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:085c7701b32246ae85c6953e73f896e5
record_format	dspace
spelling	oai:doaj.org-article:085c7701b32246ae85c6953e73f896e52021-12-02T13:17:42ZApplication of text mining to develop AOP-based mucus hypersecretion genesets and confirmation with in vitro and clinical samples10.1038/s41598-021-85345-92045-2322https://doaj.org/article/085c7701b32246ae85c6953e73f896e52021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85345-9https://doaj.org/toc/2045-2322Abstract Mucus hypersecretion contributes to lung function impairment observed in COPD (chronic obstructive pulmonary disease), a tobacco smoking-related disease. A detailed mucus hypersecretion adverse outcome pathway (AOP) has been constructed from literature reviews, experimental and clinical data, mapping key events (KEs) across biological organisational hierarchy leading to an adverse outcome. AOPs can guide the development of biomarkers that are potentially predictive of diseases and support the assessment frameworks of nicotine products including electronic cigarettes. Here, we describe a method employing manual literature curation supported by a focused automated text mining approach to identify genes involved in 5 KEs contributing to decreased lung function observed in tobacco-related COPD. KE genesets were subsequently confirmed by unsupervised clustering against 3 different transcriptomic datasets including (1) in vitro acute cigarette smoke and e-cigarette aerosol exposure, (2) in vitro repeated incubation with IL-13, and (3) lung biopsies from COPD and healthy patients. The 5 KE genesets were demonstrated to be predictive of cigarette smoke exposure and mucus hypersecretion in vitro, and less conclusively predict the COPD status of lung biopsies. In conclusion, using a focused automated text mining and curation approach with experimental and clinical data supports the development of risk assessment strategies utilising AOPs.Emmanuel MinetLinsey E. HaswellSarah CorkeAnisha BanerjeeAndrew BaxterIvan VerrastroFrancisco De Abreu e LimaTomasz JaunkySimone SantopietroDamien BrehenyMarianna D. GaçaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Emmanuel Minet Linsey E. Haswell Sarah Corke Anisha Banerjee Andrew Baxter Ivan Verrastro Francisco De Abreu e Lima Tomasz Jaunky Simone Santopietro Damien Breheny Marianna D. Gaça Application of text mining to develop AOP-based mucus hypersecretion genesets and confirmation with in vitro and clinical samples
description	Abstract Mucus hypersecretion contributes to lung function impairment observed in COPD (chronic obstructive pulmonary disease), a tobacco smoking-related disease. A detailed mucus hypersecretion adverse outcome pathway (AOP) has been constructed from literature reviews, experimental and clinical data, mapping key events (KEs) across biological organisational hierarchy leading to an adverse outcome. AOPs can guide the development of biomarkers that are potentially predictive of diseases and support the assessment frameworks of nicotine products including electronic cigarettes. Here, we describe a method employing manual literature curation supported by a focused automated text mining approach to identify genes involved in 5 KEs contributing to decreased lung function observed in tobacco-related COPD. KE genesets were subsequently confirmed by unsupervised clustering against 3 different transcriptomic datasets including (1) in vitro acute cigarette smoke and e-cigarette aerosol exposure, (2) in vitro repeated incubation with IL-13, and (3) lung biopsies from COPD and healthy patients. The 5 KE genesets were demonstrated to be predictive of cigarette smoke exposure and mucus hypersecretion in vitro, and less conclusively predict the COPD status of lung biopsies. In conclusion, using a focused automated text mining and curation approach with experimental and clinical data supports the development of risk assessment strategies utilising AOPs.
format	article
author	Emmanuel Minet Linsey E. Haswell Sarah Corke Anisha Banerjee Andrew Baxter Ivan Verrastro Francisco De Abreu e Lima Tomasz Jaunky Simone Santopietro Damien Breheny Marianna D. Gaça
author_facet	Emmanuel Minet Linsey E. Haswell Sarah Corke Anisha Banerjee Andrew Baxter Ivan Verrastro Francisco De Abreu e Lima Tomasz Jaunky Simone Santopietro Damien Breheny Marianna D. Gaça
author_sort	Emmanuel Minet
title	Application of text mining to develop AOP-based mucus hypersecretion genesets and confirmation with in vitro and clinical samples
title_short	Application of text mining to develop AOP-based mucus hypersecretion genesets and confirmation with in vitro and clinical samples
title_full	Application of text mining to develop AOP-based mucus hypersecretion genesets and confirmation with in vitro and clinical samples
title_fullStr	Application of text mining to develop AOP-based mucus hypersecretion genesets and confirmation with in vitro and clinical samples
title_full_unstemmed	Application of text mining to develop AOP-based mucus hypersecretion genesets and confirmation with in vitro and clinical samples
title_sort	application of text mining to develop aop-based mucus hypersecretion genesets and confirmation with in vitro and clinical samples
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/085c7701b32246ae85c6953e73f896e5
work_keys_str_mv	AT emmanuelminet applicationoftextminingtodevelopaopbasedmucushypersecretiongenesetsandconfirmationwithinvitroandclinicalsamples AT linseyehaswell applicationoftextminingtodevelopaopbasedmucushypersecretiongenesetsandconfirmationwithinvitroandclinicalsamples AT sarahcorke applicationoftextminingtodevelopaopbasedmucushypersecretiongenesetsandconfirmationwithinvitroandclinicalsamples AT anishabanerjee applicationoftextminingtodevelopaopbasedmucushypersecretiongenesetsandconfirmationwithinvitroandclinicalsamples AT andrewbaxter applicationoftextminingtodevelopaopbasedmucushypersecretiongenesetsandconfirmationwithinvitroandclinicalsamples AT ivanverrastro applicationoftextminingtodevelopaopbasedmucushypersecretiongenesetsandconfirmationwithinvitroandclinicalsamples AT franciscodeabreuelima applicationoftextminingtodevelopaopbasedmucushypersecretiongenesetsandconfirmationwithinvitroandclinicalsamples AT tomaszjaunky applicationoftextminingtodevelopaopbasedmucushypersecretiongenesetsandconfirmationwithinvitroandclinicalsamples AT simonesantopietro applicationoftextminingtodevelopaopbasedmucushypersecretiongenesetsandconfirmationwithinvitroandclinicalsamples AT damienbreheny applicationoftextminingtodevelopaopbasedmucushypersecretiongenesetsandconfirmationwithinvitroandclinicalsamples AT mariannadgaca applicationoftextminingtodevelopaopbasedmucushypersecretiongenesetsandconfirmationwithinvitroandclinicalsamples
_version_	1718393312603275264

Application of text mining to develop AOP-based mucus hypersecretion genesets and confirmation with in vitro and clinical samples

Ejemplares similares