Literature DB >> 26518187

Development of an in vitro test to identify respiratory sensitizers in bronchial epithelial cells using gene expression profiling.

Sander Dik1, Jeroen L A Pennings2, Henk van Loveren1, Janine Ezendam3.   

Abstract

Chemicals that induce asthma at the workplace are substances of concern. At present, there are no widely accepted methods to identify respiratory sensitizers, and classification of these substances is based on human occupational data. Several studies have contributed to understanding the mechanisms involved in respiratory sensitization, although uncertainties remain. One point of interest for respiratory sensitization is the reaction of the epithelial lung barrier to respiratory sensitizers. To elucidate potential molecular effects of exposure of the epithelial lung barrier, a gene expression profile was created based on a DNA microarray experiment using the bronchial epithelial cell line 16 HBE14o(-). The cells were exposed to 12 respiratory sensitizers and 10 non-sensitizers. For statistical analysis, we used a class prediction approach that combined three machine learning algorithms, leave-one-compound-out cross validation, and majority voting per tested compound. This approach allowed for a prediction accuracy of 95%. Identified predictive genes were mainly associated with the cytoskeleton and barrier function of the epithelial cell. Several of these genes were reported to be associated with asthma as well. Taken together, this indicates that pulmonary barrier function is an important target for respiratory sensitizers and associated genes can be used to predict the respiratory sensitization potential of chemicals.
Copyright © 2015 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Epithelial cells; Gene expression; Occupational asthma; Pulmonary barrier function; Respiratory sensitization

Mesh:

Substances:

Year:  2015        PMID: 26518187     DOI: 10.1016/j.tiv.2015.10.010

Source DB:  PubMed          Journal:  Toxicol In Vitro        ISSN: 0887-2333            Impact factor:   3.500


  3 in total

1.  Modeling and insights into molecular basis of low molecular weight respiratory sensitizers.

Authors:  Xueyan Cui; Rui Yang; Siwen Li; Juan Liu; Qiuyun Wu; Xiao Li
Journal:  Mol Divers       Date:  2020-03-12       Impact factor: 2.943

2.  Chemical-induced asthma and the role of clinical, toxicological, exposure and epidemiological research in regulatory and hazard characterization approaches.

Authors:  Melissa J Vincent; Jonathan A Bernstein; David Basketter; Judy S LaKind; G Scott Dotson; Andrew Maier
Journal:  Regul Toxicol Pharmacol       Date:  2017-09-01       Impact factor: 3.271

Review 3.  In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts.

Authors:  Hongbin Yang; Lixia Sun; Weihua Li; Guixia Liu; Yun Tang
Journal:  Front Chem       Date:  2018-02-20       Impact factor: 5.221

  3 in total

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