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dc.contributor.author
Michaelson, Jacob J.
dc.contributor.author
Trump, Saskia
dc.contributor.author
Rudzok, Susanne
dc.contributor.author
Gräbsch, Carolin
dc.contributor.author
Madureira, Danielle J.
dc.contributor.author
Dautel, Franziska
dc.contributor.author
Mai, Juliane
dc.contributor.author
Attinger, Sabine
dc.contributor.author
Schirmer, Kristin
dc.contributor.author
von Bergen, Martin
dc.contributor.author
Lehmann, Irina
dc.contributor.author
Beyer, Andreas
dc.date.accessioned
2018-10-01T12:28:57Z
dc.date.available
2017-06-09T16:55:58Z
dc.date.available
2018-10-01T12:28:57Z
dc.date.issued
2011-10
dc.identifier.other
10.1186/1471-2164-12-502
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/40409
dc.identifier.doi
10.3929/ethz-b-000040409
dc.description.abstract
Background Small molecule ligands often have multiple effects on the transcriptional program of a cell: they trigger a receptor specific response and additional, indirect responses ("side effects"). Distinguishing those responses is important for understanding side effects of drugs and for elucidating molecular mechanisms of toxic chemicals. Results We explored this problem by exposing cells to the environmental contaminant benzo-[a]-pyrene (B[a]P). B[a]P exposure activates the aryl hydrocarbon receptor (Ahr) and causes toxic stress resulting in transcriptional changes that are not regulated through Ahr. We sought to distinguish these two types of responses based on a time course of expression changes measured after B[a]P exposure. Using Random Forest machine learning we classified 81 primary Ahr responders and 1,308 genes regulated as side effects. Subsequent weighted clustering gave further insight into the connection between expression pattern, mode of regulation, and biological function. Finally, the accuracy of the predictions was supported through extensive experimental validation. Conclusion Using a combination of machine learning followed by extensive experimental validation, we have further expanded the known catalog of genes regulated by the environmentally sensitive transcription factor Ahr. More broadly, this study presents a strategy for distinguishing receptor-dependent responses and side effects based on expression time courses.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/2.0/
dc.subject
Random Forest
en_US
dc.subject
TCDD
en_US
dc.subject
Transcriptional Response
en_US
dc.subject
Weighted Cluster
en_US
dc.subject
TCDD Exposure
en_US
dc.title
Transcriptional signatures of regulatory and toxic responses to benzo-[a]-pyrene exposure
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 2.0 Generic
ethz.journal.title
BMC Genomics
ethz.journal.volume
12
en_US
ethz.pages.start
502
en_US
ethz.size
14 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
004256340
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2017-06-09T16:56:08Z
ethz.source
ECIT
ethz.identifier.importid
imp59364e94e0b1c91554
ethz.ecitpid
pub:67687
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-14T12:23:50Z
ethz.rosetta.lastUpdated
2020-02-15T15:14:18Z
ethz.rosetta.versionExported
true
ethz.COinS
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