Network inference algorithms elucidate Nrf2 regulation of mouse lung oxidative stress.

A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods. Nuclear factor erythroid 2-related factor (Nrf2) regulates the transcription of...

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Autores principales:	Ronald C Taylor, George Acquaah-Mensah, Mudita Singhal, Deepti Malhotra, Shyam Biswal
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2008
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/77534884b7534c5c82072850c667bf15
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spelling	oai:doaj.org-article:77534884b7534c5c82072850c667bf152021-11-25T05:42:00ZNetwork inference algorithms elucidate Nrf2 regulation of mouse lung oxidative stress.1553-734X1553-735810.1371/journal.pcbi.1000166https://doaj.org/article/77534884b7534c5c82072850c667bf152008-08-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18769717/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods. Nuclear factor erythroid 2-related factor (Nrf2) regulates the transcription of several gene products involved in the protective response to oxidative stress. The transcriptional regulatory and signaling relationships linking gene products involved in the response to oxidative stress are, currently, only partially resolved. Microarray data constitute RNA abundance measures representing gene expression patterns. In some cases, these patterns can identify the molecular interactions of gene products. They can be, in effect, proxies for protein-protein and protein-DNA interactions. Traditional techniques used for clustering coregulated genes on high-throughput gene arrays are rarely capable of distinguishing between direct transcriptional regulatory interactions and indirect ones. In this study, newly developed information-theoretic algorithms that employ the concept of mutual information were used: the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Context Likelihood of Relatedness (CLR). These algorithms captured dependencies in the gene expression profiles of the mouse lung, allowing the regulatory effect of Nrf2 in response to oxidative stress to be determined more precisely. In addition, a characterization of promoter sequences of Nrf2 regulatory targets was conducted using a Support Vector Machine classification algorithm to corroborate ARACNE and CLR predictions. Inferred networks were analyzed, compared, and integrated using the Collective Analysis of Biological Interaction Networks (CABIN) plug-in of Cytoscape. Using the two network inference algorithms and one machine learning algorithm, a number of both previously known and novel targets of Nrf2 transcriptional activation were identified. Genes predicted as novel Nrf2 targets include Atf1, Srxn1, Prnp, Sod2, Als2, Nfkbib, and Ppp1r15b. Furthermore, microarray and quantitative RT-PCR experiments following cigarette-smoke-induced oxidative stress in Nrf2(+/+) and Nrf2(-/-) mouse lung affirmed many of the predictions made. Several new potential feed-forward regulatory loops involving Nrf2, Nqo1, Srxn1, Prdx1, Als2, Atf1, Sod1, and Park7 were predicted. This work shows the promise of network inference algorithms operating on high-throughput gene expression data in identifying transcriptional regulatory and other signaling relationships implicated in mammalian disease.Ronald C TaylorGeorge Acquaah-MensahMudita SinghalDeepti MalhotraShyam BiswalPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 4, Iss 8, p e1000166 (2008)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Ronald C Taylor George Acquaah-Mensah Mudita Singhal Deepti Malhotra Shyam Biswal Network inference algorithms elucidate Nrf2 regulation of mouse lung oxidative stress.
description	A variety of cardiovascular, neurological, and neoplastic conditions have been associated with oxidative stress, i.e., conditions under which levels of reactive oxygen species (ROS) are elevated over significant periods. Nuclear factor erythroid 2-related factor (Nrf2) regulates the transcription of several gene products involved in the protective response to oxidative stress. The transcriptional regulatory and signaling relationships linking gene products involved in the response to oxidative stress are, currently, only partially resolved. Microarray data constitute RNA abundance measures representing gene expression patterns. In some cases, these patterns can identify the molecular interactions of gene products. They can be, in effect, proxies for protein-protein and protein-DNA interactions. Traditional techniques used for clustering coregulated genes on high-throughput gene arrays are rarely capable of distinguishing between direct transcriptional regulatory interactions and indirect ones. In this study, newly developed information-theoretic algorithms that employ the concept of mutual information were used: the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Context Likelihood of Relatedness (CLR). These algorithms captured dependencies in the gene expression profiles of the mouse lung, allowing the regulatory effect of Nrf2 in response to oxidative stress to be determined more precisely. In addition, a characterization of promoter sequences of Nrf2 regulatory targets was conducted using a Support Vector Machine classification algorithm to corroborate ARACNE and CLR predictions. Inferred networks were analyzed, compared, and integrated using the Collective Analysis of Biological Interaction Networks (CABIN) plug-in of Cytoscape. Using the two network inference algorithms and one machine learning algorithm, a number of both previously known and novel targets of Nrf2 transcriptional activation were identified. Genes predicted as novel Nrf2 targets include Atf1, Srxn1, Prnp, Sod2, Als2, Nfkbib, and Ppp1r15b. Furthermore, microarray and quantitative RT-PCR experiments following cigarette-smoke-induced oxidative stress in Nrf2(+/+) and Nrf2(-/-) mouse lung affirmed many of the predictions made. Several new potential feed-forward regulatory loops involving Nrf2, Nqo1, Srxn1, Prdx1, Als2, Atf1, Sod1, and Park7 were predicted. This work shows the promise of network inference algorithms operating on high-throughput gene expression data in identifying transcriptional regulatory and other signaling relationships implicated in mammalian disease.
format	article
author	Ronald C Taylor George Acquaah-Mensah Mudita Singhal Deepti Malhotra Shyam Biswal
author_facet	Ronald C Taylor George Acquaah-Mensah Mudita Singhal Deepti Malhotra Shyam Biswal
author_sort	Ronald C Taylor
title	Network inference algorithms elucidate Nrf2 regulation of mouse lung oxidative stress.
title_short	Network inference algorithms elucidate Nrf2 regulation of mouse lung oxidative stress.
title_full	Network inference algorithms elucidate Nrf2 regulation of mouse lung oxidative stress.
title_fullStr	Network inference algorithms elucidate Nrf2 regulation of mouse lung oxidative stress.
title_full_unstemmed	Network inference algorithms elucidate Nrf2 regulation of mouse lung oxidative stress.
title_sort	network inference algorithms elucidate nrf2 regulation of mouse lung oxidative stress.
publisher	Public Library of Science (PLoS)
publishDate	2008
url	https://doaj.org/article/77534884b7534c5c82072850c667bf15
work_keys_str_mv	AT ronaldctaylor networkinferencealgorithmselucidatenrf2regulationofmouselungoxidativestress AT georgeacquaahmensah networkinferencealgorithmselucidatenrf2regulationofmouselungoxidativestress AT muditasinghal networkinferencealgorithmselucidatenrf2regulationofmouselungoxidativestress AT deeptimalhotra networkinferencealgorithmselucidatenrf2regulationofmouselungoxidativestress AT shyambiswal networkinferencealgorithmselucidatenrf2regulationofmouselungoxidativestress
_version_	1718414524408659968

Network inference algorithms elucidate Nrf2 regulation of mouse lung oxidative stress.

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