IMR Press / FBS / Volume 4 / Issue 4 / DOI: 10.2741/S352

Frontiers in Bioscience-Scholar (FBS) is published by IMR Press from Volume 13 Issue 1 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on as a courtesy and upon agreement with Frontiers in Bioscience.

Transcriptomic analysis reveals pH-responsive antioxidant gene networks
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1 Unidade de Bioinformatica, Centro de Estudos em Estresse Oxidativo, Departamento de Bioquimica, Universidade Federal do Rio Grande do Sul (UFRGS), Rua Ramiro Barcelos 2600-anexo, Porto Alegre 90035-003, Brazil

*Author to whom correspondence should be addressed.

Academic Editor: Mauro Castro

Front. Biosci. (Schol Ed) 2012, 4(4), 1556–1567;
Published: 1 June 2012
(This article belongs to the Special Issue Gene networks, genome instability and evolutionary dynamics of cancer)

Reactive oxygen species (ROS) are produced in different physiological conditions. In response to ROS imbalance cells activate oxidative stress defenses, which include more than 60 antioxidant genes. It has been suggested that gene products associated with ROS detoxification can work coordinately, acting as an antioxidant-defense network. However, the functional overlap among oxidative stress defenses and other related cell functions makes difficult the characterization of this network. We previously described a network-based model to characterize the interactions existing among different antioxidant gene products and their substrates. Here, we test whether this network-based model of human antioxidant genes can respond to different physiological conditions. We used a systems biology approach applied to the analysis of two independent gene expression datasets: transcriptomes from HeLa cells and primary astrocytes maintained under hypoxic conditions and transcriptomes from SKGT4 cells exposed to low pH environment. We found that the proposed gene network model responds selectively to both hypoxia and acidosis. We anticipate that this antioxidant gene network model can be helpful to describe stress-responsive expression profiles in different cell types.

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