Transcriptome-Based Modeling Reveals that Oxidative Stress Induces Modulation of the AtfA-Dependent Signaling Networks in Aspergillus nidulans.
Erzsébet OroszKároly AntalZoltán GazdagZsuzsa SzabóKap-Hoon HanJae-Hyuk YuIstván PócsiTamás EmriPublished in: International journal of genomics (2017)
To better understand the molecular functions of the master stress-response regulator AtfA in Aspergillus nidulans, transcriptomic analyses of the atfA null mutant and the appropriate control strains exposed to menadione sodium bisulfite- (MSB-), t-butylhydroperoxide- and diamide-induced oxidative stresses were performed. Several elements of oxidative stress response were differentially expressed. Many of them, including the downregulation of the mitotic cell cycle, as the MSB stress-specific upregulation of FeS cluster assembly and the MSB stress-specific downregulation of nitrate reduction, tricarboxylic acid cycle, and ER to Golgi vesicle-mediated transport, showed AtfA dependence. To elucidate the potential global regulatory role of AtfA governing expression of a high number of genes with very versatile biological functions, we devised a model based on the comprehensive transcriptomic data. Our model suggests that an important function of AtfA is to modulate the transduction of stress signals. Although it may regulate directly only a limited number of genes, these include elements of the signaling network, for example, members of the two-component signal transduction systems. AtfA acts in a stress-specific manner, which may increase further the number and diversity of AtfA-dependent genes. Our model sheds light on the versatility of the physiological functions of AtfA and its orthologs in fungi.
Keyphrases
- cell cycle
- cell proliferation
- genome wide
- oxidative stress
- signaling pathway
- stress induced
- rna seq
- transcription factor
- diabetic rats
- genome wide identification
- gene expression
- nitric oxide
- dna damage
- bioinformatics analysis
- genome wide analysis
- heat stress
- dna methylation
- drinking water
- high glucose
- electronic health record
- climate change
- cell wall
- heat shock
- machine learning