Quantitative modeling and analytic assessment of the transcription dynamics of the XlnR regulon in Aspergillus niger. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Omony, Jimmy; Mach-Aigner, Astrid; van Straten, Gerrit; van Boxtel, Anton
• Is Part Of: BMC systems biology. Volume 10:Issue 1(2016)
• Page Start: 1
• Page End: 10
• Publication Date: 2015-12
• Subjects: Dynamic modeling -- Parameter estimation -- XlnR regulon -- Aspergillus niger -- D-xylose -- CreA; Biological systems -- Periodicals; Biology -- Research -- Periodicals; Cell physiology -- Periodicals; Genes -- Analysis -- Periodicals; 571
• DOI: 10.1186/s12918-016-0257-4
• Languages: English
• ISSNs: 1752-0509

Abstract

Abstract Background Transcription of genes coding for xylanolytic and cellulolytic enzymes inAspergillus niger is controlled by the transactivator XlnR. In this work we analyse and model the transcription dynamics in the XlnR regulon from time-course data of the messenger RNA levels for some XlnR target genes, obtained by reverse transcription quantitative PCR (RT-qPCR). Induction of transcription was achieved using low (1 mM) and high (50 mM) concentrations of D-xylose (Xyl). We investigated the wild type strain (Wt) and a mutant strain with partial loss-of-function of the carbon catabolite repressor CreA (Mt). Results An improved kinetic differential equation model based on two antagonistic Hill functions was proposed, and fitted to the time-course RT-qPCR data from the Wt and the Mt by numerical optimization of the parameters. We show that perturbing the XlnR regulon with Xyl in low and high concentrations results in different expression levels and transcription dynamics of the target genes. At least four distinct transcription profiles were observed, particularly for the usage of 50 mM Xyl. Higher transcript levels were observed for some genes after induction with 1 mM rather than 50 mM Xyl, especially in the Mt. Grouping the expression profiles of the investigated genes has improved our understanding of induction by Xyl and the according regulatory role of CreA. Conclusions The model explains for the higher expression levels at 1 mM versus 50 mM in both Wt and Mt. It does not yet fully encapsulate the effect of partial loss-of-function of CreA in the Mt. The model describes the dynamics in most of the data and elucidates the time-dynamics of the two major regulatory mechanisms: i) the activation by XlnR, and ii) the carbon catabolite repression by CreA.