Quantitative modeling and analytic assessment of the transcription dynamics of the XlnR regulon in Aspergillus niger

Background: Transcription of genes coding for xylanolytic and cellulolytic enzymes in Aspergillus 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.</p

Dynamic sporulation gene co-expression networks for Bacillus subtilis 168 and the food-borne isolate Bacillus amyloliquefaciens:a transcriptomic model

Sporulation is a survival strategy, adapted by bacterial cells in response to harsh environmental adversities. The adaptation potential differs between strains and the variations may arise from differences in gene regulation. Gene networks are a valuable way of studying such regulation processes and establishing associations between genes. We reconstructed and compared sporulation gene co-expression networks (GCNs) of the model laboratory strain Bacillus subtilis 168 and the food-borne industrial isolate Bacillus amyloliquefaciens. Transcriptome data obtained from samples of six stages during the sporulation process were used for network inference. Subsequently, a gene set enrichment analysis was performed to compare the reconstructed GCNs of B. subtilis 168 and B. amyloliquefaciens with respect to biological functions, which showed the enriched modules with coherent functional groups associated with sporulation. On basis of the GCNs and time-evolution of differentially expressed genes, we could identify novel candidate genes strongly associated with sporulation in B. subtilis 168 and B. amyloliquefaciens. The GCNs offer a framework for exploring transcription factors, their targets, and co-expressed genes during sporulation. Furthermore, the methodology described here can conveniently be applied to other species or biological processes

Modeling and analysis of the dynamic behavior of the XlnR regulon in Aspergillus niger

Background: In this paper the dynamics of the transcription-translation system for XlnR regulon in Aspergillus niger is modeled. The model is based on Hill regulation functions and uses ordinary differential equations. The network response to a trigger of D-xylose is considered and stability analysis is performed. The activating, repressive feedback, and the combined effect of the two feedbacks on the network behavior are analyzed. Results: Simulation and systems analysis showed significant influence of activating and repressing feedback on metabolite expression profiles. The dynamics of the D-xylose input function has an important effect on the profiles of the individual metabolite concentrations. Variation of the time delay in the feedback loop has no significant effect on the pattern of the response. The stability and existence of oscillatory behavior depends on which proteins are involved in the feedback loop. Conclusions: The dynamics in the regulation properties of the network are dictated mainly by the transcription and translation degradation rate parameters, and by the D-xylose consumption profile. This holds true with and without feedback in the network. Feedback was found to significantly influence the expression dynamics of genes and proteins. Feedback increases the metabolite abundance, changes the steady state values, alters the time trajectories and affects the response oscillatory behavior and stability conditions. The modeling approach provides insight into network behavioral dynamics particularly for small-sized networks. The analysis of the network dynamics has provided useful information for experimental design for future in vitro experimental wor

DNA methylation analysis in plants: review of computational tools and future perspectives

Genome-wide DNA methylation studies have quickly expanded due to advances in next-generation sequencing techniques along with a wealth of computational tools to analyze the data. Most of our knowledge about DNA methylation profiles, epigenetic heritability and the function of DNA methylation in plants derives from the model species Arabidopsis thaliana. There are increasingly many studies on DNA methylation in plants-uncovering methylation profiles and explaining variations in different plant tissues. Additionally, DNA methylation comparisons of different plant tissue types and dynamics during development processes are only slowly emerging but are crucial for understanding developmental and regulatory decisions. Translating this knowledge from plant model species to commercial crops could allow the establishment of new varieties with increased stress resilience and improved yield. In this review, we provide an overview of the most commonly applied bioinformatics tools for the analysis of DNA methylation data (particularly bisulfite sequencing data). The performances of a selection of the tools are analyzed for computational time and agreement in predicted methylated sites for A. thaliana, which has a smaller genome compared to the hexaploid bread wheat. The performance of the tools was benchmarked on five plant genomes. We give examples of applications of DNA methylation data analysis in crops (with a focus on cereals) and an outlook for future developments for DNA methylation status manipulations and data integration

Performance Analysis of Thermal Energy Storage System Integrated with a Cooking Unit

This paper presents an experimental study on a single tank thermal energy storage (TES) system integrated with a cooking unit. The tank had a capacity of 45 L of oil. The cooking chamber was embedded in the storage tank, thereby eliminating the use of pumps and connecting pipes between the cooking unit and the storage unit. The system was designed to make good physical contact, circumferential and basally, with the cooking pot, to improve the rate of heat transfer. Experimental tests were performed with oil only and oil–rock pebbles as sensible heat storage materials. The charging unit was connected to the TES unit in such a way that it allowed circulation of oil between them during charging, using the thermosiphon principle. An electric heater rated at 800 W 240 V was inserted into the charging unit to charge the system. The thermal performance of the TES systems was evaluated in terms of the charging temperature, heat retention capacity, energy stored and cooking efficiency, and the overall heat lost coefficient. The results showed that the oil–rock system performed best, with a cooking efficiency of 64.9%, followed by the oil-only TES system, with 60.3%. Further tests on cooking indicated that the system was able to cook beans in 2.25 h and 2.0 h using the oil only and oil–rock pebbles thermal energy storage systems, respectively

Reconstruction and inference of the Lactococcus lactis MG1363 gene co-expression network

Lactic acid bacteria are Gram-positive bacteria used throughout the world in many industrial applications for their acidification, flavor and texture formation attributes. One of the species, Lactococcus lactis, is employed for the production of fermented milk products like cheese, buttermilk and quark. It ferments lactose to lactic acid and, thus, helps improve the shelf life of the products. Many physiological and transcriptome studies have been performed in L. lactis in order to comprehend and improve its biotechnological assets. Using large amounts of transcriptome data to understand and predict the behavior of biological processes in bacterial or other cell types is a complex task. Gene networks enable predicting gene behavior and function in the context of transcriptionally linked processes. We reconstruct and present the gene co-expression network (GCN) for the most widely studied L. lactis strain, MG1363, using publicly available transcriptome data. Several methods exist to generate and judge the quality of GCNs. Different reconstruction methods lead to networks with varying structural properties, consequently altering gene clusters. We compared the structural properties of the MG1363 GCNs generated by five methods, namely Pearson correlation, Spearman correlation, GeneNet, Weighted Gene Co-expression Network Analysis (WGCNA), and Sparse PArtial Correlation Estimation (SPACE). Using SPACE, we generated an L. lactis MG1363 GCN and assessed its quality using modularity and structural and biological criteria. The L. lactis MG1363 GCN has structural properties similar to those of the gold-standard networks of Escherichia coli K-12 and Bacillus subtilis 168. We showcase that the network can be used to mine for genes with similar expression profiles that are also generally linked to the same biological process

Additional file 6: of Quantitative modeling and analytic assessment of the transcription dynamics of the XlnR regulon in Aspergillus niger

Data for Wt. Data used for the modeling. (ZIP 7 kb

Additional file 2: of Quantitative modeling and analytic assessment of the transcription dynamics of the XlnR regulon in Aspergillus niger

Matlab codes. The Matlab codes used for modeling the Wt. (ZIP 19 kb

Additional file 5: of Quantitative modeling and analytic assessment of the transcription dynamics of the XlnR regulon in Aspergillus niger

Data for Mt. Data used for the modeling. (ZIP 3 kb