Qualitative modelling and formal verification of the FLR1 gene mancozeb response in Saccharomyces cerevisiae

Qualitative Modeling, Analysis and Control of Synthetic Regulatory Circuits

Qualitative modeling approaches are promising and still underexploited tools for the analysis and design of synthetic circuits. They can make predictions of circuit behavior in the absence of precise, quantitative information. Moreover, they provide direct insight into the relation between the feedback structure and the dynamical properties of a network. We review qualitative modeling approaches by focusing on two specific formalisms, Boolean networks and piecewise-linear differential equations, and illustrate their application by means of three well-known synthetic circuits. We describe various methods for the analysis of state transition graphs, discrete representations of the network dynamics that are generated in both modeling frameworks. We also briefly present the problem of controlling synthetic circuits, an emerging topic that could profit from the capacity of qualitative modeling approaches to rapidly scan a space of design alternatives.

Membrane Proteomics Analysis of the Candida glabrata Response to 5-Flucytosine: Unveiling the Role and Regulation of the Drug Efflux Transporters CgFlr1 and CgFlr2

Resistance to 5-flucytosine (5-FC), used as an antifungal drug in combination therapy, compromises its therapeutic action. In this work, the response of the human pathogen Candida glabrata to 5-FC was evaluated at the membrane proteome level, using an iTRAQ-based approach. A total of 32 proteins were found to display significant expression changes in the membrane fraction of cells upon exposure to 5-FC, 50% of which under the control of CgPdr1, the major regulator of azole drug resistance. These proteins cluster into functional groups associated to cell wall assembly, lipid metabolism, amino acid/nucleotide metabolism, ribosome components and translation machinery, mitochondrial function, glucose metabolism, and multidrug resistance transport. Given the obtained indications, the function of the drug:H+ antiporters CgFlr1 (ORF CAGL0H06017g) and CgFlr2 (ORF CAGL0H06039g) was evaluated. The expression of both proteins, localized to the plasma membrane, was found to confer flucytosine resistance. CgFlr2 further confers azole drug resistance. The deletion of CgFLR1 or CgFLR2 was seen to increase the intracellular accumulation of 5-FC, or 5-FC and clotrimazole, suggesting that these transporters play direct roles in drug extrusion. The expression of CgFLR1 and CgFLR2 was found to be controlled by the transcription factors CgPdr1 and CgYap1, major regulator of oxidative stress resistance.

Piecewise linear approximations to model the dynamics of adaptation to osmotic stress by food-borne pathogens

Addition of salt to food is one of the most ancient and most common methods of food preservation. However, little is known of how bacterial cells adapt to such conditions. We propose to use piecewise linear approximations to model the regulatory adaptation of Escherichiacoli to osmotic stress. We apply the method to eight selected genes representing the functions known to be at play during osmotic adaptation. The network is centred on the general stress response factor, sigma S, and also includes a module representing the catabolic repressor CRP-cAMP. Glutamate, potassium and supercoiling are combined to represent the intracellular regulatory signal during osmotic stress induced by salt. The output is a module where growth is represented by the concentration of stable RNAs and the transcription of the osmotic gene osmY. The time course of gene expression of transport of osmoprotectant represented by the symporter proP and of the osmY is successfully reproduced by the network. The behaviour of the rpoS mutant predicted by the model is in agreement with experimental data. We discuss the application of the model to food-borne pathogens such as Salmonella; although the genes considered have orthologs, it seems that supercoiling is not regulated in the same way. The model is limited to a few selected genes, but the regulatory interactions are numerous and span different time scales. In addition, they seem to be condition specific: the links that are important during the transition from exponential to stationary phase are not all needed during osmotic stress. This model is one of the first steps towards modelling adaptation to stress in food safety and has scope to be extended to other genes and pathways, other stresses relevant to the food industry, and food-borne pathogens. The method offers a good compromise between systems of ordinary differential equations, which would be unmanageable because of the size of the system and for which insufficient data are available, and the more abstract Boolean methods.

Yeast toxicogenomics: genome-wide responses to chemical stresses with impact in environmental health, pharmacology, and biotechnology

The emerging transdisciplinary field of Toxicogenomics aims to study the cell response to a given toxicant at the genome, transcriptome, proteome, and metabolome levels. This approach is expected to provide earlier and more sensitive biomarkers of toxicological responses and help in the delineation of regulatory risk assessment. The use of model organisms to gather such genomic information, through the exploitation of Omics and Bioinformatics approaches and tools, together with more focused molecular and cellular biology studies are rapidly increasing our understanding and providing an integrative view on how cells interact with their environment. The use of the model eukaryote Saccharomyces cerevisiae in the field of Toxicogenomics is discussed in this review. Despite the limitations intrinsic to the use of such a simple single cell experimental model, S. cerevisiae appears to be very useful as a first screening tool, limiting the use of animal models. Moreover, it is also one of the most interesting systems to obtain a truly global understanding of the toxicological response and resistance mechanisms, being in the frontline of systems biology research and developments. The impact of the knowledge gathered in the yeast model, through the use of Toxicogenomics approaches, is highlighted here by its use in prediction of toxicological outcomes of exposure to pesticides and pharmaceutical drugs, but also by its impact in biotechnology, namely in the development of more robust crops and in the improvement of yeast strains as cell factories.