Transcriptional regulators of seven yeast species: Comparative genome analysis — Review

Pullulan biosynthesis in yeast-like fungal cells is regulated by the transcriptional activator Msn2 and cAMP-PKA signaling pathway

Pullulan is an important polysaccharide. Although its synthetic pathway in Aureobasidium melanogenum has been elucidated, the mechanism underlying its biosynthesis as regulated by signaling pathway and transcriptional regulator is still unknown. In this study, it was found that the expression of the UGP1 gene encoding UDPG-pyrophosphorylase (Ugp1) and other genes which were involved in pullulan biosynthesis was controlled by the transcriptional activator Msn2 in the nuclei of yeast-like fungal cells. The Ugp1 was a rate-limiting enzyme for pullulan biosynthesis. In addition, the activity and subcellular localization of the Msn2 were regulated only by the cAMP-PKA signaling pathway. When the cAMP-PKA activity was low, the Msn2 was localized in the nuclei, the UGP1 gene was highly expressed, and pullulan was actively synthesized. By contrast, when the cAMP-PKA activity was high, the Msn2 was localized in the cytoplasm and the UGP1 gene expression was disabled so that pullulan was stopped, but lipid biosynthesis was actively enhanced. This study was the first to report that pullulan and lipid biosynthesis in yeast-like fungal cells were regulated by the Msn2 and cAMP-PKA signaling pathway. Elucidating the regulation mechanisms was important to understand their functions and enhance pullulan and lipid biosynthesis.

Mutation of the CgPDR16 gene attenuates azole tolerance and biofilm production in pathogenic Candida glabrata

The PDR16 gene encodes the homologue of Sec14p, participating in protein secretion, regulation of lipid synthesis and turnover in vivo and acting as a phosphatidylinositol transfer protein in vitro. This gene is also involved in the regulation of multidrug resistance in Saccharomyces cerevisiae and pathogenic yeasts. Here we report the results of functional analysis of the CgPDR16 gene, whose mutation has been previously shown to enhance fluconazole sensitivity in Candida glabrata mutant cells. We have cloned the CgPDR16 gene, which was able to complement the pdr16Δ mutation in both C. glabrata and S. cerevisiae. Along with fluconazole, the pdr16Δ mutation resulted in increased susceptibility of mutant cells to several azole antifungals without changes in sensitivity to polyene antibiotics, cycloheximide, NQO, 5-fluorocytosine and oxidants inducing the intracellular formation of reactive oxygen species. The susceptibility of the pdr16Δ mutant strain to itraconazole and 5-fluorocytosine was enhanced by CTBT [7-chlorotetrazolo(5,1-c)benzo(1,2,4)triazine] inducing oxidative stress. The pdr16Δ mutation increased the accumulation of rhodamine 6G in mutant cells, decreased the level of itraconazole resistance caused by gain-of-function mutations in the CgPDR1 gene, and reduced cell surface hydrophobicity and biofilm production. These results point to the pleiotropic phenotype of the pdr16Δ mutant and support the role of the CgPDR16 gene in the control of drug susceptibility and virulence in the pathogenic C. glabrata.

Transcriptional regulation in Saccharomyces cerevisiae: transcription factor regulation and function, mechanisms of initiation, and roles of activators and coactivators

Here we review recent advances in understanding the regulation of mRNA synthesis in Saccharomyces cerevisiae. Many fundamental gene regulatory mechanisms have been conserved in all eukaryotes, and budding yeast has been at the forefront in the discovery and dissection of these conserved mechanisms. Topics covered include upstream activation sequence and promoter structure, transcription factor classification, and examples of regulated transcription factor activity. We also examine advances in understanding the RNA polymerase II transcription machinery, conserved coactivator complexes, transcription activation domains, and the cooperation of these factors in gene regulatory mechanisms.

Autoactivated KlPDR1 gene in the control of multidrug resistance in Kluyveromyces lactis

The KlPDR1 gene encodes a zinc finger transcription factor that has recently been shown to be involved in the control of multidrug resistance of Kluyveromyces lactis . In this work, we provide evidence that the K. lactis KlPDR1 gene is under positive autoregulation by KlPdr1p, which plays a role in the activation of the main multidrug resistance transporter gene KlPDR5. Electrophoretic mobility shift assays, as well as the use of gusA reporter constructs, enabled us to identify the 5'-tataTCCGGGTAactt-3' sequence motif in the KlPDR1 promoter (in the position -326 to -319 bp) as the PDRE (pleiotropic drug responsive element) for the binding of KlPdr1p. The drug sensitivity of Klpdr1Δ mutant cells was complemented by introducing the plasmid-born KlPDR1 gene. The KlPdr1p activated the expression of the P(KlPDR1)-gusA fusion gene, and the expression of the KlPDR1 gene was induced by fluconazole. The PDRE was also found in the promoter of KlPDR5, a gene encoding the ATP-dependent efflux pump responsible for the drug resistance phenomenon in K. lactis.

Emergence of switch-like behavior in a large family of simple biochemical networks

Bistability plays a central role in the gene regulatory networks (GRNs) controlling many essential biological functions, including cellular differentiation and cell cycle control. However, establishing the network topologies that can exhibit bistability remains a challenge, in part due to the exceedingly large variety of GRNs that exist for even a small number of components. We begin to address this problem by employing chemical reaction network theory in a comprehensive in silico survey to determine the capacity for bistability of more than 40,000 simple networks that can be formed by two transcription factor-coding genes and their associated proteins (assuming only the most elementary biochemical processes). We find that there exist reaction rate constants leading to bistability in ∼90% of these GRN models, including several circuits that do not contain any of the TF cooperativity commonly associated with bistable systems, and the majority of which could only be identified as bistable through an original subnetwork-based analysis. A topological sorting of the two-gene family of networks based on the presence or absence of biochemical reactions reveals eleven minimal bistable networks (i.e., bistable networks that do not contain within them a smaller bistable subnetwork). The large number of previously unknown bistable network topologies suggests that the capacity for switch-like behavior in GRNs arises with relative ease and is not easily lost through network evolution. To highlight the relevance of the systematic application of CRNT to bistable network identification in real biological systems, we integrated publicly available protein-protein interaction, protein-DNA interaction, and gene expression data from Saccharomyces cerevisiae, and identified several GRNs predicted to behave in a bistable fashion.

Switch-like behavior is found across a wide range of biological systems, and as a result there is significant interest in identifying the various ways in which biochemical reactions can be combined to yield a switch-like response. In this work we use a set of mathematical tools from chemical reaction network theory that provide information about the steady-states of a reaction network irrespective of the values of network rate constants, to conduct a large computational study of a family of model networks consisting of only two protein-coding genes. We find that a large majority of these networks (∼90%) have (for some set of parameters) the mathematical property known as bistability and can behave in a switch-like manner. Interestingly, the capacity for switch-like behavior is often maintained as networks increase in size through the introduction of new reactions. We then demonstrate using published yeast data how theoretical parameter-free surveys such as this one can be used to discover possible switch-like circuits in real biological systems. Our results highlight the potential usefulness of parameter-free modeling for the characterization of complex networks and to the study of network evolution, and are suggestive of a role for it in the development of novel synthetic biological switches.