Gln3p Nuclear Localization and Interaction with Ure2p inSaccharomyces cerevisiae

156

149

The [URE3] infectious protein (prion) of Saccharomyces cerevisiae is a self-propagating amyloid form of Ure2p. The C-terminal domain of Ure2p controls nitrogen catabolism by complexing with the transcription factor, Gln3p, whereas the asparagine-rich Nterminal ''prion'' domain is responsible for amyloid filament formation (prion conversion). On filament formation, Ure2p is inactivated, reflecting either a structural change in the C-terminal domain or steric blocking of its interaction with Gln3p. We fused the prion domain with four proteins whose activities should not be sterically impeded by aggregation because their substrates are very small: barnase, carbonic anhydrase, glutathione S-transferase, and green fluorescent protein. All formed amyloid filaments in vitro, whose diameters increased with the mass of the appended enzyme. The helical repeat lengths were consistent within a single filament but varied with the construct and between filaments from a single construct. CD data suggest that, in the soluble fusion proteins, the prion domain has no regular secondary structure, whereas earlier data showed that in filaments, it is virtually all ␤-sheet. In filaments, the activity of the appended proteins was at most mildly reduced, when substrate diffusion effects were taken into account, indicating that they retained their native structures. These observations suggest that the amyloid content of these filaments is confined to their prion domain-containing backbones and imply that Ure2p is inactivated in [URE3] cells by a steric blocking mechanism.

Section: Amyloid Filament Formation By Ure2p Chimeras Involves Major supporting

confidence: 65%

Mechanism of inactivation on prion conversion of the Saccharomyces cerevisiae Ure2 protein

Baxa

Speransky

Steven

et al. 2002

156

149

“…Structure-function analyses of Gln3 have identified specific sequences for nuclear localization, nuclear export, and interaction with Ure2 and Tor1 (26,27). We find that a fraction of Ure2, expressed from its endogenous locus or from a low-copy centromeric plasmid to improve detection, also is tightly associated with light membranes and resistant to salt extraction (Fig.…”

Section: A Fraction Of Gln3 Associates With Light Membranes Via a Nonmentioning

confidence: 94%

Nuclear translocation of Gln3 in response to nutrient signals requires Golgi-to-endosome trafficking in Saccharomyces cerevisiae

Puria

Zurita-Martinez

Cárdenas

2008

The yeast Saccharomyces cerevisiae has developed specialized mechanisms that enable growth on suboptimal nitrogen sources. Exposure of yeast cells to poor nitrogen sources or treatment with the Tor kinase inhibitor rapamycin elicits activation of Gln3 and transcription of nitrogen catabolite-repressed (NCR) genes whose products function in scavenging and metabolizing nitrogen. Here, we show that mutations in class C and D Vps components, which mediate Golgi-to-endosome vesicle transport, impair nuclear translocation of Gln3, NCR gene activation, and growth in poor nitrogen sources. In nutrient-replete conditions, a significant fraction of Gln3 is peripherally associated with light membranes and partially colocalizes with Vps10-containing foci. These results reveal a role for Golgi-to-endosome vesicular trafficking in TORC1-controlled nuclear translocation of Gln3 and support a model in which Tor-mediated signaling in response to nutrient cues occurs in these compartments. These findings have important implications for nutrient sensing and growth control via mTor pathways in metazoans.rapamycin action ͉ Tor signaling

“…Intuitively we may expect a positive correlation between the two profiles, but this is not the case; very little similarity exists between the two expression profiles. Perhaps this merely reflects the complexity due to many cis elements and trans-acting factors for CAR1 (14), the localization of Gln3p, and its interactions with other factors (15). To apply the LA methodology, we take (GLN3,CAR1) as the pair (X,Y) in Fig.…”

Section: Resultsmentioning

confidence: 99%

Genome-wide coexpression dynamics: Theory and application

2002

167

223

High-throughput expression profiling enables the global study of gene activities. Genes with positively correlated expression profiles are likely to encode functionally related proteins. However, all biological processes are interlocked, and each protein may play multiple cellular roles. Thus the coexpression of any two functionally related genes may depend on the constantly varying, yet often-unknown cellular state. To initiate a systematic study on this issue, a theory of coexpression dynamics is presented. This theory is used to rationalize a strategy of conducting a genome-wide search for the most critical cellular players that may affect the coexpression pattern of any two genes. In one example, using a yeast data set, our method reveals how the enzymes associated with the urea cycle are expressed to ensure proper mass flow of the involved metabolites. The correlation between ARG2 and CAR2 is found to change from positive to negative as the expression level of CPA2 increases. This delicate interplay in correlation signifies a remarkable control on the influx and efflux of ornithine and reflects well the intrinsic cellular demand for arginine. In addition to the urea cycle, our examples include SCH9 and CYR1 (both implicated in a recent longevity study), cytochrome c1 (mitochondrial electron transport), calmodulin (main calcium-binding protein), PFK1 and PFK2 (glycolysis), and two genes, ECM1 and YNL101W, the functions of which are newly revealed. The complexity in computation is eased by a new result from mathematical statistics.gene expression ͉ microarray ͉ urea cycle ͉ correlation ͉ glycolysis M icroarrays have generated an enormous amount of geneexpression data from a variety of biological studies (1-5). After proper preprocessing, the data can be stored as a matrix of real numbers with N rows and m columns. Rows represent the gene-expression profiles, and columns represent the cell types, time points, or environmental or other experimental conditions under which the mRNA samples are taken. To elucidate microarray data, most methods (6-10) rely on the notion of profile similarity as described for Fig. 1. It is argued that coexpressed genes are likely to encode proteins that participate in a common structural complex, metabolic pathway, or biological process (6, 10).Despite the many successful applications reported in the literature, there is an important issue that is hard to address by profile-similarity analysis. As is known, all biological processes are interlocked, and many proteins have multiple cellular roles. Two proteins engaged in a common process under certain conditions may disengage and embark on activities of their own under other conditions, which implies that both the strength and pattern of association between two gene profiles may vary as the intrinsic cellular-state changes. Weaver (11) discussed two transcription factors, Max and thyroid hormone receptor (TR). They can serve either as activators or repressors depending on other molecules bound to them. Max can bind to Myc and form a M...