The yeast protein kinase Sch9 adjusts V-ATPase assembly/disassembly to control pH homeostasis and longevity in response to glucose availability

The evolutionary conserved TOR complex 1 controls growth in response to the quality and quantity of nutrients such as carbon and amino acids. The protein kinase Sch9 is the main TORC1 effector in yeast. However, only few of its direct targets are known. In this study, we performed a genome-wide screening looking for mutants which require Sch9 function for their survival and growth. In this way, we identified multiple components of the highly conserved vacuolar proton pump (V-ATPase) which mediates the luminal acidification of multiple biosynthetic and endocytic organelles. Besides a genetic interaction, we found Sch9 also physically interacts with the V- ATPase to regulate its assembly state in response to glucose availability and TORC1 activity. Moreover, the interaction with the V-ATPase has consequences for ageing as it allowed Sch9 to control vacuolar pH and thereby trigger either lifespan extension or lifespan shortening. Hence, our results provide insights into the signaling mechanism coupling glucose availability, TORC1 signaling, pH homeostasis and longevity. As both Sch9 and the V-ATPase are highly conserved and implicated in various pathologies, these results offer fertile ground for further research in higher eukaryotes

Embedding mRNA Stability in Correlation Analysis of Time-Series Gene Expression Data

Current methods for the identification of putatively co-regulated genes directly from gene expression time profiles are based on the similarity of the time profile. Such association metrics, despite their central role in gene network inference and machine learning, have largely ignored the impact of dynamics or variation in mRNA stability. Here we introduce a simple, but powerful, new similarity metric called lead-lag R2 that successfully accounts for the properties of gene dynamics, including varying mRNA degradation and delays. Using yeast cell-cycle time-series gene expression data, we demonstrate that the predictive power of lead-lag R2 for the identification of co-regulated genes is significantly higher than that of standard similarity measures, thus allowing the selection of a large number of entirely new putatively co-regulated genes. Furthermore, the lead-lag metric can also be used to uncover the relationship between gene expression time-series and the dynamics of formation of multiple protein complexes. Remarkably, we found a high lead-lag R2 value among genes coding for a transient complex

Prior Information Properties.

<p>Prior Information Properties.</p

Behaviour of MSE and MSSRE with respect to changes in the connection strengths differing between the competing networks.

<p>The change in MSE (Panel A) and MSSRE (Panel B) in simulations with MN3% and MN5% relative to the simulations with CN are shown in the y-axis. In the x-axis, the total magnitude of the missing connections which existed in CN but had been ignored in the imposed network structure are plotted.</p

Values of Connection Strengths in .

<p>In this plot, each black point represents the connection strength of an element in the unrestricted connectivity matrix which was restricted to 0 previously in the imposed network structure. Each column represents one transcription factor. Red stars are the connections which were missing in the imposed network structure whereas in CN these elements have nonzero values indicating existing connections instead. The outlier elements for each transcription factor identified at a whisker length of 2 are surrounded with additional squares in blue.</p