Reinhard Dechant

Centrosome Separation and Central Spindle Assembly Act in Redundant Pathways that Regulate Microtubule Density and Trigger Cleavage Furrow Formation

The mitotic spindle provides the spatial cue that coordinates cytokinesis with nuclear division. However, the specific property of the mitotic spindle that mediates this spatial regulation remains obscure, in part because different aspects of the mitotic spindle appear to have furrow inducing activity in different systems. We show that in C. elegans embryos, although the central spindle is usually dispensable for furrow initiation, it becomes essential for furrow formation when the extent of centrosome separation during anaphase is reduced. Measurements of microtubule density demonstrate that furrow formation occurs in the vicinity of a local minimum of microtubule density. Reduction of the extent of spindle elongation or disruption of the central spindle causes delayed formation of the cleavage furrow. These data suggest that reduced microtubule density triggers cleavage furrow initiation and demonstrate that redundant mechanisms direct efficient formation of the cleavage furrow.

Cytosolic pH is a second messenger for glucose and regulates the PKA pathway through V-ATPase

Glucose is the preferred carbon source for most cell types and a major determinant of cell growth. In yeast and certain mammalian cells, glucose activates the cAMP‐dependent protein kinase A (PKA), but the mechanisms of PKA activation remain unknown. Here, we identify cytosolic pH as a second messenger for glucose that mediates activation of the PKA pathway in yeast. We find that cytosolic pH is rapidly and reversibly regulated by glucose metabolism and identify the vacuolar ATPase (V‐ATPase), a proton pump required for the acidification of vacuoles, as a sensor of cytosolic pH. V‐ATPase assembly is regulated by cytosolic pH and is required for full activation of the PKA pathway in response to glucose, suggesting that it mediates, at least in part, the pH signal to PKA. Finally, V‐ATPase is also regulated by glucose in the Min6 β‐cell line and contributes to PKA activation and insulin secretion. Thus, these data suggest a novel and potentially conserved glucose‐sensing pathway and identify a mechanism how cytosolic pH can act as a signal to promote cell growth.

Mass spectrometry-based metabolomics of single yeast cells

Single-cell level measurements are necessary to characterize the intrinsic biological variability in a population of cells. In this study, we demonstrate that, with the microarrays for mass spectrometry platform, we are able to observe this variability. We monitor environmentally (2-deoxy-d-glucose) and genetically (ΔPFK2) perturbed Saccharomyces cerevisiae cells at the single-cell, few-cell, and population levels. Correlation plots between metabolites from the glycolytic pathway, as well as with the observed ATP/ADP ratio as a measure of cellular energy charge, give biological insight that is not accessible from population-level metabolomic data.

Recruitment of a chromatin remodelling complex by the Hog1 MAP kinase to stress genes

For efficient transcription, RNA PolII must overcome the presence of nucleosomes. The p38‐related MAPK Hog1 is an important regulator of transcription upon osmostress in yeast and thereby it is involved in initiation and elongation. However, the role of this protein kinase in elongation has remained unclear. Here, we show that during stress there is a dramatic change in the nucleosome organization of stress‐responsive loci that depends on Hog1 and the RSC chromatin remodelling complex. Upon stress, the MAPK Hog1 physically interacts with RSC to direct its association with the ORF of osmo‐responsive genes. In RSC mutants, PolII accumulates on stress promoters but not in coding regions. RSC mutants also display reduced stress gene expression and enhanced sensitivity to osmostress. Cell survival under acute osmostress might thus depend on a burst of transcription that in turn could occur only with efficient nucleosome eviction. Our results suggest that the selective targeting of the RSC complex by Hog1 provides the necessary mechanistic basis for this event.

Nutrient signals driving cell growth.

Regulation of cell growth in response to nutrients is crucial for the survival of all organisms. In yeast, cell growth and division require two signaling pathways, TORC1 and PKA. Activation of these pathways crucially depends on intracellular metabolic signals, but the mechanisms remain elusive. Recent studies have identified potential activators of TORC1 and have highlighted a crucial role for the endomembrane system. Moreover, calcium was recognized as an important second messenger for TORC1 activation in response to amino acid levels. On the contrary, genetic analysis indicates that PKA activation depends on an intracellular glucose metabolite. Together with novel quantitative approaches, these findings provide important groundwork in our understanding of the molecular mechanisms for nutrient sensing in yeast and humans.

Cytosolic pH Regulates Cell Growth through Distinct GTPases, Arf1 and Gtr1, to Promote Ras/PKA and TORC1 Activity

Regulation of cell growth by nutrients is governed by highly conserved signaling pathways, yet mechanisms of nutrient sensing are still poorly understood. In yeast, glucose activates both the Ras/PKA pathway and TORC1, which coordinately regulate growth through enhancing translation and ribosome biogenesis and suppressing autophagy. Here, we show that cytosolic pH acts as a cellular signal to activate Ras and TORC1 in response to glucose availability. We demonstrate that cytosolic pH is sensitive to the quality and quantity of the available carbon source (C-source). Interestingly, Ras/PKA and TORC1 are both activated through the vacuolar ATPase (V-ATPase), which was previously identified as a sensor for cytosolic pH in vivo. V-ATPase interacts with two distinct GTPases, Arf1 and Gtr1, which are required for Ras and TORC1 activation, respectively. Together, these data provide a molecular mechanism for how cytosolic pH links C-source availability to the activity of signaling networks promoting cell growth.

Unraveling condition-dependent networks of transcription factors that control metabolic pathway activity in yeast.

Which transcription factors control the distribution of metabolic fluxes under a given condition? We address this question by systematically quantifying metabolic fluxes in 119 transcription factor deletion mutants of Saccharomyces cerevisiae under five growth conditions. While most knockouts did not affect fluxes, we identified 42 condition‐dependent interactions that were mediated by a total of 23 transcription factors that control almost exclusively the cellular decision between respiration and fermentation. This relatively sparse, condition‐specific network of active metabolic control contrasts with the much larger gene regulation network inferred from expression and DNA binding data. Based on protein and transcript analyses in key mutants, we identified three enzymes in the tricarboxylic acid cycle as the key targets of this transcriptional control. For the transcription factor Gcn4, we demonstrate that this control is mediated through the PKA and Snf1 signaling cascade. The discrepancy between flux response predictions, based on the known regulatory network architecture and our functional 13C‐data, demonstrates the importance of identifying and quantifying the extent to which regulatory effectors alter cellular functions.

Automatic generation of predictive dynamic models reveals nuclear phosphorylation as the key Msn2 control mechanism.

Topological filtering identifies biological networks compatible with known data and enables quantitative analysis of regulatory mechanisms. Reducing the Options Quantitative analysis of signaling systems is challenging because limited quantitative data are available and the data can be represented by many network models. Sunnåker et al. developed a computational approach called topological filtering to systematically and automatically integrate modeling and data acquisition to infer the set of mechanistically plausible models, thus vastly reducing the number of potential models. The approach iteratively eliminates reactions from the model to identify only those topological networks that fit the data. Application of their method to an extracellular signal–regulated kinase (ERK) pathway that could be represented by 512 possible network topologies reduced the possibilities to 16 and showed that a set of feedback reactions were necessary to quantitatively represent the results. Topological filtering applied to the regulation of the localization of Msn2, a yeast transcription factor controlled by phosphorylation by PKA (protein kinase A) in response to changes in glucose abundance, identified a single model that fit the data. Comparison of model predictions with experimental data showed that the nuclear phosphorylation rate was key to controlling Msn2 nuclear abundance in response to cAMP (cyclic adenosine monophosphate), a signal produced as cells recover from glucose starvation. Predictive dynamical models are critical for the analysis of complex biological systems. However, methods to systematically develop and discriminate among systems biology models are still lacking. We describe a computational method that incorporates all hypothetical mechanisms about the architecture of a biological system into a single model and automatically generates a set of simpler models compatible with observational data. As a proof of principle, we analyzed the dynamic control of the transcription factor Msn2 in Saccharomyces cerevisiae, specifically the short-term mechanisms mediating the cells’ recovery after release from starvation stress. Our method determined that 12 of 192 possible models were compatible with available Msn2 localization data. Iterations between model predictions and rationally designed phosphoproteomics and imaging experiments identified a single-circuit topology with a relative probability of 99% among the 192 models. Model analysis revealed that the coupling of dynamic phenomena in Msn2 phosphorylation and transport could lead to efficient stress response signaling by establishing a rate-of-change sensor. Similar principles could apply to mammalian stress response pathways. Systematic construction of dynamic models may yield detailed insight into nonobvious molecular mechanisms.

Inferring causal metabolic signals that regulate the dynamic TORC1-dependent transcriptome

Cells react to nutritional cues in changing environments via the integrated action of signaling, transcriptional, and metabolic networks. Mechanistic insight into signaling processes is often complicated because ubiquitous feedback loops obscure causal relationships. Consequently, the endogenous inputs of many nutrient signaling pathways remain unknown. Recent advances for system‐wide experimental data generation have facilitated the quantification of signaling systems, but the integration of multi‐level dynamic data remains challenging. Here, we co‐designed dynamic experiments and a probabilistic, model‐based method to infer causal relationships between metabolism, signaling, and gene regulation. We analyzed the dynamic regulation of nitrogen metabolism by the target of rapamycin complex 1 (TORC1) pathway in budding yeast. Dynamic transcriptomic, proteomic, and metabolomic measurements along shifts in nitrogen quality yielded a consistent dataset that demonstrated extensive re‐wiring of cellular networks during adaptation. Our inference method identified putative downstream targets of TORC1 and putative metabolic inputs of TORC1, including the hypothesized glutamine signal. The work provides a basis for further mechanistic studies of nitrogen metabolism and a general computational framework to study cellular processes.

An integrated image analysis platform to quantify signal transduction in single cells.

Microscopy can provide invaluable information about biological processes at the single cell level. It remains a challenge, however, to extract quantitative information from these types of datasets. We have developed an image analysis platform named YeastQuant to simplify data extraction by offering an integrated method to turn time-lapse movies into single cell measurements. This platform is based on a database with a graphical user interface where the users can describe their experiments. The database is connected to the engineering software Matlab, which allows extracting the desired information by automatically segmenting and quantifying the microscopy images. We implemented three different segmentation methods that recognize individual cells under different conditions, and integrated image analysis protocols that allow measuring and analyzing distinct cellular readouts. To illustrate the power and versatility of YeastQuant, we investigated dynamic signal transduction processes in yeast. First, we quantified the expression of fluorescent reporters induced by osmotic stress to study noise in gene expression. Second, we analyzed the dynamic relocation of endogenous proteins from the cytoplasm to the cell nucleus, which provides a fast measure of pathway activity. These examples demonstrate that YeastQuant provides a versatile and expandable database and an experimental framework that improves image analysis and quantification of diverse microscopy-based readouts. Such dynamic single cell measurements are highly needed to establish mathematical models of signal transduction pathways.