Inferring causal metabolic signals that regulate the dynamic TORC1-dependent transcriptome
Ontology highlight
ABSTRACT:
ORGANISM(S): Saccharomyces cerevisiae Schizosaccharomyces pombe
PROVIDER: GSE54852 | GEO | 2015/04/07
SECONDARY ACCESSION(S): PRJNA237814
REPOSITORIES: GEO
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Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2015-04-07 | E-GEOD-54852 | biostudies-arrayexpress
Project description: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.
| S-EPMC4422559 | biostudies-literature
Project description:The specific connectivity of a neuronal network is reflected in the dynamics of the signals recorded on its nodes. The analysis of how the activity in one node predicts the behaviour of another gives the directionality in their relationship. However, each node is composed of many different elements which define the properties of the links. For instance, excitatory and inhibitory neuronal subtypes determine the functionality of the connection. Classic indexes such as the Granger causality (GC) quantifies these interactions, but they do not infer into the mechanism behind them. Here, we introduce an extension of the well-known GC that analyses the correlation associated to the specific influence that a transmitter node has over the receiver. This way, the G-causal link has a positive or negative effect if the predicted activity follows directly or inversely, respectively, the dynamics of the sender. The method is validated in a neuronal population model, testing the paradigm that excitatory and inhibitory neurons have a differential effect in the connectivity. Our approach correctly infers the positive or negative coupling produced by different types of neurons. Our results suggest that the proposed approach provides additional information on the characterization of G-causal connections, which is potentially relevant when it comes to understanding interactions in the brain circuits.
| S-EPMC6863873 | biostudies-literature
Project description:The target of rapamycin (TOR) is a eukaryotic serine/threonine protein kinase that functions in two distinct complexes, TORC1 and TORC2, to regulate growth and metabolism. GTPases, responding to signals generated by abiotic stressors, nutrients, and, in metazoans, growth factors, play an important but poorly understood role in TORC1 regulation. Here we report that, in budding yeast, glucose withdrawal (which leads to an acute loss of TORC1 kinase activity) triggers a similarly rapid Rag GTPase-dependent redistribution of TORC1 from being semi-uniform around the vacuolar membrane to a single, vacuole-associated cylindrical structure visible by super-resolution optical microscopy. Three-dimensional reconstructions of cryo-electron micrograph images of these purified cylinders demonstrate that TORC1 oligomerizes into a higher-level hollow helical assembly, which we name a TOROID (TORC1 organized in inhibited domain). Fitting of the recently described mammalian TORC1 structure into our helical map reveals that oligomerization leads to steric occlusion of the active site. Guided by the implications from our reconstruction, we present a TOR1 allele that prevents both TOROID formation and TORC1 inactivation in response to glucose withdrawal, demonstrating that oligomerization is necessary for TORC1 inactivation. Our results reveal a novel mechanism by which Rag GTPases regulate TORC1 activity and suggest that the reversible assembly and/or disassembly of higher-level structures may be an underappreciated mechanism for the regulation of protein kinases.
| S-EPMC5640987 | biostudies-literature
Project description:BackgroundThe budding yeast Saccharomyces cerevisiae undergoes differentiation into filamentous-like forms and invades the growth medium as a foraging response to nutrient and environmental stresses. These developmental responses are under the downstream control of effectors regulated by the cAMP/PKA and MAPK pathways. However, the upstream sensors and signals that induce filamentous growth through these signaling pathways are not fully understood. Herein, through a biochemical purification of the yeast TORC1 (Target of Rapamycin Complex 1), we identify several proteins implicated in yeast filamentous growth that directly associate with the TORC1 and investigate their roles in nitrogen starvation-dependent or independent differentiation in yeast.MethodologyWe isolated the endogenous TORC1 by purifying tagged, endogenous Kog1p, and identified associated proteins by mass spectrometry. We established invasive and pseudohyphal growth conditions in two S. cerevisiae genetic backgrounds (?1278b and CEN.PK). Using wild type and mutant strains from these genetic backgrounds, we investigated the roles of TORC1 and associated proteins in nitrogen starvation-dependent diploid pseudohyphal growth as well as nitrogen starvation-independent haploid invasive growth.ConclusionsWe show that several proteins identified as associated with the TORC1 are important for nitrogen starvation-dependent diploid pseudohyphal growth. In contrast, invasive growth due to other nutritional stresses was generally not affected in mutant strains of these TORC1-associated proteins. Our studies suggest a role for TORC1 in yeast differentiation upon nitrogen starvation. Our studies also suggest the CEN.PK strain background of S. cerevisiae may be particularly useful for investigations of nitrogen starvation-induced diploid pseudohyphal growth.
| S-EPMC3197150 | biostudies-literature
Project description:Cognitive skills undergo protracted developmental changes resulting in proficiencies that are a hallmark of human cognition. One skill that develops over time is the ability to problem solve, which in turn relies on cognitive control and attention abilities. Here we use a novel multimodal neurocognitive network-based approach combining task-related fMRI, resting-state fMRI and diffusion tensor imaging (DTI) to investigate the maturation of control processes underlying problem solving skills in 7-9 year-old children. Our analysis focused on two key neurocognitive networks implicated in a wide range of cognitive tasks including control: the insula-cingulate salience network, anchored in anterior insula (AI), ventrolateral prefrontal cortex and anterior cingulate cortex, and the fronto-parietal central executive network, anchored in dorsolateral prefrontal cortex and posterior parietal cortex (PPC). We found that, by age 9, the AI node of the salience network is a major causal hub initiating control signals during problem solving. Critically, despite stronger AI activation, the strength of causal regulatory influences from AI to the PPC node of the central executive network was significantly weaker and contributed to lower levels of behavioral performance in children compared to adults. These results were validated using two different analytic methods for estimating causal interactions in fMRI data. In parallel, DTI-based tractography revealed weaker AI-PPC structural connectivity in children. Our findings point to a crucial role of AI connectivity, and its causal cross-network influences, in the maturation of dynamic top-down control signals underlying cognitive development. Overall, our study demonstrates how a unified neurocognitive network model when combined with multimodal imaging enhances our ability to generalize beyond individual task-activated foci and provides a common framework for elucidating key features of brain and cognitive development. The quantitative approach developed is likely to be useful in investigating neurodevelopmental disorders, in which control processes are impaired, such as autism and ADHD.
| S-EPMC3271018 | biostudies-other
Project description:Glycolysis and fatty acid (FA) synthesis directs the production of energy-carrying molecules and building blocks necessary to support cell growth, although the absolute requirement of these metabolic pathways must be deeply investigated. Here, we used Drosophila genetics and focus on the TOR (Target of Rapamycin) signaling network that controls cell growth and homeostasis. In mammals, mTOR (mechanistic-TOR) is present in two distinct complexes, mTORC1 and mTORC2; the former directly responds to amino acids and energy levels, whereas the latter sustains insulin-like-peptide (Ilp) response. The TORC1 and Ilp signaling branches can be independently modulated in most Drosophila tissues. We show that TORC1 and Ilp-dependent overgrowth can operate independently in fat cells and that ubiquitous over-activation of TORC1 or Ilp signaling affects basal metabolism, supporting the use of Drosophila as a powerful model to study the link between growth and metabolism. We show that cell-autonomous restriction of glycolysis or FA synthesis in fat cells retrains overgrowth dependent on Ilp signaling but not TORC1 signaling. Additionally, the mutation of FASN (Fatty acid synthase) results in a drop in TORC1 but not Ilp signaling, whereas, at the cell-autonomous level, this mutation affects none of these signals in fat cells. These findings thus reveal differential metabolic sensitivity of TORC1- and Ilp-dependent growth and suggest that cell-autonomous metabolic defects might elicit local compensatory pathways. Conversely, enzyme knockdown in the whole organism results in animal death. Importantly, our study weakens the use of single inhibitors to fight mTOR-related diseases and strengthens the use of drug combination and selective tissue-targeting.
| S-EPMC8045685 | biostudies-literature
Project description:Under insulin-resistant conditions such as obesity, pancreatic ?-cells proliferate to prevent blood glucose elevations. A liver-brain-pancreas neuronal relay plays an important role in this process. Here, we show the molecular mechanism underlying this compensatory ?-cell proliferation. We identify FoxM1 activation in islets from neuronal relay-stimulated mice. Blockade of this relay, including vagotomy, inhibits obesity-induced activation of the ?-cell FoxM1 pathway and suppresses ?-cell expansion. Inducible ?-cell-specific FoxM1 deficiency also blocks compensatory ?-cell proliferation. In isolated islets, carbachol and PACAP/VIP synergistically promote ?-cell proliferation through a FoxM1-dependent mechanism. These findings indicate that vagal nerves that release several neurotransmitters may allow simultaneous activation of multiple pathways in ?-cells selectively, thereby efficiently promoting ?-cell proliferation and maintaining glucose homeostasis during obesity development. This neuronal signal-mediated mechanism holds potential for developing novel approaches to regenerating pancreatic ?-cells.
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Project description:Genes are often combinatorially regulated by multiple transcription factors (TFs). Such combinatorial regulation plays an important role in development and facilitates the ability of cells to respond to different stresses. While a number of approaches have utilized sequence and ChIP-based datasets to study combinational regulation, these have often ignored the combinational logic and the dynamics associated with such regulation. Here we present cDREM, a new method for reconstructing dynamic models of combinatorial regulation. cDREM integrates time series gene expression data with (static) protein interaction data. The method is based on a hidden Markov model and utilizes the sparse group Lasso to identify small subsets of combinatorially active TFs, their time of activation, and the logical function they implement. We tested cDREM on yeast and human data sets. Using yeast we show that the predicted combinatorial sets agree with other high throughput genomic datasets and improve upon prior methods developed to infer combinatorial regulation. Applying cDREM to study human response to flu, we were able to identify several combinatorial TF sets, some of which were known to regulate immune response while others represent novel combinations of important TFs.
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Project description:To determine how TORC1 and PKA cooperate to regulate cell growth, we performed temporal analysis of gene expression in yeast switched from a non-fermentable substrate, to glucose, in the presence and absence of TORC1 and PKA inhibitors. Quantitative analysis of these data reveals that PKA drives the expression of key cell growth genes during transitions into, and out of, the rapid growth state in glucose, while TORC1 is important for the steady-state expression of the same genes.
2019-07-19 | GSE133591 | GEO