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.

Nitrogen source activates TOR (target of rapamycin) complex 1 via glutamine and independently of Gtr/Rag proteins.

The evolutionary conserved TOR complex 1 (TORC1) activates cell growth in response to nutrients. In yeast, TORC1 responds to the nitrogen source via a poorly understood mechanism. Leucine, and perhaps other amino acids, activates TORC1 via the small GTPases Gtr1 and Gtr2, orthologs of the mammalian Rag GTPases. Here we investigate the activation of TORC1 by the nitrogen source and how this might be related to TORC1 activation by Gtr/Rag. The quality of the nitrogen source, as defined by its ability to promote growth and glutamine accumulation, directly correlates with its ability to activate TORC1 as measured by Sch9 phosphorylation. Preferred nitrogen sources stimulate rapid, sustained Sch9 phosphorylation and glutamine accumulation. Inhibition of glutamine synthesis reduces TORC1 activity and growth. Poor nitrogen sources stimulate rapid but transient Sch9 phosphorylation. A Gtr1 deficiency prevents the transient stimulation of TORC1 but does not affect the sustained TORC1 activity in response to good nitrogen sources. These findings suggest that the nitrogen source must be converted to glutamine, the preferred nitrogen source in yeast, to sustain TORC1 activity. Furthermore, sustained TORC1 activity is independent of Gtr/Rag. Thus, the nitrogen source and Gtr/Rag activate TORC1 via different mechanisms.

Integration of time-resolved transcriptomics data with flux-based methods reveals stress-induced metabolic adaptation in Escherichia coli.

BACKGROUND: Changes in environmental conditions require temporal effectuation of different metabolic pathways in order to maintain the organisms' viability but also to enable the settling into newly arising conditions. While analyses of robustness in biological systems have resulted in the characterization of reactions that facilitate homeostasis, temporal adaptation-related processes and the role of cellular pathways in the metabolic response to changing conditions remain elusive. RESULTS: Here we develop a flux-based approach that allows the integration of time-resolved transcriptomics data with genome-scale metabolic networks. Our framework uses bilevel optimization to extract temporal minimal operating networks from a given large-scale metabolic model. The minimality of the extracted networks enables the computation of elementary flux modes for each time point, which are in turn used to characterize the transitional behavior of the network as well as of individual reactions. Application of the approach to the metabolic network of Escherichia coli in conjunction with time-series gene expression data from cold and heat stress results in two distinct time-resolved modes for reaction utilization-constantly active and temporally (de)activated reactions. These patterns contrast the processes for the maintenance of basic cellular functioning and those required for adaptation. They also allow the prediction of reactions involved in time- and stress-specific metabolic response and are verified with respect to existing experimental studies. CONCLUSIONS: Altogether, our findings pinpoint the inherent relation between the systemic properties of robustness and adaptability arising from the interplay of metabolic network structure and changing environment.

Metabolomic and transcriptomic stress response of Escherichia coli.

Environmental fluctuations lead to a rapid adjustment of the physiology of Escherichia coli, necessitating changes on every level of the underlying cellular and molecular network. Thus far, the majority of global analyses of E. coli stress responses have been limited to just one level, gene expression. Here, we incorporate the metabolite composition together with gene expression data to provide a more comprehensive insight on system level stress adjustments by describing detailed time-resolved E. coli response to five different perturbations (cold, heat, oxidative stress, lactose diauxie, and stationary phase). The metabolite response is more specific as compared with the general response observed on the transcript level and is reflected by much higher specificity during the early stress adaptation phase and when comparing the stationary phase response to other perturbations. Despite these differences, the response on both levels still follows the same dynamics and general strategy of energy conservation as reflected by rapid decrease of central carbon metabolism intermediates coinciding with downregulation of genes related to cell growth. Application of co-clustering and canonical correlation analysis on combined metabolite and transcript data identified a number of significant condition-dependent associations between metabolites and transcripts. The results confirm and extend existing models about co-regulation between gene expression and metabolites demonstrating the power of integrated systems oriented analysis.

Stability of metabolic correlations under changing environmental conditions in Escherichia coli--a systems approach.

BACKGROUND: Biological systems adapt to changing environments by reorganizing their cellular and physiological program with metabolites representing one important response level. Different stresses lead to both conserved and specific responses on the metabolite level which should be reflected in the underlying metabolic network. METHODOLOGY/PRINCIPAL FINDINGS: Starting from experimental data obtained by a GC-MS based high-throughput metabolic profiling technology we here develop an approach that: (1) extracts network representations from metabolic condition-dependent data by using pairwise correlations, (2) determines the sets of stable and condition-dependent correlations based on a combination of statistical significance and homogeneity tests, and (3) can identify metabolites related to the stress response, which goes beyond simple observations about the changes of metabolic concentrations. The approach was tested with Escherichia coli as a model organism observed under four different environmental stress conditions (cold stress, heat stress, oxidative stress, lactose diauxie) and control unperturbed conditions. By constructing the stable network component, which displays a scale free topology and small-world characteristics, we demonstrated that: (1) metabolite hubs in this reconstructed correlation networks are significantly enriched for those contained in biochemical networks such as EcoCyc, (2) particular components of the stable network are enriched for functionally related biochemical pathways, and (3) independently of the response scale, based on their importance in the reorganization of the correlation network a set of metabolites can be identified which represent hypothetical candidates for adjusting to a stress-specific response. CONCLUSIONS/SIGNIFICANCE: Network-based tools allowed the identification of stress-dependent and general metabolic correlation networks. This correlation-network-based approach does not rely on major changes in concentration to identify metabolites important for stress adaptation, but rather on the changes in network properties with respect to metabolites. This should represent a useful complementary technique in addition to more classical approaches.

Transcription factor AtDOF4;2 affects phenylpropanoid metabolism in Arabidopsis thaliana.

In a phenotypic screen of plants constitutively overexpressing DOF (DNA-binding-with-one-finger) transcription factors under the control of the Cauliflower mosaic virus 35S promoter, AtDOF4;2 was identified as a gene inducing a bushy plant phenotype and potentially being involved in the regulation of phenylpropanoid metabolism in Arabidopsis. Further molecular and biochemical characterization was performed in parallel using transgenic plants with enhanced and reduced AtDOF4;2 expression. The expression pattern of AtDOF4;2 was determined by quantitative real-time polymerase chain reaction (Q-RTPCR) and through promoter-beta-glucuronidase (GUS) fusions, indicating preferential transcriptional activity in axillary buds of the flower stalk, the hypocotyls periderm and in tapetum cells. Constitutive overexpression and RNAi-mediated silencing of AtDOF4;2 caused reciprocal changes in the expression of flavonoid biosynthetic genes and the accumulation of flavonoids under cold and high-light conditions. Moreover, tapetum-specific overexpression of AtDOF4;2 led to pollen grains devoid of flavonols. In contrast to its negative influence on flavonoid biosynthesis and coincident with high expression in the periderm and tapetum, AtDOF4;2 positively influences the production of hydroxycinnamic acids in the hypocotyl and flower buds, implicating its possible importance for suberin and sporopollenin production. These data provide evidence that AtDOF4;2, influences phenylpropanoid metabolism in an environmental and tissue-specific manner.