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

Abstract

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.

Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast

“…Salt and pheromone data-sets were integrated with a compendium of biological experiments including time-resolved measurements related to nitrogen metabolism and steady-state genetic perturbations. To this end we considered a panel of 115 K/Ps knockouts, for which molecular changes at the transcript [5], phosphorylation [4] and metabolite [6] were characterised (Fig 1A) (see Methods) as well as metabolomics, transcriptomics and phosphoproteomics data-sets for three perturbations around nitrogen metabolism [11,12]. In these studies, yeast cells were perturbed by varying the growth medium from poor to rich nitrogen growing conditions (nitrogen upshift) and vice-versa (nitrogen downshift).…”

“…At present, most of the integrative analysis of metabolomics data-sets have focused on the role of transcriptional regulation [8–11]. Previous studies have focused on the regulatory implication of transcription-factors (TFs) to model the metabolic transition between different steady-state conditions [10].…”

“…Previous studies have focused on the regulatory implication of transcription-factors (TFs) to model the metabolic transition between different steady-state conditions [10]. Moreover, these regulatory interactions may occur in the inverse direction where metabolites directly impact the activity of global cellular regulators, such as TOR1 [11,12]. Nevertheless, transcript levels have been shown to poorly predict metabolic fluxes in the central carbon metabolism and that glycolytic enzymes are predominantly regulated at the post-transcriptional level [3,13,14].…”

“…Transcriptional and translational regulatory interactions of metabolism can, in principle, be comprehensively explored using available high-throughput data-sets and methods [4,5,11]. However, current methods have yet to integrate gene-expression and phosphoproteomics measurement to infer regulatory interactions of metabolism.…”

“…We propose a computational approach to systematically identify putative post-transcriptional and post-translational regulatory mechanisms of metabolism (Fig 1A). To this end, we characterised the metabolomics adaptation of yeast under salt and pheromone conditions and further expanded it to consider a compendium of experimental data-sets [4–6,11,12], comprising a total of 143 unique conditions. Firstly, our computational approach predicted the in vivo activity of TFs and K/Ps.…”