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

Ana Paula Oliveira1, Sotiris Dimopoulos2, Alberto Giovanni Busetto3

  • 1Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.

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Researchers developed a new computational framework to understand how cells adapt to nutrient changes. This method reveals how the TORC1 pathway regulates nitrogen metabolism, identifying key signals like glutamine.

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Area of Science:

  • Cellular Biology
  • Systems Biology
  • Metabolic Engineering

Background:

  • Cellular adaptation to nutrient availability relies on complex interactions between signaling, transcriptional, and metabolic networks.
  • Feedback loops in signaling pathways often obscure causal relationships, leaving endogenous inputs of nutrient signaling pathways unknown.
  • Integrating multi-level dynamic data from system-wide experiments presents a significant challenge in understanding these networks.

Purpose of the Study:

  • To develop a computational framework for inferring causal relationships between metabolism, signaling, and gene regulation.
  • To analyze the dynamic regulation of nitrogen metabolism by the target of rapamycin complex 1 (TORC1) pathway in budding yeast.
  • To identify putative downstream targets and metabolic inputs of the TORC1 pathway.

Main Methods:

  • Co-design of dynamic experiments with a probabilistic, model-based inference method.
  • Generation of dynamic transcriptomic, proteomic, and metabolomic data during nitrogen quality shifts.
  • Analysis of extensive cellular network re-wiring during adaptation.

Main Results:

  • Identification of extensive re-wiring of cellular networks during adaptation to changing nitrogen quality.
  • Inferred putative downstream targets of the TORC1 pathway.
  • Identified putative metabolic inputs of TORC1, including the hypothesized glutamine signal.

Conclusions:

  • The study provides a computational framework for studying cellular processes and inferring causal relationships in dynamic biological systems.
  • The findings offer a basis for further mechanistic studies into nitrogen metabolism regulation.
  • The research highlights the importance of integrating multi-level dynamic data for a comprehensive understanding of cellular adaptation.