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

Molecular Systems Biology

Ana Paula Oliveira¹  Sotiris Dimopoulos³  Alberto Giovanni Busetto²  Stefan Christen¹  Reinhard Dechant⁴  Laura Falter¹  Morteza Haghir Chehreghani²  Szymon Jozefczuk¹  Christina Ludwig¹  Florian Rudroff¹  Juliane Caroline Schulz¹  Asier González⁵  Alexandre Soulard⁵  Daniele Stracka⁵  Ruedi Aebersold¹  Joachim M Buhmann²  Michael N Hall⁵  Matthias Peter⁴  Uwe Sauer¹ 

[1] Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland;Department of Computer Science, ETH Zurich, Zurich, Switzerland;Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zurich, Basel, Switzerland;Department of Biology, Institute of Biochemistry, ETH Zurich, Zurich, Switzerland;Biozentrum, University of Basel, Basel, Switzerland

关键词: causal inference;    network motifs;    nutrient signaling;    target of rapamycin pathway;   

DOI  :  10.15252/msb.20145475

来源: Wiley

【 摘 要 】

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.

Synopsis

Dynamic experiments and a computational method are co-designed to infer causal interactions between metabolism, signaling and transcription. Model-based data integration suggests new candidates for inputs and targets of yeast nitrogen signaling via TOR complex 1.

• Dynamic experiments yield a consistent, multi-omics dataset for yeast responses to shifts in nitrogen quality.
• The cellular response involves extensive rewiring of metabolism via multiple mechanisms.
• Our generalizable probabilistic framework infers causal relations from heterogeneous data types and exploits prior knowledge on networks and biological mechanisms.

【 授权许可】

CC BY   
© 2015 The Authors. Published under the terms of the CC BY 4.0 license

Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.