Reconstruction of the yeast Snf1 kinase regulatory network reveals its role as a global energy regulator

Renata Usaite^1,2, Michael C Jewett^1,a, Ana Paula Oliveira^1,b, John R Yates, III², Lisbeth Olsson^1,c & Jens Nielsen^1,c

^aPresent address: Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA

^bPresent address: Institute for Molecular Systems Biology, ETH Zurich, Zurich 8093, Switzerland

^cPresent address: Department of Chemical and Biological Engineering, Chalmers University of Technology, Kemigarden 4, Gothenburg 412 96, Sweden

Article highlights

Synopsis

AMP-activated kinases (AMPKs) are highly conserved among yeast, plants, and mammals and are central regulators involved in cellular development and survival (Polge and Thomas, 2007). Mammalian AMPK, for example, is a master regulator of energy metabolism (Kahn et al, 2005). Its function is linked to metabolic and aging diseases and it is a key drug target against obesity and diabetes (Hardie, 2007a). Through homology studies, yeast AMPK (Snf1) has been used as a model to study the function of human AMPK (Hardie, 2007a, 2007b).

The yeast Snf1 regulates carbon metabolism during growth on various carbon sources (Celenza and Carlson, 1986; Carlson, 1999). Growing evidence, however, suggests a much broader role for Snf1 as a master regulator of both carbon and energy metabolism. Elucidating the organization and interactions of the Snf1 regulatory network is important for uncovering the complexity of global AMPK function and, ultimately, for using yeast as a model to study the role of AMPK in humans. To achieve this goal, a systems approach combining global measurements across different levels of the cellular hierarchy (mRNAs, proteins, and metabolites) is required.

Here, we integrated data from genome-wide expression profiling and protein measurements with different networks comprising protein–protein interactions, protein–DNA interactions, and metabolic reaction stoichiometry to reconstruct the global Snf1 regulatory network. We first collected a global dataset for wild-type S. cerevisiae CEN.PK113-7D and three Snf1 complex knockout mutants snf1, snf4, snf1snf4 grown in triplicate in carbon-limited chemostat cultivations at a fixed dilution rate D=0.100 h^-1. We quantified a total of 5667 transcripts, 2388 proteins, and 44 intracellular metabolites. At a threshold of P<0.05, a total of 1651, 1810, and 2395 mRNAs, 381, 396, and 352 proteins and 20, 14, and 34 metabolites had significantly changed abundance levels in the three mutants compared with the wild type, respectively. Only 159, 151, and 231 genes were identified to have significantly changed both mRNA and proteins, but among these there was a good correlation between mRNA and protein expression changes for about 85% of the proteins in each of the three mutants compared with the wild type, which highlights the importance of transcription regulation.

To show how the biological system was reprogramed as a result of deleting SNF1, SNF4, or both, we applied several systems-wide methods that integrated our experimental measurements with data from the yeast protein–DNA binding (Hodges et al, 1999; Harbison et al, 2004) and protein–protein interaction (Stark et al, 2006) databases, and the yeast genome-scale metabolic model (Forster et al, 2003). High scoring and DOGMA sub-network analyses identified co-regulated circuits of proteins most significantly changing through protein interactions as a group in response to the loss of Snf1 kinase activity. Reporter effector analysis identified transcription factors (TFs), whose target genes were most significantly affected and responded as a group to genetic Snf1 kinase complex disruptions. Reporter metabolite analysis identified metabolic hot spots that significantly responded to the loss of Snf1 kinase activity. In total, our four analyses identified the significant network interactions (P<0.05), in which Snf1 kinase has a critical function regulating yeast metabolism through protein, transcription and metabolite level. Three levels of ome-data and carefully chosen/designed computational analysis tools identified a diversity of interactions that show the global regulatory network of the Snf1 kinase. The regulatory map reconstructed here confirmed previously reported regulatory links that validated the power of our method, and identified new Snf1 targets, for example the carnitine metabolism and transfer system.

Mammalian AMPK is described as a low-energy checkpoint that mediates the energy state of the cell by regulating catabolic and anabolic reactions (Hardie and Sakamoto, 2006). If this ancestral function is conserved, yeast Snf1 kinase would be expected to induce energy generating and repress energy consuming reactions under carbon-limited growth conditions, as used in this study. Indeed, our systems-wide data support this hypothesis. DOGMA sub-network analysis identified the most significant factors associated with Snf1 to be enzymes of fatty acid synthesis and oxidation pathways (Fox2, Acc1, and Fas1) (Figure 2A). To explore how these pathways were affected, we built a pathway model linking all measurement types and known protein–protein interactions in Cytoscape (Shannon et al, 2003) (Figure 3). Our results showed that genes and proteins (Cta1, Pox1, Fox2, and Pot1) involved in -oxidation had significantly (P<0.05) lower expression in the Snf1 mutants relative to the wild type. Quantitative metabolome analysis showed that free fatty acids (oleic, palmitoleic, myristic, palmitic, and stearic acid) accumulated, rather than being catabolized by -oxidation to generate energy, in the Snf1 kinase knockout mutants relatively to the wild-type strain. It has earlier been shown that Snf1 kinase regulates -oxidation gene expression through the TFs Adr1, Pip2, and Oaf1 (Young et al, 2002; Schuller, 2003). Our study for the first time showed that as Snf1 kinase complex affected -oxidation, it also affected energy consuming fatty acid synthesis and carnitine metabolic pathway (Figure 3) identifying Snf1 kinase as a central regulator of the complete fatty acid metabolism.

Figure 2

The reconstructed regulatory network of Snf1 kinase. The network was reconstructed by integrating mRNA and protein expression data for the snf1 mutant versus the wild-type strain with previously reported protein–DNA (Hodges et al, 1999; Harbison et al, 2004) and protein–protein (BIOGRID-Saccharomyces_cerevisiae v.2.0.25) (Stark et al, 2006) interactions, and with protein–metabolite interactions provided by the yeast genome-scale metabolic model (Forster et al, 2003). The network includes Snf1-interacting proteins that were identified by using gene expression data and high scoring sub-network analysis (blue connections to diamonds), or protein expression data and DOGMA analysis (blue connections to circles). Diamonds show gene expression data and circles show protein expression data, which are colored according to log2-ratio color scale of snf1 relative to WT. The network also includes Reporter Metabolites, around which mRNA or protein abundance changes were significantly concentrated in response to the loss of SNF1 (gray connections to triangles and hexagons, respectively). Reporter Effectors of Snf1 (orange connections to squares) show gene expression data. Reporter Effectors that are reported to associate to Snf1 kinase (Stark et al, 2006) are indicated using solid orange connections. Dashed lines indicate molecular or physiological links between Snf1 and the Reporter Effectors, or between Snf1 and the Reporter Metabolites not reported earlier. Small black arrow-diamonds represent previously determined Snf1-based phosphorylation of the Snf1 targets: Reporter Effectors and Snf1 interacting proteins (Ptacek et al, 2005). Nodes with black borders have significantly different (P<0.05) mRNA or protein expression data for the snf1 mutant versus the wild-type strain. Genes and proteins are named according to the SGDatabase nomenclature. PEP, phosphoenolpyruvate; SAICAR, 1-(5'-phosphoribosyl)-5-amino-4-(N-succinocarboxamide)-imidazole; UDP-GalNAc, UDP-N-acetyl-D-galactosamine; GlcNAc-1-P, N-acetyl-D-glucosamine 1-phosphate; m, mitochondrial; ext, extracellular. More detailed information describing the sub-network, Reporter Effector, and Reporter Metabolite analyses outputs can be found in Supplementary Tables III–VII. (A) The components identified using the DOGMA sub-network analysis. (B) The components identified using the high scoring sub-network analysis. (C) The identified reporter metabolites. (D) The identified reporter effectors. (E) The combined and fully reconstructed interaction network for Snf1.

Full figure and legend (691K)Figures & Tables index

Figure 3

The impact of Snf1 kinase on fatty acid metabolism shows its role as a global energy regulator. This figure comprises information of the yeast metabolic network (Forster et al, 2003), the reconstructed Snf1 regulatory network, and raw mRNA, protein and metabolite abundance data for the snf1 mutant compared with the wild-type strain (Figure 2; Supplementary Tables VIII and IX). This figure shows that the loss of Snf1 activity results in reduced activity of energy producing reactions (e.g., -oxidation). Enzymes are mapped using protein (in diamonds) and mRNA (in small circles) expression data, which is colored according to log2-ratio color scale. Available protein and metabolite relative abundance data are mapped on the regulators and metabolites, accordingly. Nodes with black borders have significantly different (P<0.05) expression data for the snf1 mutant versus the wild-type strain. Gray nodes represent components that were not measured. Five Snf1–protein interactions (solid gray lines) were identified using sub-network analyses. Colored dashed lines indicate previously reported protein, transcriptional, and allosteric regulations (Young et al, 2002; Schuller, 2003).

Full figure and legend (255K)Figures & Tables index

Collectively, our systems approach identified that energy generating -oxidation pathways, energy consuming fatty acid synthesis, energy homeostasis maintaining, and energy storing pathways were significantly affected by the loss of Snf1 kinase activity. Our data therefore show that Snf1 is mimicking the role of its homolog AMPK in mammalian cells as a low-energy checkpoint, and hence strengthens the homology in function between yeast Snf1 and mammalian AMPK and opens the door for further using yeast as a model organism to study AMPK and hereby use our reconstructed network as a scaffold for better understanding and ultimately addressing metabolic disorders in humans.