Reconstruction of the yeast Snf1 kinase regulatory network reveals its role as a global energy regulator
2009; Springer Nature; Volume: 5; Issue: 1 Linguagem: Inglês
10.1038/msb.2009.67
ISSN1744-4292
AutoresRenata Usaite, Michael C. Jewett, Ana Paula Oliveira, John R. Yates, Lisbeth Olsson, Jens Nielsen,
Tópico(s)Microbial Metabolic Engineering and Bioproduction
ResumoArticle3 November 2009Open Access Reconstruction of the yeast Snf1 kinase regulatory network reveals its role as a global energy regulator Renata Usaite Renata Usaite Department of Systems Biology, Center for Microbial Biotechnology, BioCentrum-DTU, Technical University of Denmark, Lyngby, Denmark Department of Cell Biology, Proteomics Mass Spectrometry Labratory, The Scripps Research Institute, La Jolla, CA, USA Search for more papers by this author Michael C Jewett Michael C Jewett Department of Systems Biology, Center for Microbial Biotechnology, BioCentrum-DTU, Technical University of Denmark, Lyngby, DenmarkPresent address: Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA Search for more papers by this author Ana Paula Oliveira Ana Paula Oliveira Department of Systems Biology, Center for Microbial Biotechnology, BioCentrum-DTU, Technical University of Denmark, Lyngby, DenmarkPresent address: Institute for Molecular Systems Biology, ETH Zurich, Zurich 8093, Switzerland Search for more papers by this author John R Yates III John R Yates III Department of Cell Biology, Proteomics Mass Spectrometry Labratory, The Scripps Research Institute, La Jolla, CA, USA Search for more papers by this author Lisbeth Olsson Lisbeth Olsson Department of Systems Biology, Center for Microbial Biotechnology, BioCentrum-DTU, Technical University of Denmark, Lyngby, DenmarkPresent address: Department of Chemical and Biological Engineering, Chalmers University of Technology, Kemigarden 4, Gothenburg 412 96, Sweden Search for more papers by this author Jens Nielsen Corresponding Author Jens Nielsen Department of Systems Biology, Center for Microbial Biotechnology, BioCentrum-DTU, Technical University of Denmark, Lyngby, DenmarkPresent address: Department of Chemical and Biological Engineering, Chalmers University of Technology, Kemigarden 4, Gothenburg 412 96, Sweden Search for more papers by this author Renata Usaite Renata Usaite Department of Systems Biology, Center for Microbial Biotechnology, BioCentrum-DTU, Technical University of Denmark, Lyngby, Denmark Department of Cell Biology, Proteomics Mass Spectrometry Labratory, The Scripps Research Institute, La Jolla, CA, USA Search for more papers by this author Michael C Jewett Michael C Jewett Department of Systems Biology, Center for Microbial Biotechnology, BioCentrum-DTU, Technical University of Denmark, Lyngby, DenmarkPresent address: Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA Search for more papers by this author Ana Paula Oliveira Ana Paula Oliveira Department of Systems Biology, Center for Microbial Biotechnology, BioCentrum-DTU, Technical University of Denmark, Lyngby, DenmarkPresent address: Institute for Molecular Systems Biology, ETH Zurich, Zurich 8093, Switzerland Search for more papers by this author John R Yates III John R Yates III Department of Cell Biology, Proteomics Mass Spectrometry Labratory, The Scripps Research Institute, La Jolla, CA, USA Search for more papers by this author Lisbeth Olsson Lisbeth Olsson Department of Systems Biology, Center for Microbial Biotechnology, BioCentrum-DTU, Technical University of Denmark, Lyngby, DenmarkPresent address: Department of Chemical and Biological Engineering, Chalmers University of Technology, Kemigarden 4, Gothenburg 412 96, Sweden Search for more papers by this author Jens Nielsen Corresponding Author Jens Nielsen Department of Systems Biology, Center for Microbial Biotechnology, BioCentrum-DTU, Technical University of Denmark, Lyngby, DenmarkPresent address: Department of Chemical and Biological Engineering, Chalmers University of Technology, Kemigarden 4, Gothenburg 412 96, Sweden Search for more papers by this author Author Information Renata Usaite1,2, Michael C Jewett1, Ana Paula Oliveira1, John R Yates2, Lisbeth Olsson1 and Jens Nielsen 1 1Department of Systems Biology, Center for Microbial Biotechnology, BioCentrum-DTU, Technical University of Denmark, Lyngby, Denmark 2Department of Cell Biology, Proteomics Mass Spectrometry Labratory, The Scripps Research Institute, La Jolla, CA, USA *Corresponding author. Department of Chemical and Biological Engineering, Chalmers University of Technology, Kemigarden 4, Gothenburg 412 96, Sweden. Tel.: +31 772 38 05; Fax: +31 772 38 01; E-mail: [email protected] Molecular Systems Biology (2009)5:319https://doi.org/10.1038/msb.2009.67 PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Highly conserved among eukaryotic cells, the AMP-activated kinase (AMPK) is a central regulator of carbon metabolism. To map the complete network of interactions around AMPK in yeast (Snf1) and to evaluate the role of its regulatory subunit Snf4, we measured global mRNA, protein and metabolite levels in wild type, Δsnf1, Δsnf4, and Δsnf1Δsnf4 knockout strains. Using four newly developed computational tools, including novel DOGMA sub-network analysis, we showed the benefits of three-level ome-data integration to uncover the global Snf1 kinase role in yeast. We for the first time identified Snf1's global regulation on gene and protein expression levels, and showed that yeast Snf1 has a far more extensive function in controlling energy metabolism than reported earlier. Additionally, we identified complementary roles of Snf1 and Snf4. Similar to the function of AMPK in humans, our findings showed that Snf1 is a low-energy checkpoint and that yeast can be used more extensively as a model system for studying the molecular mechanisms underlying the global regulation of AMPK in mammals, failure of which leads to metabolic diseases. 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 Δsnf1, Δsnf4, Δsnf1Δsnf4 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 β-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 β-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 β-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 β-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. Collectively, our systems approach identified that energy generating β-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. Introduction 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 control (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 human AMPK. For example, the upstream kinases of Snf1 (Elm1, Pak1, and Tos3) helped identify their mammalian counterparts, Lkb1 and CaMKK-β, that activate human AMPK (Hardie and Sakamoto, 2006). The yeast Snf1 regulates carbon metabolism during growth on various carbon sources (Celenza and Carlson, 1986; Carlson, 1999). In a complex with its regulator Snf4 and scaffolding protein Gal83, Snf1 regulates the usage of alternative carbon sources through the transcription factors (TFs) Mig1 and Cat8 (Schuller, 2003). Two other Snf1 scaffolding proteins Sip1 and Sip2 determine distinct Snf1-substrate specificity, sub-cellular localization and function (Vincent et al, 2001). There is, however, growing evidence that suggests a much broader role of Snf1 as a master regulator of carbon and energy metabolism. Genome-wide transcriptional profiling in yeast batch cultures has identified that active Snf1 is required for more than 400 of 1500 gene expression changes under glucose exhaustion (DeRisi et al, 1997; Young et al, 2003). At the level of protein interactions (BioGRID database) (Stark et al, 2006), Snf1 associates with 209 proteins, only 10% of which are enriched (hypergeometric test: P=1.5E−5) within GO carbohydrate metabolic process group (e.g., Adr1, Cat8, Sip4, Pho85, Gsy2, Reg1, Glc7). Moreover, similar to mammalian AMPK, Snf1 has been found to respond to various nutrient and environmental stresses including oxidative stress (Hong and Carlson, 2007), implicating a role for Snf1 as a global regulator in addition to controlling the usage of various carbon sources (Gancedo, 1998). Furthermore, the remarkable structural conservation of AMPKs' heterotrimeric complexes, specific upstream activators, and downstream targets (at the transcriptional, protein synthesis and degradation, and posttranslational levels) in different kingdoms suggests a common AMPK ancestral function as a key regulator of energy homeostasis (Polge and Thomas, 2007). Clarifying 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. However, neither transcriptional profiling, nor protein–protein interactions, nor ancestry alone can adequately describe the global regulatory role of Snf1. For this, a systems approach combining global measurements across different levels of the cellular hierarchy (mRNAs, proteins, and metabolites) is required. Recently, Ishii et al (2007) and Castrillo et al (2007) showed the utility of such an approach for mapping the cellular response of Escherichia coli and Saccharomyces cerevisiae, respectively, to genetic and environmental perturbations. Here, we integrated data from genome-wide mRNA and protein profiling and metabolite measurements with different networks comprising protein–protein interactions, protein–DNA interactions, and metabolic reaction stoichiometry, with the following objectives: (1) to show the use of novel algorithms for integrated analysis of high-throughput experimental datasets; (2) to reconstruct a global regulatory network for the protein kinase Snf1; and (3) to evaluate whether the components Snf1 and Snf4 of the Snf1 protein kinase complex have additional functions. Results and discussion Dataset collected in this study We first collected a global dataset for wild-type S. cerevisiae CEN.PK113-7D and three Snf1 complex knockout mutants Δsnf1, Δsnf4, Δsnf1Δsnf4 (Supplementary Table I) grown in triplicate in carbon-limited chemostat cultivations at a fixed dilution rate D=0.100 h−1 (see Materials and methods). Abundances of gene, protein, and intracellular metabolites were quantified using Affymetrix GeneChip Yeast Genome 2.0 Arrays (Wodicka et al, 1997), multidimensional protein identification technology (MudPIT) followed by quantitation using stable isotope labeling approach (Washburn et al, 2001; Usaite et al, 2008b), and gas chromatography coupled to mass spectrometry (Villas-Boas et al, 2005b), respectively. 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 knockout Δsnf1, Δsnf4, Δsnf1Δsnf4 mutants compared with the wild-type strain, respectively (Supplementary Table II). However, only 159, 151, and 231 genes were identified to have significant changes in both mRNA and proteins in the knockout Δsnf1, Δsnf4, Δsnf1Δsnf4 mutants compared with the wild-type strain, respectively. Among these there was the same change in abundance, that is both mRNA and protein were up- or downregulated, for 84, 87, and 88% of the proteins, respectively. Genes, whose mRNA and protein expression change correlated, belonged to carbon and amino-acid metabolism and indicated the presence of strong transcription regulation in these pathways. Genes, whose mRNA and protein had opposing significant expression changes, indicated dual level of regulation and, thus, the presence of physiologically meaningful regulation on protein level. Integrated analysis for mapping Snf1 interactions To show how the biological system was reprogramed as a result of deleting SNF1, SNF4, or both SNF1 and SNF4, we applied four systems-wide methods that integrated our experimental measurements of mRNA, protein, and metabolites with data from protein–DNA binding (Hodges et al, 1999; Harbison et al, 2004), protein–protein interaction databases (Stark et al, 2006), and the yeast genome-scale metabolic model (Forster et al, 2003) (Figure 1). These four methods allowed us to identify reporter effectors reporter metabolites, high scoring sub-networks, and high scoring DNA-to-protein translation Onto a Graph-based Multi-level integrative Analysis (DOGMA) sub-networks, and based on this we reconstructed the global Snf1 kinase regulatory network (Figure 2). In total, our four different analyses identified the significant network interactions (P<0.05) in which the deletion of Snf1 has a critical function regulating global yeast metabolism (Figure 2E). As the metabolome dataset is relatively scarce we did not include this in our integrated data analysis, but only used these data to support some of our findings in terms of how Snf1 is globally regulating metabolism. Below we describe the four different types of integrated analysis separately followed by a presentation of the metabolome data. Thereafter, we discuss how the results from the different types of analysis can be integrated into reconstruction of the global regulatory network. Figure 1.A systems approach to mapping Snf1 response pathways. The panels depict the central dogma with highlights on the different levels of regulation captured by each method used in this work. Methods (A, B) are global methods to search for highly active biological sub-networks, whereas methods (C, D) are local scoring systems for evaluation of the regulatory significance, or 'activity', of effectors and metabolites. Each panel highlights the type of interaction/association that constitutes the basis of the underlying network graph used in the corresponding graph-based method. Nodes scored based on transcript level information are colored in gray. Nodes scored based on protein abundance information are colored in orange. Only nodes that are part of the interaction network are colored; for example, the transcript T2 is not colored in (C) as T2 is not part of the transcriptional regulatory network, but is colored in (B) because there is a translational association between T2 and P2. (A) Represents high scoring sub-network analysis (Ideker et al, 2001) that, based on gene expression data, was used to identify co-regulatory circuits of directly connected proteins and regulated genes that are significantly changing as a group in response to the loss of Snf1 kinase activity. (B) Represents a novel DOGMA sub-network approach (described here for the first time) that, based on gene and protein expression data, was used to identify co-regulatory circuits of directly connected proteins and regulated genes, and amplifies the significance of coordinated mRNA and protein expression that are significantly changing as a group in response to the loss of Snf1 kinase activity. (C) Represents Reporter Effector analysis tool (Oliveira et al, 2008) that, based on gene expression data, was used to identify TFs and regulatory proteins whose connected genes were most significantly affected and responded as a group to genetic disruptions of the Snf1 complex. Here, P1 is a TF that targets Gene 3, Gene 4, and Gene 5. (D) Represents Reporter Metabolite Analysis (Patil and Nielsen, 2005) that, based on gene (gray) or protein expression data (orange), was used for discovering metabolic hot spots that significantly responded to the loss of Snf1 kinase activity. (E–H) The number of component (mRNA or proteins) identified for the three different mutants in the different types of analysis. Download figure Download PowerPoint Figure 2.The reconstructed regulatory network of Snf1 kinase. The network was reconstructed by integrating mRNA and protein expression data for the Δsnf1 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 Δsnf1 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 Δsnf1 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. Download figure Download PowerPoint High scoring sub-network analysis First, we used high scoring sub-network analysis (Ideker et al, 2002) to identify co-regulatory circuits of directly connected proteins and regulated genes that are significantly changing as a group in response to the loss of Snf1 kinase activity (Figure 1A) (see Materials and methods). We used high scoring sub-network analysis using mRNA data, as this is the only complete dataset covering expression of all genes in yeast and that potentially could accounts for all possible significantly changed protein and gene interactions. Not using protein expression data on protein nodes might skew high scoring sub-network analysis results in case of presence of posttranscription regulation, but still the analysis identifies transcriptionally co-regulated sub-networks. High scoring sub-network analysis showed three sub-networks comprising 301, 363, and 334 nodes and 651, 987, and 834 edges for the Δsnf1, Δsnf4, and Δsnf1Δsnf4 mutants, respectively. From these co-regulated circuits, a total of 12, 18, and 13 proteins interacted with the Snf1 kinase (based on the definition of BIOGRID-Saccharomyces_cerevisiae v.2.0.25) for the Δsnf1, Δsnf4, and Δsnf1Δsnf4 mutants, respectively. The results are summarized in Supplementary Table III and Figure 2B for the Δsnf1 mutant. Only Snf1 kinase interacting proteins were included in the reconstructed Snf1 kinase regulatory network to represent the nodes most directly affected by the Snf1 kinase through protein interaction (Figure 2E). High scoring sub-network analysis identified expected glucose repression TF Mig1 as well as protein nodes in redox and biogenesis (Figure 2B). Results of the high scoring sub-network analysis were fairly consistent in terms of interactions with components of different parts of the metabolism for the three strains Δsnf1, Δsnf4, and Δsnf1Δsnf4 (Figure 1E), but the targets vary somewhat between the strains (Supplementary Table III). DOGMA sub-network analysis To integrate our transcriptomics and proteomics measurements in the same analysis, we extended the high scoring sub-network analysis by mapping protein abundance data for protein nodes and mRNA data for DNA nodes, and included interaction edges between mRNA species and their corresponding proteins (Figure 1B). We call our new approach, which amplifies the significance of coordinated mRNA and protein expression, 'DOGMA sub-network analysis' (see Materials and methods). DOGMA sub-network analysis contains three types of interactions: protein–protein, protein—DNA, and 'mRNA to protein' translation interactions. The network expansion arises from the inclusion of interactions between each transcript (mRNA) and its corresponding protein. Changes in proteome levels were used to score protein nodes, whereas transcriptome (mRNA) data were used to score DNA nodes. As for standard sub-network analysis, a simulated annealing algorithm was used to identify co-regulated regions in the network. DOGMA sub-network analysis identified three networks comprising 444, 450, and 376 nodes and 766, 740, and 609 edges for the Δsnf1, Δsnf4, Δsnf1Δsnf4 mutants, respectively. Resulting high scoring sub-networks showed connected circuits being significantly regulated at gene, translation, and protein levels. From these co-regulated circuits, a total of 21, 14, and 16 proteins that interact with Snf1 kinase were identified for the Δsnf1, Δsnf4, Δsnf1Δsnf4 mutants, respectively, and these are listed in Supplementary Table IV. Dogma sub-network analysis identified more Snf1 kinase interacting proteins involved in fatty acid and lipid metabolism compared with high scoring sub-network analysis results (Figure 2A and B) indicating the presence of significant posttranscriptional regulation in lipid metabolism. These results were consistent among the three strains studied (Figure 1F). Overall, both the high scoring sub-network and the DOGMA sub-network analysis identified a few proteins (e.g., Mig1, Snf4, Acc1, Gsy2) that were expected on the basis of earlier studies, but we also identified many other proteins interacting with Snf1, including proteins involved in carnitine metabolism (Yat2, Cat2), lipid metabolism (Smp2, Fas1, Fox2), and stress response (Hog1, Cna1). Strikingly, for all three strains studied, ∼85% of the first neighbors of the Snf1 kinase have a primary functional role outside the carbon metabolism including redox, lipid metabolism, and biogenesis. Reporter effector analysis We also applied our newly published 'Reporter Effector' algorithm (Oliveira et al, 2008) to identify TFs and regulatory proteins whose target genes were most significantly affected and responded as a group to genetic disruptions of the Snf1 complex (see Materials and methods and Figure 1C). Here, Z-scores for each effector were calculated based on the average of Z-scores of its adjacent genes (based on P-values from gene expression data) in a network of 3246 protein–DNA interactions and 484 effectors collected from ChIP-chip experiments and the YPD database (Hodges et al, 1999; Harbison et al, 2004). The cumulative Z-score was corrected for the size of the group and then converted back to P-values by using the normal cumulative distribution function. The Reporter Effector analysis identified 22, 16, and 22 top-scoring (P<0.05) effectors for the Δsnf1, Δsnf4, and Δsnf1Δsnf4 mutants, respectively (Supplementary Table V; Figure 1G). Using Reporter Effector analysis, we identified significant transcription regulati
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