Tumors overcome the action of the wasting factor ImpL2 by locally elevating Wnt/Wingless.

Tumors often secrete wasting factors associated with atrophy and the degeneration of host tissues. If tumors were to be affected by the wasting factors, mechanisms allowing tumors to evade the adverse effects of the wasting factors must exist, and impairing such mechanisms may attenuate tumors. We use Drosophila midgut tumor models to show that tumors up-regulate Wingless (Wg) to oppose the growth-impeding effects caused by the wasting factor, ImpL2 (insulin-like growth factor binding protein [IGFBP]-related protein). Growth of Yorkie (Yki)-induced tumors is dependent on Wg while either elimination of ImpL2 or elevation of insulin/insulin-like growth factor signaling in tumors revokes this dependency. Notably, Wg augmentation could be a general mechanism for supporting the growth of tumors with elevated ImpL2 and exploited to attenuate muscle degeneration during wasting. Our study elucidates the mechanism by which tumors negate the action of ImpL2 to uphold their growth during cachexia-like wasting and implies that targeting the Wnt/Wg pathway might be an efficient treatment strategy for cancers with elevated IGFBPs.

Yeast 14-3-3 protein functions as a comodulator of transcription by inhibiting coactivator functions.

In eukaryotes combinatorial activation of transcription is an important component of gene regulation. In the budding yeast Saccharomyces cerevisiae, Adr1-Cat8 and Adr1-Oaf1/Pip2 are pairs of activators that act together to regulate two diverse sets of genes. Transcription activation of both sets is regulated positively by the yeast AMP-activated protein kinase homolog, Snf1, in response to low glucose or the presence of a non-fermentable carbon source and negatively by two redundant 14-3-3 isoforms, Bmh1 and Bmh2. Bmh regulates the function of these pairs at a post-promoter binding step by direct binding to Adr1. However, how Bmh regulates transcription after activator binding remains unknown. In the present study we analyzed Bmh-mediated regulation of two sets of genes activated independently by these pairs of activators. We report that Bmh inhibits mRNA synthesis when the second activator is absent. Using gene fusions we show that Bmh binding to the Adr1 regulatory domain inhibits an Adr1 activation domain but not a heterologous activation domain or artificially recruited Mediator, consistent with Bmh acting at a step in transcription downstream of activator binding. Bmh inhibits the assembly and the function of a preinitiation complex (PIC). Gene expression studies suggest that Bmh regulates Adr1 activity through the coactivators Mediator and Swi/Snf. Mediator recruitment appeared to occur normally, but PIC formation and function were defective, suggesting that Bmh inhibits a step between Mediator recruitment and PIC activation.

Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation.

Cells employ multiple levels of regulation, including transcriptional and translational regulation, that drive core biological processes and enable cells to respond to genetic and environmental changes. Small-molecule metabolites are one category of critical cellular intermediates that can influence as well as be a target of cellular regulations. Because metabolites represent the direct output of protein-mediated cellular processes, endogenous metabolite concentrations can closely reflect cellular physiological states, especially when integrated with other molecular-profiling data. Here we develop and apply a network reconstruction approach that simultaneously integrates six different types of data: endogenous metabolite concentration, RNA expression, DNA variation, DNA-protein binding, protein-metabolite interaction, and protein-protein interaction data, to construct probabilistic causal networks that elucidate the complexity of cell regulation in a segregating yeast population. Because many of the metabolites are found to be under strong genetic control, we were able to employ a causal regulator detection algorithm to identify causal regulators of the resulting network that elucidated the mechanisms by which variations in their sequence affect gene expression and metabolite concentrations. We examined all four expression quantitative trait loci (eQTL) hot spots with colocalized metabolite QTLs, two of which recapitulated known biological processes, while the other two elucidated novel putative biological mechanisms for the eQTL hot spots.

Construction of regulatory networks using expression time-series data of a genotyped population.

The inference of regulatory and biochemical networks from large-scale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale gene-expression data implies coregulation and potential gene-gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature.

Toward a global analysis of metabolites in regulatory mutants of yeast.

The AMP-activated protein kinase in yeast, Snf1, coordinates expression and activity of numerous intracellular signaling and developmental pathways, including those regulating cellular differentiation, response to stress, meiosis, autophagy, and the diauxic transition. Snf1 phosphorylates metabolic enzymes and transcription factors to change cellular physiology and metabolism. Adr1 and Cat8, transcription factors that activate gene expression after the diauxic transition, are regulated by Snf1; Cat8 through direct phosphorylation and Adr1 by dephosphorylation in a Snf1-dependent manner. Adr1 and Cat8 coordinately regulate numerous genes encoding enzymes of gluconeogenesis, the glyoxylate cycle, ß-oxidation of fatty acids, and the utilization of alternative fermentable sugars and nonfermentable substrates. To determine the roles of Adr1, Cat8, and Snf1 in metabolism, two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and liquid chromatography coupled to tandem mass spectrometry were used to identify metabolites whose levels change after the diauxic transition in wild-type-, ADR1-, CAT8-, and SNF1-deficient yeast. A discovery-based approach to data analysis utilized chemometric algorithms to identify, quantify, and compare 63 unique metabolites between wild type, adr1∆, cat8∆, adr1∆cat8∆, and snf1∆ strains. The primary metabolites found to differ were those of gluconeogenesis, the glyoxylate and tricarboxylic acid cycles, and amino acid metabolism. In general, good agreement was observed between the levels of metabolites derived from these pathways and the levels of transcripts from the same strains, suggesting that transcriptional control plays a major role in regulating the levels of metabolites after the diauxic transition.

Snf1-independent, glucose-resistant transcription of Adr1-dependent genes in a mediator mutant of Saccharomyces cerevisiae.

Glucose represses transcription of a network of co-regulated genes in Saccharomyces cerevisiae, ensuring that it is utilized before poorer carbon sources are metabolized. Adr1 is a glucose-regulated transcription factor whose promoter binding and activity require Snf1, the yeast homologue of the AMP-activated protein kinase in higher eukaryotes. In this study we found that a temperature-sensitive allele of MED14, a Mediator middle subunit that tethers the tail to the body, allowed a low level of Adr1-independent ADH2 expression that can be enhanced by Adr1 in a dose-dependent manner. A low level of TATA-independent ADH2 expression was observed in the med14-truncated strain and transcription of ADH2 and other Adr1-dependent genes occurred in the absence of Snf1 and chromatin remodeling coactivators. Loss of ADH2 promoter nucleosomes had occurred in the med14 strain in repressing conditions and did not require ADR1. A global analysis of transcription revealed that loss of Med14 function was associated with both up- and down- regulation of several groups of co-regulated genes, with ADR1-dependent genes being the most highly represented in the upregulated class. Expression of most genes was not significantly affected by the loss of Med14 function.

Time-dependent profiling of metabolites from Snf1 mutant and wild type yeast cells.

The effect of sampling time in the context of growth conditions on a dynamic metabolic system was investigated in order to assess to what extent a single sampling time may be sufficient for general application, as well as to determine if useful kinetic information could be obtained. A wild type yeast strain (W) was compared to a snf1Delta mutant yeast strain (S) grown in high-glucose medium (R) and in low-glucose medium containing ethanol (DR). Under these growth conditions, different metabolic pathways for utilizing the different carbon sources are expected to be active. Thus, changes in metabolite levels relating to the carbon source in the growth medium were anticipated. Furthermore, the Snf1 protein kinase complex is required to adapt cellular metabolism from fermentative R conditions to oxidative DR conditions. So, differences in intracellular metabolite levels between the W and S yeast strains were also anticipated. Cell extracts were collected at four time points (0.5, 2, 4, 6 h) after shifting half of the cells from R to DR conditions, resulting in 16 sample classes (WR, WDR, SR, SDR) x (0.5, 2, 4, 6 h). The experimental design provided time course data, so temporal dependencies could be monitored in addition to carbon source and strain dependencies. Comprehensive two-dimensional (2D) gas chromatography coupled to time-of-flight mass spectrometry (GC x GC-TOFMS) was used with discovery-based data mining algorithms ( Anal. Chem. 2006, 78, 5068-5075 (ref 1); J. Chromatogr., A 2008, 1186, 401-411 (ref 2)) to locate regions within the 2D chromatograms (i.e., metabolites) that provided chemical selectivity between the 16 sample classes. These regions were mathematically resolved using parallel factor analysis to positively identify the metabolites and to acquire quantitative results. With these tools, 51 unique metabolites were identified and quantified. Various time course patterns emerged from these data, and principal component analysis (PCA) was utilized as a comparison tool to determine the sources of variance between these 51 metabolites. The effect of sampling time was investigated with separate PCA analyses using various subsets of the data. PCA utilizing all of the time course data, averaged time course data, and each individual time point data set independently were performed to discern the differences. For the yeast strains examined in the current study, data collection at either 4 or 6 h provided information comparable to averaged time course data, albeit with a few metabolites missing using a single sampling time point.

Identification and evaluation of cycling yeast metabolites in two-dimensional comprehensive gas chromatography-time-of-flight-mass spectrometry data.

A yeast metabolome exhibiting oscillatory behavior was analyzed using comprehensive two-dimensional gas chromatography-time-of-flight-mass spectrometry (GC x GC-TOF-MS) and in-house developed data analysis software methodology, referred to as a signal ratio method (S(ratio) method). In this study, 44 identified unique metabolites were found to exhibit cycling, with a depth-of-modulation amplitude greater than three. After the initial locations are found using the S(ratio) software, and identified preliminarily using ChromaTOF software, the refined mass spectra and peak volumes were subsequently obtained using parallel factor analysis (PARAFAC). The peak volumes provided by PARAFAC deconvolution provide a measurement of the cycling depth-of-modulation amplitude that is more accurate than the initial S(ratio) information (which serves as a rapid screening procedure to find the cycling metabolites while excluding peaks that do not cycle). The S(ratio) reported is a rapid method to determine the depth-of-modulation while not constraining the search to specific cycling frequencies. The phase delay of the cycling metabolites ranged widely in relation to the oxygen consumption cycling pattern.

Snf1-Dependent Transcription Confers Glucose-Induced Decay upon the mRNA Product.

In the yeast Saccharomyces cerevisiae, the switch from respiratory metabolism to fermentation causes rapid decay of transcripts encoding proteins uniquely required for aerobic metabolism. Snf1, the yeast ortholog of AMP-activated protein kinase, has been implicated in this process because inhibiting Snf1 mimics the addition of glucose. In this study, we show that the SNF1-dependent ADH2 promoter, or just the major transcription factor binding site, is sufficient to confer glucose-induced mRNA decay upon heterologous transcripts. SNF1-independent expression from the ADH2 promoter prevented glucose-induced mRNA decay without altering the start site of transcription. SNF1-dependent transcripts are enriched for the binding motif of the RNA binding protein Vts1, an important mediator of mRNA decay and mRNA repression whose expression is correlated with decreased abundance of SNF1-dependent transcripts during the yeast metabolic cycle. However, deletion of VTS1 did not slow the rate of glucose-induced mRNA decay. ADH2 mRNA rapidly dissociated from polysomes after glucose repletion, and sequences bound by RNA binding proteins were enriched in the transcripts from repressed cells. Inhibiting the protein kinase A pathway did not affect glucose-induced decay of ADH2 mRNA. Our results suggest that Snf1 may influence mRNA stability by altering the recruitment activity of the transcription factor Adr1.

Genome-wide amplifications caused by chromosomal rearrangements play a major role in the adaptive evolution of natural yeast.

The relative importance of gross chromosomal rearrangements to adaptive evolution has not been precisely defined. The Saccharomyces cerevisiae flor yeast strains offer significant advantages for the study of molecular evolution since they have recently evolved to a high degree of specialization in a very restrictive environment. Using DNA microarray technology, we have compared the genomes of two prominent variants of S. cerevisiae flor yeast strains. The strains differ from one another in the DNA copy number of 116 genomic regions that comprise 38% of the genome. In most cases, these regions are amplicons flanked by repeated sequences or other recombination hotspots previously described as regions where double-strand breaks occur. The presence of genes that confer specific characteristics to the flor yeast within the amplicons supports the role of chromosomal rearrangements as a major mechanism of adaptive evolution in S. cerevisiae. We propose that nonallelic interactions are enhanced by ethanol- and acetaldehyde-induced double-strand breaks in the chromosomal DNA, which are repaired by pathways that yield gross chromosomal rearrangements. This mechanism of chromosomal evolution could also account for the sexual isolation shown among the flor yeast.

Phosphoproteomic analysis identifies proteins involved in transcription-coupled mRNA decay as targets of Snf1 signaling.

Stresses, such as glucose depletion, activate Snf1, the Saccharomyces cerevisiae ortholog of adenosine monophosphate-activated protein kinase (AMPK), enabling adaptive cellular responses. In addition to affecting transcription, Snf1 may also promote mRNA stability in a gene-specific manner. To understand Snf1-mediated signaling, we used quantitative mass spectrometry to identify proteins that were phosphorylated in a Snf1-dependent manner. We identified 210 Snf1-dependent phosphopeptides in 145 proteins. Thirteen of these proteins are involved in mRNA metabolism. Of these, we found that Ccr4 (the major cytoplasmic deadenylase), Dhh1 (an RNA helicase), and Xrn1 (an exoribonuclease) were required for the glucose-induced decay of Snf1-dependent mRNAs that were activated by glucose depletion. Unexpectedly, deletion of XRN1 reduced the accumulation of Snf1-dependent transcripts that were synthesized during glucose depletion. Deletion of SNF1 rescued the synthetic lethality of simultaneous deletion of XRN1 and REG1, which encodes a regulatory subunit of a phosphatase that inhibits Snf1. Mutation of three Snf1-dependent phosphorylation sites in Xrn1 reduced glucose-induced mRNA decay. Thus, Xrn1 is required for Snf1-dependent mRNA homeostasis in response to nutrient availability.

14-3-3 (Bmh) proteins regulate combinatorial transcription following RNA polymerase II recruitment by binding at Adr1-dependent promoters in Saccharomyces cerevisiae.

Adr1 and Cat8 are nutrient-regulated transcription factors in Saccharomyces cerevisiae that coactivate genes necessary for growth in the absence of a fermentable carbon source. Transcriptional activation by Adr1 is dependent on the AMP-activated protein kinase Snf1 and is inhibited by binding of Bmh, yeast 14-3-3 proteins, to the phosphorylated Adr1 regulatory domain. We show here that Bmh inhibits transcription by binding to Adr1 at promoters that contain a preinitiation complex, demonstrating that Bmh-mediated inhibition is not due to nuclear exclusion, inhibition of DNA binding, or RNA polymerase II (Pol II) recruitment. Adr1-dependent mRNA levels under repressing growth conditions are synergistically enhanced in a mutant lacking Bmh and the two major histone deacetylases (HDACs), suggesting that Bmh and HDACs inhibit gene expression independently. The synergism requires Snf1 and Adr1 but not Cat8. Inactivating Bmh or preventing it from binding to Adr1 suppresses the normal requirement for Cat8 at codependent promoters, suggesting that Bmh modulates combinatorial control of gene expression in addition to having an inhibitory role in transcription. Activating Snf1 by deleting Reg1, a Glc7 protein phosphatase regulatory subunit, is lethal in combination with defective Bmh in strain W303, suggesting that Bmh and Snf1 have opposing roles in an essential cellular process.

Integrating external biological knowledge in the construction of regulatory networks from time-series expression data.

BACKGROUND: Inference about regulatory networks from high-throughput genomics data is of great interest in systems biology. We present a Bayesian approach to infer gene regulatory networks from time series expression data by integrating various types of biological knowledge. RESULTS: We formulate network construction as a series of variable selection problems and use linear regression to model the data. Our method summarizes additional data sources with an informative prior probability distribution over candidate regression models. We extend the Bayesian model averaging (BMA) variable selection method to select regulators in the regression framework. We summarize the external biological knowledge by an informative prior probability distribution over the candidate regression models. CONCLUSIONS: We demonstrate our method on simulated data and a set of time-series microarray experiments measuring the effect of a drug perturbation on gene expression levels, and show that it outperforms leading regression-based methods in the literature.

Artificial recruitment of mediator by the DNA-binding domain of Adr1 overcomes glucose repression of ADH2 expression.

The transcription factor Adr1 activates numerous genes in nonfermentable carbon source metabolism. An unknown mechanism prevents Adr1 from stably binding to the promoters of these genes in glucose-grown cells. Glucose depletion leads to Snf1-dependent binding. Chromatin immunoprecipitation showed that the Adr1 DNA-binding domain could not be detected at the ADH2 promoter under conditions in which the binding of the full-length protein occurred. This suggested that an activation domain is required for stable binding, and coactivators may stabilize the interaction with the promoter. Artificial recruitment of Mediator tail subunits by fusion to the Adr1 DNA-binding domain overcame both the inhibition of promoter binding and glucose repression of ADH2 expression. In contrast, an Adr1 DNA-binding domain-Tbp fusion did not overcome glucose repression, although it was an efficient activator of ADH2 expression under derepressing conditions. When Mediator was artificially recruited, ADH2 expression was independent of SNF1, SAGA, and Swi/Snf, whereas ADH2 expression was dependent on these factors with wild-type Adr1. These results suggest that in the presence of glucose, the ADH2 promoter is accessible to Adr1 but that other interactions that occur when glucose is depleted do not take place. Artificial recruitment of Mediator appears to overcome this requirement and to allow stable binding and transcription under normally inhibitory conditions.

Comprehensive analysis of yeast metabolite GC x GC-TOFMS data: combining discovery-mode and deconvolution chemometric software.

The first extensive study of yeast metabolite GC x GC-TOFMS data from cells grown under fermenting, R, and respiring, DR, conditions is reported. In this study, recently developed chemometric software for use with three-dimensional instrumentation data was implemented, using a statistically-based Fisher ratio method. The Fisher ratio method is fully automated and will rapidly reduce the data to pinpoint two-dimensional chromatographic peaks differentiating sample types while utilizing all the mass channels. The effect of lowering the Fisher ratio threshold on peak identification was studied. At the lowest threshold (just above the noise level), 73 metabolite peaks were identified, nearly three-fold greater than the number of previously reported metabolite peaks identified (26). In addition to the 73 identified metabolites, 81 unknown metabolites were also located. A Parallel Factor Analysis graphical user interface (PARAFAC GUI) was applied to selected mass channels to obtain a concentration ratio, for each metabolite under the two growth conditions. Of the 73 known metabolites identified by the Fisher ratio method, 54 were statistically changing to the 95% confidence limit between the DR and R conditions according to the rigorous Student's t-test. PARAFAC determined the concentration ratio and provided a fully-deconvoluted (i.e. mathematically resolved) mass spectrum for each of the metabolites. The combination of the Fisher ratio method with the PARAFAC GUI provides high-throughput software for discovery-based metabolomics research, and is novel for GC x GC-TOFMS data due to the use of the entire data set in the analysis (640 MB x 70 runs, double precision floating point).

Cyclic changes in metabolic state during the life of a yeast cell.

Budding yeast undergo robust oscillations in oxygen consumption during continuous growth in a nutrient-limited environment. Using liquid chromatography-mass spectrometry and comprehensive 2D gas chromatography-mass spectrometry-based metabolite profiling methods, we have determined that the intracellular concentrations of many metabolites change periodically as a function of these metabolic cycles. These results reveal the logic of cellular metabolism during different phases of the life of a yeast cell. They may further indicate that oscillation in the abundance of key metabolites might help control the temporal regulation of cellular processes and the establishment of a cycle. Such oscillations in metabolic state might occur during the course of other biological cycles.

Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry analysis of metabolites in fermenting and respiring yeast cells.

Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry coupled with rapid chemometric analysis were used to identify chemical differences in metabolite extracts isolated from yeast cells either metabolizing glucose (repressed (R) cells) via fermentation or metabolizing ethanol by respiration (derepressed (DR) cells). Principal component analysis (PCA) followed by parallel factor analysis (PARAFAC) in concert with the LECO ChromaTOF software located and identified the differences in composition between the two types of cell extracts and provided a reliable ratio of the metabolite concentrations. In this report, we demonstrate the analytical method developed to provide relatively rapid analysis of three selective mass channels (m/z 73, 205, 387), although in principle all collected mass channels could be analyzed. Twenty-six metabolites that differentiate repressed cells from derepressed cells were identified. The DR/R ratio of metabolite concentrations ranged from 0.02 for glucose to 67 for trehalose. The average biological variation of the sample extracts was 31%. This analysis demonstrates the utility and benefit of using PCA combined with PARAFAC and ChromaTOF software on extremely complex samples to derive useful information from complex three-dimensional chromatographic data objectively and relatively rapidly.

The Reg1-interacting proteins, Bmh1, Bmh2, Ssb1, and Ssb2, have roles in maintaining glucose repression in Saccharomyces cerevisiae.

In Saccharomyces cerevisiae, a type 1 protein phosphatase complex composed of the Glc7 catalytic subunit and the Reg1 regulatory subunit represses expression of many glucose-regulated genes. Here we show that the Reg1-interacting proteins Bmh1, Bmh2, Ssb1, and Ssb2 have roles in glucose repression. Deleting both BMH genes causes partially constitutive ADH2 expression without significantly increasing the level of Adr1 protein, the major activator of ADH2 expression. Adr1 and Bcy1, the regulatory subunit of cAMP-dependent protein kinase, are both required for this effect indicating that constitutive expression in Deltabmh1Deltabmh2 cells uses the same activation pathway that operates in Deltareg1 cells. Deletion of both BMH genes and REG1 causes a synergistic relief from repression, suggesting that Bmh proteins also act independently of Reg1 during glucose repression. A two-hybrid interaction with the Bmh proteins was mapped to amino acids 187-232, a region of Reg1 that is conserved in different classes of fungi. Deleting this region partially releases SUC2 from glucose repression. This indicates a role for the Reg1-Bmh interaction in glucose repression and also suggests a broad role for Bmh proteins in this process. An in vivo Reg1-Bmh interaction was confirmed by copurification of Bmh proteins with HA(3)-TAP-tagged Reg1. The nonconventional heat shock proteins Ssb1 and Ssb2 are also copurified with HA(3)-TAP-tagged Reg1. Deletion of both SSB genes modestly decreases repression of ADH2 expression in the presence of glucose, suggesting that Ssb proteins, perhaps through their interaction with Reg1, play a minor role in glucose repression.

Multiple pathways are co-regulated by the protein kinase Snf1 and the transcription factors Adr1 and Cat8.

ADR1 and CAT8 encode carbon source-responsive transcriptional regulators that cooperatively control expression of genes involved in ethanol utilization. These transcription factors are active only after the diauxic transition, when glucose is depleted and energy-generating metabolism has shifted to the aerobic oxidation of non-fermentable carbon sources. The Snf1 protein kinase complex is required for activation of their downstream target genes described previously. Using DNA microarrays, we determined the extent to which these three factors collaborate in regulating the expression of the yeast genome after glucose depletion. The expression of 108 genes is significantly decreased in the absence of ADR1. The importance of ADR1 during the diauxic transition is illustrated by the observation that expression of almost one-half of the 40 most highly glucose-repressed genes is ADR1-dependent. ADR1-dependent genes fall into a variety of functional classes with carbon metabolism containing the largest number of members. Most of the genes in this class are involved in the oxidation of different non-fermentable carbon sources. These microarray data show that ADR1 coordinates the biochemical pathways that generate acetyl-CoA and NADH from non-fermentable substrates. Only a small number of ADR1-dependent genes are also CAT8-dependent. However, nearly one-half of the ADR1-dependent genes are also dependent on the Snf1 protein kinase for derepression. Many more genes are SNF1-dependent than are either ADR1- or CAT8-dependent suggesting that SNF1 plays a broader role in gene expression than either ADR1 or CAT8. The largest class of SNF1-dependent genes encodes regulatory proteins that could extend SNF1 dependence to additional pathways.