Unraveling condition-dependent networks of transcription factors that control metabolic pathway activity in yeast

While typically many expression levels change in transcription factor mutants, only few of these changes lead to functional changes. The predictive capability of expression and DNA binding data for such functional changes in metabolism is very limited. Large-scale 13C-flux data reveal the condition specificity of transcriptional control of metabolic function. Transcription control in yeast focuses on the switch between respiration and fermentation. Follow-up modeling on the basis of transcriptomics and proteomics data suggest the newly discovered Gcn4 control of respiration to be mediated via PKA and Snf1.
Effective control and modulation of cellular behavior is of paramount importance in medicine (Kreeger and Lauffenburger, 2010) and biotechnology (Haynes and Silver, 2009), and requires profound understanding of control mechanisms. In this study, we aim to elucidate the extent to which transcription factors control the operation of yeast metabolism. As a quantitative readout of metabolic function, we monitored the traffic of small molecules through various pathways of central metabolism by 13C-flux analysis (Sauer, 2006). The choosen growth conditions represent two different regulatory states of reduced (galactose) and maximal carbon source repression (glucose), as well as a different nitrogen metabolism and two common, permanent stress conditions. Depending on the growth condition, between 7 and 13% of the deleted transcription factors altered the determined flux ratios (Figure 3). Of the six quantified flux ratios, only the glycolysis/pentose phosphate pathway split, and the convergent ratio of anaplerosis and TCA cycle were controlled by the deleted transcription factors. Thus, we concluded that 23 transcription factors control flux distributions under at least one of the tested growth conditions, leading to 42 condition-dependent interactions of transcription factors with metabolic pathway activity (Figure 4). With two exceptions, all other identified transcription factors interactions controlled the TCA cycle flux. This condition-specific active control of metabolic function could not have been predicted from DNA binding and expression data; that is, 26.1% false negatives, 48.6% true positives. Of the 23 transcription factors that controlled TCA cycle flux distributions under the tested conditions, only Bas1, Gcn4, Gcr2 and Pho2 exerted control under more than one condition. We identified Cit1, Mdh1 and Idh1/2 with a proteomics approach as the relevant target enzyme that increase the TCA cycle flux. Next, we asked whether Bas1, Gcr2, Gcn4 and Pho2 act directly on the TCA cycle or mediate their effect indirectly. Based on the transcriptomics data, the pattern of differentially activated transcription factors inferred by the differential expression of their target genes suggested reduced glucose repression in all four mutants as the common mechanism. Starting from the currently largest set of 13C-based flux distributions, we identified networks of individual transcription factors that control metabolic pathway activity. These networks of active metabolic control have the following properties. First, they are highly condition dependent, as at most four transcription factors control the same metabolic flux distribution under more than one growth conditions. Second, they focus almost exclusively on the TCA cycle, thereby controlling the switch between respiratory and fermentative metabolism. Third, with four to 14 active transcription factors, they are small compared with gene regulation networks that were obtained from expression and DNA binding data. For the metabolic network studied here, robustness is also apparent from the fact that upregulated TCA cycle fluxes were not sufficient to achieve full respiratory metabolism; that is, absent or low ethanol formation. Several explanations could potentially explain the observed robustness. The most likely explanation is that environmental signals might be transmitted by different signaling pathways to several transcription factors, whose orchestrated action on multiple target genes is necessary to achieve a functional flux response. This hypothesis would explain why several transcription factors exert flux effects on the same pathway, but each flux effect is relatively small, as further, coordinated manipulations would be necessary to further improve the respiratory flux. Our findings demonstrate the importance of identifying and quantifying the extent to which regulatory effectors alter cellular function.
Which transcription factors control the distribution of metabolic fluxes under a given condition? We address this question by systematically quantifying metabolic fluxes in 119 transcription factor deletion mutants of Saccharomyces cerevisiae under five growth conditions. While most knockouts did not affect fluxes, we identified 42 condition-dependent interactions that were mediated by a total of 23 transcription factors that control almost exclusively the cellular decision between respiration and fermentation. This relatively sparse, condition-specific network of active metabolic control contrasts with the much larger gene regulation network inferred from expression and DNA binding data. Based on protein and transcript analyses in key mutants, we identified three enzymes in the tricarboxylic acid cycle as the key targets of this transcriptional control. For the transcription factor Gcn4, we demonstrate that this control is mediated through the PKA and Snf1 signaling cascade. The discrepancy between flux response predictions, based on the known regulatory network architecture and our functional 13C-data, demonstrates the importance of identifying and quantifying the extent to which regulatory effectors alter cellular functions.