Coordination of the dynamics of yeast sphingolipid metabolism during the diauxic shift

Abstract

Background: The diauxic shift in yeast requires cells to coordinate a complicated response that involves numerous genes and metabolic processes. It is unknown whether responses of this type are mediated in vivo through changes in a few "key" genes and enzymes, which are mathematically characterized by high sensitivities, or whether they are based on many small changes in genes and enzymes that are not particularly sensitive. In contrast to global assessments of changes in gene or protein interaction networks, we study here control aspects of the diauxic shift by performing a detailed analysis of one specific pathway-sphingolipid metabolism-which is known to have signaling functions and is associated with a wide variety of stress responses.

Results: The approach uses two components: publicly available sets of expression data of sphingolipid genes and a recently developed Generalized Mass Action (GMA) mathematical model of the sphingolipid pathway. In one line of exploration, we analyze the sensitivity of the model with respect to enzyme activities, and thus gene expression. Complementary to this approach, we convert the gene expression data into changes in enzyme activities and then predict metabolic consequences by means of the mathematical model. It was found that most of the sensitivities in the model are low in magnitude, but that some stand out as relatively high. This information was then deployed to test whether the cell uses a few of the very sensitive pathway steps to mount a response or whether the control is distributed throughout the pathway. Pilot experiments confirm qualitatively and in part quantitatively the predictions of a group of metabolite simulations.

Conclusion: The results indicate that yeast coordinates sphingolipid mediated changes during the diauxic shift through an array of small changes in many genes and enzymes, rather than relying on a strategy involving a few select genes with high sensitivity. This study also highlights a novel approach in coupling data mining with mathematical modeling in order to evaluate specific metabolic pathways.

Figure 1

Sphingolipid-glycerolipid model for yeast. Solid boxes represent time dependent variables, italics represent variables assumed to be constant (time independent), dashed boxes represent variables with inhibitory or activating effects. Blue boxes represent metabolite log gains analyzed in this work. The color scale corresponds to the summed absolute values of metabolite log gains for the enzymes of the sphingolipid block listed in Table 2 (see text for details).

Figure 2

Cellular density and external glucose concentration during the time period when genomic expression was measured. mRNA levels at 11 hrs are assumed to correspond to the model of [9]. Adapted from DeRisi ([1], Fig 5A).

Figure 3

"Spider-web" representation of log gains in the model of Fig. 1 at the 11 hr time point. Log gains are summed for ten representative sphingolipid related metabolites or fluxes with respect to the time independent variable blocks listed in Table 2. Overlapping lines correspond to log gains with similar values. a. Metabolite Log Gains of the Precursor block. b. Metabolite Log Gains of the Sphingolipid block. c. Metabolite Log Gains of the Glycerolipid block. d. Flux Log Gains of the Precursor block. e. Flux Log Gains of the Sphingolipid block. f. Flux Log Gains of the Glycerolipid block.

Figure 4

Selected fold changes in steady-state metabolite levels, according to the mathematical model, normalized with respect to metabolite values at 11 hrs (deduced from mRNA profiles in [1]).

Figure 5

Fold change variation in mRNA levels at 9, 13, 15, 17, 19, and 21 hrs, normalized with respect to values during the exponential growth phase (11 hrs) [1]. Glycerolipid block.

Figure 6

Fold change variation in mRNA levels at 9, 13, 15, 17, 19, and 21 hrs, normalized with respect to values during the exponential growth phase (11 hrs) [1]. Sphingolipid block.

Figure 7

Fold change variation in mRNA levels at 9, 13, 15, 17, 19, and 21 hrs, normalized with respect to values during the exponential growth phase (11 hrs) [1]. Precursor block.