Elsevier

Biosystems

Volume 104, Issue 1, April 2011, Pages 63-75
Biosystems

Stoichiometric network reconstruction and analysis of yeast sphingolipid metabolism incorporating different states of hydroxylation

https://doi.org/10.1016/j.biosystems.2011.01.001Get rights and content

Abstract

The first elaborate metabolic model of Saccharomyces cerevisiae sphingolipid metabolism was reconstructed in silico. The model considers five different states of sphingolipid hydroxylation, rendering it unique among other models. It is aimed to clarify the significance of hydroxylation on sphingolipids and hence to interpret the preferences of the cell between different metabolic pathway branches under different stress conditions. The newly constructed model was validated by single, double and triple gene deletions with experimentally verified phenotypes. Calcium sensitivity and deletion mutations that may suppress calcium sensitivity were examined by CSG1 and CSG2 related deletions. The model enabled the analysis of complex sphingolipid content of the plasma membrane coupled with diacylglycerol and phosphatidic acid biosynthesis and ATP consumption in in silico cell. The flux data belonging to these critically important key metabolites are integrated with the fact of phytoceramide induced cell death to propose novel potential drug targets for cancer therapeutics. In conclusion, we propose that IPT1, GDA1, CSG and AUR1 gene deletions may be novel candidates of drug targets for cancer therapy according to the results of flux balance and variability analyses coupled with robustness analysis.

Introduction

Sphingolipids are complex lipids found mostly in the plasma membrane and function as signaling molecules. Their structure is formed around a long chain sphingoid base backbone with an amide-linked fatty acid attached to it. Although there are numerous types of long-chain bases, sphingosine, dihydrosphingosine (DHS) and phytosphingosine (PHS) are the most widely encountered sphingoid bases in nature (Cuvillier and Levade, 2003); the latter two are also the major types of long chain bases of sphingolipid metabolism in Saccharomyces cerevisiae (Dickson and Lester, 1999, Dickson and Lester, 2002).

The pathway starting with the rate limiting step of condensation of serine and palmitoyl-coenzyme A in the endoplasmic reticulum (serine palmitoyl transferase) and continuing up to dihydroceramide is called the de novo sphingolipid synthesis (Hannun et al., 2001, Obeid et al., 2002, Alvarez-Vasquez et al., 2004, Alvarez-Vasquez et al., 2005). The second subsection of the sphingolipid metabolism in S. cerevisiae is the phosphorylation of long chain bases (sphingosine kinase). This pathway is finalized by the breakdown of the phosphorylated long chain bases to fatty acid and phospholipid derivatives (sphingosine phosphate lyase). Another alternative for the phosphorylated long chain bases is the dephosphorylation back to DHS and PHS. The third and last part of the current sphingolipid pathway is the synthesis of complex sphingolipids taking place in the Golgi apparatus. There are three types of complex sphingolipids in S. cerevisiae, namely inositol phosphoryl ceramide (IPC), mannose-inositol phosphorylceramide (MIPC) and mannose-diinositol phosphorylceramide (M(IP)2C) (Dickson and Lester, 1999, Dickson and Lester, 2002, Dickson et al., 2006, Kavun Ozbayraktar and Ulgen, 2009).

Sphingolipid-based therapeutics against cancer is based on the fact that sphingolipids regulate biological processes like cell death and survival playing important roles in cell's fate. There is a delicate balance between different types of sphingolipids since ceramide, sphingosine and sphingosine-1-phosphate are acting in opposing biological processes and therefore termed as sphingolipid rheostat (Spiegel and Milstien, 2002). Ceramide and sphingosine are known as tumor suppressor lipids inducing apoptosis whereas sphingosine-1-phosphate is known as tumor promoting lipid owing to its regulatory function in cell proliferation (Taha et al., 2006, Zeidan and Hannun, 2007). The essential fact of cancer therapeutics with sphingolipid metabolism manipulation is that ceramide accumulation induces apoptosis only in cancerous cell lines, such as leukemia and breast carcinoma cells (Ogretmen and Hannun, 2002).

The pharmacological manipulation of the sphingolipid metabolism in cancer therapeutics necessitates the detailed understanding of the pathway and its underlying principles. Constraint-based modeling, which is one of the gains of genomic revolution, has been used as an identifier of critical reactions of the metabolic system of interest during drug discovery and development studies (Trawick and Schilling, 2006). In the present study, a metabolic model for the sphingolipid metabolism in S. cerevisiae coupled with the reactions of phospholipid and fatty acid metabolisms was developed taking five different states of sphingolipid hydroxylation into account, rendering it unique among other models. It is aimed to clarify the significance of hydroxylation on sphingolipids and hence to interpret the preferences of the cell between different metabolic pathway branches under different stress conditions. By the application of flux balance analysis (FBA), in silico gene deletion simulations were performed to elaborate the capabilities of the present metabolic network against several perturbations. Employing the computational systems biology tools (metabolic control and metabolic pathway analyses) we previously identified several candidate enzymes, to be used as drug targets in novel cancer therapy approaches (Kavun Ozbayraktar and Ulgen, 2010). We found that the enzymes encoded by the genes of INO1, SER2, GUP1, ACB1, CDS1, LCB1/LCB2, LCB4/LCB5, and GAT1/GAT2 have important roles in achieving an increase in ceramide content of the cell and hence inducing apoptosis. The metabolic consequences of the drug actions on these candidate enzymes were also investigated using the newly constructed in silico metabolic model which was validated against experimentally verified deletion phenotypes. The in silico model is further used in proposal of potential drug target enzymes, whose inhibition may induce apoptosis in cancerous cells. The possible flux ranges of each metabolic reaction were calculated by flux variability analysis and the flexibility of the network was then evaluated. The structural robustness of the selected reactions is studied using the concept of minimal cut sets.

Section snippets

Materials and Methods

Yeast (S. cerevisiae) is a good initial point for the investigation of mammalian sphingolipid metabolism as many yeast sphingolipid genes have homologs or orthologs in mammalian cells (Obeid et al., 2002). In the present stoichiometric modeling of the S. cerevisiae sphingolipid metabolism, 60 internal metabolic reactions and 77 intracellular metabolites are taken into account. The main mathematical tools employed are flux balance analysis, flux variability analysis and metabolic pathway

Results and Discussion

In this study, it is aimed (i) to discover the metabolic effects of gene deletions in sphingolipid metabolism via a small-scale stoichiometric model of S. cerevisiae and (ii) to determine the preferences of the cell between different branches of ceramide hydroxylation under several stress conditions. In order to validate the present reconstructed stoichiometric model, the phenotypes of the deletions of numerous genes were simulated and the results were compared with the experimental findings

Conclusions

The regulational aspects of sphingolipids make the sphingolipid metabolism an appropriate target for cancer therapeutics. The delicate metabolic balance between tumor-suppressor and tumor-promoter lipids plays a very important role in cell's fate. A simple organism, S. cerevisiae, is selected as a model organism for the in silico analysis and the first comprehensive stoichiometric model of yeast sphingolipid metabolism is reconstructed. Although there are distinct variations between yeast and

Conflicts of interest

The authors have declared no conflict of interest.

Acknowledgments

The authors thank Dr. Tunahan Cakir for critical reading of the manuscript. The financial supports of Turkish Scientific Research Council (TUBITAK) through project 104M362 and Bogazici University Research Fund through projects 09A501P, 06HA504D are gratefully acknowledged.

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