Abstract:
Sphingolipids are both structural and regulatory components of the cell, where they control processes decisive in cell’s fate. The pharmacological manipulation of the sphingolipid metabolism in cancer therapeutics necessitates the detailed understanding of the pathway. Different methodologies based on dynamic, stoichiometric and protein interaction information are used to identify potential drug target enzymes among sphingolipid pathway components. All the enzymes in sphingolipid pathway were ranked according to their roles in controlling the metabolic network using metabolic control analysis. The physiologically connected reactions were identified by metabolic pathway analysis. The mathematical tools’ efficiency for drug target identification performed in this study is validated by clinically available drugs. The first elaborate metabolic model of Sacchoramyces cerevisiae sphingolipid metabolism was reconstructed in silico. The model considers five different states of sphingolipid hydroxylation, rendering it unique among other models. 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. Constructing the protein-protein interaction network of sphingolipids in S. cerevisiae enabled us to understand the topological properties of the network of 1591 nodes. 14 novel interactions are predicted using a newly developed integrated methodology employing sequence and structure based computational interaction prediction tools, orthology, expression profiles, co-localization and Gene Ontology terms. The function annotation of 11 uncharacterized proteins of the network is performed using another newly developed multi-dimensional hybrid method which combines the results from modules and neighbors. LCB5 and IPT1 are identified as potential drug targets for cancer, possessing the topological properties of an ideal drug target.
