Abstract:
Cryptococcus neoformans is a common opportunistic human pathogen that causes fatal infections, especially for immunocompromised individuals. The computational systems biology approach enables the elucidation of the host-pathogen interaction of pathogens and the development of new drug strategies and treatment methods. The aim of this study is to reconstruct a genome-scale metabolic model speci c to the C.neoformans and to identify potential drug targets by analyzing the metabolic processes of this pathogen with computational methods. The genome-scale metabolic model reconstructed in this study comprises 1267 reactions (1151 internal and 116 exchange reactions). This model also has a total of 1140 metabolites and 649 genes in 8 compartments. The metabolic changes under di erent environmental conditions were investigated by performing ux balance analysis. The ux distribution obtained is consistent with the literature. The performance and basic properties of the model were tested through MEMOTE and COBRA Toolbox. In the consistency part (stoichiometric consistency, mass/charge balance, and connectivity), the model achieved a score of 97%. The reaction, metabolite and single/double gene deletion analyses were applied to nd new drug targets against C.neoformans infections. It was determined that 183 out of 1267 reactions were essential reactions. However, 143 genes and 108 metabolites were found to be essential for C.neoformans. 57 of the essential genes and 12 of the essential metabolites are not present in the human model. Therefore, these metabolites and genes could be potential drug targets. The drug targets determined by reaction, gene and metabolite deletion analyses are consistent with the literature.
