Computational protocol: Metabolic Response of Candida albicans to Phenylethyl Alcohol under Hyphae-Inducing Conditions

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Protocol publication

[…] AMDIS software (NIST, Boulder, CO, USA) was used for deconvoluting GC-MS chromatograms and identifying metabolites using our in-house MCF MS library. The identifications were based on both the MS spectrum of the derivatized metabolite and its respective chromatographic retention time. The relative abundance of identified metabolites was determined by ChemStation (Agilent) using the GC base-peak value of a selected reference ion. These values were normalized by the biomass content in each sample as well as by the abundance of internal standard (2,3,3,3-d4-alanine). A univariate analysis of variance (ANOVA) was applied to determine whether the relative abundance of each identified metabolite was significantly different between growth conditions. Our Pathway Activity Profiling (PAPi) algorithm was used to predict and compare the relative activity of different metabolic pathways in C. albicans during the growth conditions tested based on metabolite profiling results. This programme connects to the KEGG online database (http://www.kegg.com) and uses the number of metabolites identified from each pathway and their relative abundances to predict which metabolic pathway is likely to be active in the cell. The entire data mining, data normalization and pathway activity predictions were automated in R software as described in Smart et al. and Aggio et al. . Graphical representations of the results were generated by gplots and ggplot2 R packages , . […]

Pipeline specifications

Software tools AMDIS, PAPi, gplots, Ggplot2
Application Miscellaneous
Organisms Candida albicans, Saccharomyces cerevisiae
Chemicals Carbon, NAD, NADP, Niacin, Niacinamide, Nitrogen, Nucleotides, Phenylethyl Alcohol, Lactic Acid