Genome-scale prediction of gene ontology from mass fingerprints reveals new metabolic gene functions

Abstract

Mass-based fingerprinting can characterize unknown strains, however expansion of these methods to predict specific gene functions is lacking. Therefore, rapid mass fingerprinting was developed to functionally profile a comprehensive yeast knockout library. Matrix assisted laser desorption ionization (MALDI)-time of flight (TOF) mass fingerprints of 3,238 Saccharomyces cerevisiae knockouts were digitized for correlation with gene ontology (GO) annotations. Random forests and support vector machine (SVM) algorithms precisely assigned GO accessions with AUC scores all above 0.83. SVM was the best predictor with average true positive and true negative rates of 0.975 and 0.991, respectively. The SVM model suggested new functions for 28 uncharacterized yeast genes. Metabolomics analysis of two knockouts (YDR215C and YLR122C) of uncharacterized genes predicted to be involved in methylation-related metabolism, showed altered intracellular contents of methionine-related metabolites. Increased S-adenosylmethionine in YDR215C highlights potential for enhancement of methylation pathways. These results demonstrate that MALDI-TOF fingerprints can be rapidly digitized, resulting in datasets that enable prediction of microbial genotypes and even the function of specific genes. This fingerprinting method can inform optimal bioproduction chassis selection.