A comparison of direct infusion MS and GC‐MS for metabolic footprinting of yeast mutants

Abstract

Recent technical advances in mass spectrometry (MS) have propelled this technology to the forefront of methods employed in metabolome analysis. Here, we compare two distinct analytical approaches based on MS for their potential in revealing specific metabolic footprints of yeast single-deletion mutants. Filtered fermentation broth samples were analyzed by GC-MS and direct infusion ESI-MS. The potential of both methods in producing specific and, therefore, discriminant metabolite profiles was evaluated using samples from several yeast deletion mutants grown in batch-culture conditions with glucose as the carbon source. The mutants evaluated were cat8, gln3, ino2, opi1, and nil1, all with deletion of genes involved in nutrient sensing and regulation. From the analysis, we found that both methods can be used to classify mutants, but the classification depends on which metabolites are measured. Thus, the GC-MS method is good for classification of mutants with altered nitrogen regulation as it primarily measures amino acids, whereas this method cannot classify mutants involved in regulation of phospholipids metabolism as well as the direct infusion MS (DI-MS) method. From the analysis, we find that it is possible to discriminate the mutants in both the exponential and stationary growth phase, but the data from the exponential growth phase provide more physiological relevant information. Based on the data, we identified metabolites that are primarily involved in discrimination of the different mutants, and hereby providing a link between high-throughput metabolome analysis, strain classification, and physiology.

1H NMR and GC-MS metabolic fingerprinting of developmental stages of Rhizoctonia solani sclerotia

“…In spite of the importance of fungal metabolites, thorough assessment of their diverse metabolic landscape using metabolomic approaches is still in its infancy and has not been fully exploited. Global metabolite analysis for drug discovery, mycotoxin characterization and taxonomy of industrially and medically important yeast, yeast-like fungi and selected filamentous fungi is limited to few studies in which a variety of analytical platforms such as electrospray MS (Smedsgaard and Nielsen 2004), 1 H NMR (Forgue et al 2006;Sorrell et al 2006), GC-MS (Mas et al 2007) or GCxGC-TOF MS (Mohler et al 2007) were applied.…”

Time-Resolved Metabolic Footprinting for Nonlinear Modeling of Bacterial Substrate Utilization

“…It is therefore unsurprising that these patterns change with growth phase (1,30). Despite this fact, it is currently common practice to sample only at one or two time points, mostly the end of growth, in stationary phase (see, for example, reference 48) and/or in mid-exponential phase (41,52). In contrast, there is ample evidence that cellular biochemistry changes during growth (1,3,8,39).…”

Evaluation of automated sample preparation, retention time locked gas chromatography–mass spectrometry and data analysis methods for the metabolomic study of Arabidopsis species

“…Gas chromatography combined with mass spectrometry (GC-MS) is suitable for the analysis of small molecules such as amino acids, amines, sugars, organic acids, fatty acids, and sterols, in metabolomic studies [1][2][3][4][5][6][7][8][9][10]. For the analysis of these polar molecules, derivatisation is however needed and, to this, oximation combined with silylation is often selected [11,12].…”