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The structure and regulation of biosynthesis pathways in Saccharomyces cerevisiae have been detailed extensively. For other hemiascomycetes, genomic sequences are primarily available, whereas biochemical information on them is scarce. The resulting biochemical networks that are used for research in basic science and biotechnology are often biased by data from S. cerevisiae, assuming that there are often implicitly conserved structures between species. We examined the structure of the amino acid biosynthesis network in nine hemiascomycetes, spanning the phylogenetic clade. Differences in the genetic inventory included the presence and absence of isoenzymes and compartmentation of the pathways. Notably, no two hemiascomycetes had identical genetic inventories. For example, the lack of the mitochondrial αIPMS isoenzyme and presence of only one copy of the BCAA aminotransferase in Pichia pastoris indicate a disparately compartmented leucine biosynthesis pathway. Our findings suggest that αIPMS and BCAA aminotransferase are solely located in the cytosol of P. pastoris, requiring correction of the leucine biosynthesis pathway layout in this species. Our results argue for careful use of information from S. cerevisiae and for joint efforts to fill the knowledge gaps in other species. Such analysis will lead to contributions in biotechnology disciplines, such as protein production and compartment engineering.

Volatile organic compounds (VOCs) produced during microbial fermentations determine the flavor of fermented food and are of interest for the production of fragrances or food additives. However, the microbial synthesis of these compounds from simple carbon sources has not been well investigated so far. Here, we analyzed the headspace over glucose minimal salt medium cultures of Saccharomyces cerevisiae using multi-capillary column-ion mobility spectrometry (MCC-IMS). The high sensitivity and fast data acquisition of the MCC-IMS enabled online analysis of the fermentation off-gas and 19 specific signals were determined. To four of these volatile compounds, we could assign the metabolites ethanol, 2-pentanone, isobutyric acid, and 2,3-hexanedione by MCC-IMS measurements of pure standards and cross validation with thermal desorption–gas chromatography-mass spectrometry measurements. Despite the huge biochemical knowledge of the biochemistry of the model organism S. cerevisiae, only the biosynthetic pathways for ethanol and isobutyric acid are fully understood, demonstrating the considerable lack of research of volatile metabolites. As monitoring of VOCs produced during microbial fermentations can give valuable insight into the metabolic state of the organism, fast and non-invasive MCC-IMS analyses provide valuable data for process control.

Rational strain engineering requires solid testing of phenotypes including productivity and ideally contributes thereby directly to our understanding of the genotype-phenotype relationship. Actually, the test step of the strain engineering cycle becomes the limiting step, as ever advancing tools for generating genetic diversity exist. Here, we briefly define the challenge one faces in quantifying phenotypes and summarize existing analytical techniques that partially overcome this challenge. We argue that the evolution of volatile metabolites can be used as proxy for cellular metabolism. In the simplest case, the product of interest is a volatile (e.g., from bulk alcohols to special fragrances) that is directly quantified over time. But also nonvolatile products (e.g., from bulk long-chain fatty acids to natural products) require major flux rerouting that result potentially in altered volatile production. While alternative techniques for volatile determination exist, rather few can be envisaged for medium to high-throughput analysis required for phenotype testing. Here, we contribute a detailed protocol for an ion mobility spectrometry (IMS) analysis that allows volatile metabolite quantification down to the ppb range. The sensitivity can be exploited for small-scale fermentation monitoring. The insights shared might contribute to a more frequent use of IMS in biotechnology, while the experimental aspects are of general use for researchers interested in volatile monitoring.

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