Besides its use for efficient production of recombinant proteins the methylotrophic yeast Pichia pastoris (syn. Komagataella spp.) has been increasingly employed as a platform to produce metabolites of varying origin. We summarize here the impressive methodological developments of the last years to model and analyze the metabolism of P. pastoris, and to engineer its genome and metabolic pathways. Efficient methods to insert, modify or delete genes via homologous recombination and CRISPR/Cas9, supported by modular cloning techniques, have been reported. An outstanding early example of metabolic engineering in P. pastoris was the humanization of protein glycosylation. More recently the cell metabolism was engineered also to enhance the productivity of heterologous proteins. The last few years have seen an increased number of metabolic pathway design and engineering in P. pastoris, mainly towards the production of complex (secondary) metabolites. In this review, we discuss the potential role of P. pastoris as a platform for metabolic engineering, its strengths, and major requirements for future developments of chassis strains based on synthetic biology principles.

The methylotrophic yeast Pichia pastoris (syn. Komagataella spp.) is one of the most important production systems for heterologous proteins. After the first genome sequences were published in 2009, tremendous effort was made to establish systems-level analytical methods. Methylotrophic lifestyle was one of the most thoroughly investigated topics, studied at the levels of transcriptome, proteome and metabolic flux. Also the responses of P. pastoris to environmental stress conditions experienced during high cell density production processes were studied. Metabolomics and flux analysis revealed the plasticity of the cellular metabolism in its adaption to the production of foreign proteins and served as blueprints for subsequent cell engineering and/or process design. The transcriptional response elicited by overexpression of heterologous proteins seems to depend on the nature and complexity of the recombinant product. Based on these data, novel targets for strain engineering could be deduced from transcriptomics and proteomics data mining and effectively enhanced protein secretion. Transcriptional regulation data also served as a valuable resource to identify novel promoters with the desired regulatory characteristics. This review aims to provide a comprehensive overview of systems biology applications in P. pastoris ranging from increased understanding of cell physiology to improving recombinant protein production in this cell factory.

In the field of metabolic engineering 13C-based metabolic flux analysis experiments have proven successful in indicating points of action. As every step of this approach is affected by an inherent error, the aim of the present work is the comprehensive evaluation of factors contributing to the uncertainty of nonnaturally distributed C-isotopologue abundances as well as to the absolute flux value calculation. For this purpose, a previously published data set, analyzed in the course of a 13C labeling experiment studying glycolysis and the pentose phosphate pathway in a yeast cell factory, was used. Here, for isotopologue pattern analysis of these highly polar metabolites that occur in multiple isomeric forms, a gas chromatographic separation approach with preceding derivatization was used. This rendered a natural isotope interference correction step essential. Uncertainty estimation of the resulting C-isotopologue distribution was performed according to the EURACHEM guidelines with Monte Carlo simulation. It revealed a significant increase for low-abundance isotopologue fractions after application of the necessary correction step. For absolute flux value estimation, isotopologue fractions of various sugar phosphates, together with the assessed uncertainties, were used in a metabolic model describing the upper part of the central carbon metabolism. The findings pinpointed the influence of small isotopologue fractions as sources of error and highlight the need for improved model curation. Graphical abstractᅟ Electronic supplementary materialThe online version of this article (10.1007/s00216-018-1017-7) contains supplementary material, which is available to authorized users.