Metabolomics of Pichia pastoris: impact of buffering conditions on the kinetics and nature of metabolite loss during quenching.

Mass spectrometry-based metabolomic profiling is a powerful strategy to quantify the concentrations of numerous primary metabolites in parallel. To avoid distortion of metabolite concentrations, quenching is applied to stop the cellular metabolism instantly. For yeasts, cold methanol quenching is accepted to be the most suitable method to stop metabolism, while keeping the cells intact for separation from the supernatant. During this treatment, metabolite loss may occur while the cells are suspended in the quenching solution. An experiment for measuring the time-dependent loss of selected primary metabolites in differently buffered quenching solutions was conducted to study pH and salt concentration-dependent effects. Molecular properties of the observed metabolites were correlated with the kinetics of loss to gain insight into the mechanisms of metabolite leakage. Size and charge-related properties play a major role in controlling metabolite loss. We found evidence that interaction with the cell wall is the main determinant to retain a molecule inside the cell. Besides suggesting an improved quenching protocol to keep loss at a minimum, we could establish a more general understanding of the process of metabolite loss during quenching, which will allow to predict optimal conditions for hitherto not analysed metabolites.

INCAWrapper: a Python wrapper for INCA for seamless data import, -export, and -processing.

Motivation: INCA is a powerful tool for metabolic flux analysis, however, import and export of data and results can be tedious and limit the use of INCA in automated workflows. Results: The INCAWrapper enables the use of INCA purely through Python, which allows the use of INCA in common data science workflows. Availability and implementation: The INCAWrapper is implemented in Python and can be found at https://github.com/biosustain/incawrapper. It is freely available under an MIT License. To run INCA, the user needs their own MATLAB and INCA licenses. INCA is freely available for noncommercial use at mfa.vueinnovations.com.

Proceedings of the EuBIC-MS developers meeting 2023.

The 2023 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers Meeting was held from January 15th to January 20th, 2023, in Congressi Stefano Franscin at Monte Verità in Ticino, Switzerland. The participants were scientists and developers working in computational mass spectrometry (MS), metabolomics, and proteomics. The 5-day program was split between introductory keynote lectures and parallel hackathon sessions focusing on "Artificial Intelligence in proteomics" to stimulate future directions in the MS-driven omics areas. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts and actively contribute to highly relevant research projects. We successfully produced several new tools applicable to the proteomics community by improving data analysis and facilitating future research.

Customizing amino acid metabolism of Pichia pastoris for recombinant protein production.

Amino acids are the building blocks of proteins. In this respect, a reciprocal effect of recombinant protein production on amino acid biosynthesis as well as the impact of the availability of free amino acids on protein production can be anticipated. In this study, the impact of engineering the amino acid metabolism on the production of recombinant proteins was investigated in the yeast Pichia pastoris (syn Komagataella phaffii). Based on comprehensive systems-level analyses of the metabolomes and transcriptomes of different P. pastoris strains secreting antibody fragments, cell engineering targets were selected. Our working hypothesis that increasing intracellular amino acid levels could help unburden cellular metabolism and improve recombinant protein production was examined by constitutive overexpression of genes related to amino acid metabolism. In addition to 12 genes involved in specific amino acid biosynthetic pathways, the transcription factor GCN4 responsible for regulation of amino acid biosynthetic genes was overexpressed. The production of the used model protein, a secreted carboxylesterase (CES) from Sphingopyxis macrogoltabida, was increased by overexpression of pathway genes for alanine and for aromatic amino acids, and most pronounced, when overexpressing the regulator GCN4. The analysis of intracellular amino acid levels of selected clones indicated a direct linkage of improved recombinant protein production to the increased availability of intracellular amino acids. Finally, fed batch cultures showed that overexpression of GCN4 increased CES titers 2.6-fold, while the positive effect of other amino acid synthesis genes could not be transferred from screening to bioreactor cultures.

An automated workflow for multi-omics screening of microbial model organisms.

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation might enable to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

Full activation of thermogenesis in brown adipocytes requires Basigin action.

Exploring mechanisms responsible for brown adipose tissue's (BAT) high metabolic activity is crucial to exploit its energy-dissipating ability for therapeutic purposes. Basigin (Bsg), a multifunctional highly glycosylated transmembrane protein, was recently proposed as one of the 98 critical markers allowing to distinguish 'white' and 'brown' adipocytes, yet its function in thermogenic brown adipocytes is unknown. Here, we report that Bsg is negatively associated with obesity in mice. By contrast, Bsg expression increased in the mature adipocyte fraction of BAT upon cold acclimation. Additionally, Bsg levels were highly induced during brown adipocyte maturation in vitro and were further increased upon ß-adrenergic stimulation in a HIF-1α-dependent manner. siRNA-mediated Bsg gene silencing in cultured brown adipocytes did not impact adipogenesis nor mitochondrial function. However, a significant decrease in mitochondrial respiration, lipolysis and Ucp1 transcription was observed in adipocytes lacking Bsg, when activated by norepinephrine. Furthermore, using gas chromatography/mass spectrometry-time-of-flight analysis to assess the composition of cellular metabolites, we demonstrate that brown adipocytes lacking Bsg have lower levels of intracellular lactate and acetoacetate. Bsg was additionally required to regulate intracellular AcAc and tricarboxylic acid cycle intermediate levels in NE-stimulated adipocytes. Our study highlights the critical role of Bsg in active brown adipocytes, possibly by controlling cellular metabolism.

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis.

Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.