Dekkera bruxellensis, a non-conventional ethanol production yeast

Dekkera bruxellensis has been shown to outcompete an initial inoculum of Saccharomyces cerevisiae in several ethanol production plants, which nevertheless had a high efficiency in one of the monitored processes. Co-occurrence of D. bruxellensis with lactic acid bacteria (LAB) Lactobacillus vini has been observed. The aim of this thesis was to broaden the knowledge on D. bruxellensis physiology in respect to its high competitiveness. Global gene expression analysis of D. bruxellensis under conditions similar to those in which it outcompeted S. cerevisiae was performed by whole transcriptome sequencing. Low expression of genes involved in glycerol biosynthesis, and expression of NADH-ubiquinone reductase (complex I) are probably the basis for an efficient energy metabolism. Genes of putative high affinity glucose transporters might be involved in the efficient glucose transport of D. bruxellensis. D. bruxellensis also has a good potential to ferment lignocellulose hydrolysate to ethanol. Adaptation to lignocellulose hydrolysate inhibitors by pre-cultivation was demonstrated. Adapted cells had a shorter lag phase and produced higher amounts of ethanol compared to non-adapted cells. The role of L. vini during co-cultivation with D. bruxellensis or S. cerevisiae was also investigated. Formation of LAB–yeast cell aggregates consisting of a bacterial core with an outer layer of yeast cells was identified. It was noted that addition of mannose to the aggregates dissolved them, but higher mannose amounts were required to inhibit co-flocculation between L. vini and S. cerevisiae compared to L. vini and D. bruxellensis. Growth and metabolite profiles of D. bruxellensis during cultivation on different combinations of carbon and nitrogen sources were studied. Repression of genes involved in nitrate assimilation in D. bruxellensis under oxygen-limited conditions in presence of ammonium was shown. In conclusion, D. bruxellensis has a great potential for industrial ethanol production due to a highly efficient energy metabolism, adaptability to lignocellulose hydrolysate, utilisation of an alternative nitrogen source and robustness against bacterial contaminants

LGEM+^\text{+}: a first-order logic framework for automated improvement of metabolic network models through abduction

Scientific discovery in biology is difficult due to the complexity of the systems involved and the expense of obtaining high quality experimental data. Automated techniques are a promising way to make scientific discoveries at the scale and pace required to model large biological systems. A key problem for 21st century biology is to build a computational model of the eukaryotic cell. The yeast Saccharomyces cerevisiae is the best understood eukaryote, and genome-scale metabolic models (GEMs) are rich sources of background knowledge that we can use as a basis for automated inference and investigation. We present LGEM+, a system for automated abductive improvement of GEMs consisting of: a compartmentalised first-order logic framework for describing biochemical pathways (using curated GEMs as the expert knowledge source); and a two-stage hypothesis abduction procedure. We demonstrate that deductive inference on logical theories created using LGEM+, using the automated theorem prover iProver, can predict growth/no-growth of S. cerevisiae strains in minimal media. LGEM+ proposed 2094 unique candidate hypotheses for model improvement. We assess the value of the generated hypotheses using two criteria: (a) genome-wide single-gene essentiality prediction, and (b) constraint of flux-balance analysis (FBA) simulations. For (b) we developed an algorithm to integrate FBA with the logic model. We rank and filter the hypotheses using these assessments. We intend to test these hypotheses using the robot scientist Genesis, which is based around chemostat cultivation and high-throughput metabolomics.Comment: 15 pages, one figure, two tables, two algorithm

A Genetic Trap in Yeast for Inhibitors of SARS-CoV-2 Main Protease

The ongoing COVID-19 pandemic urges searches for antiviral agents that can block infection or ameliorate its symptoms. Using dissimilar search strategies for new antivirals will improve our overall chances of finding effective treatments. Here, we have established an experimental platform for screening of small molecule inhibitors of the SARS-CoV-2 main protease in Saccharomyces cerevisiae cells, genetically engineered to enhance cellular uptake of small molecules in the environment. The system consists of a fusion of the Escherichia coli toxin MazF and its antitoxin MazE, with insertion of a protease cleavage site in the linker peptide connecting the MazE and MazF moieties. Expression of the viral protease confers cleavage of the MazEF fusion, releasing the MazF toxin from its antitoxin, resulting in growth inhibition. In the presence of a small molecule inhibiting the protease, cleavage is blocked and the MazF toxin remains inhibited, promoting growth. The system thus allows positive selection for inhibitors. The engineered yeast strain is tagged with a fluorescent marker protein, allowing precise monitoring of its growth in the presence or absence of inhibitor. We detect an established main protease inhibitor by a robust growth increase, discernible down to 1 mM. The system is suitable for robotized large-scale screens. It allows in vivo evaluation of drug candidates and is rapidly adaptable for new variants of the protease with deviant site specificities. IMPORTANCE The COVID-19 pandemic may continue for several years before vaccination campaigns can put an end to it globally. Thus, the need for discovery of new antiviral drug candidates will remain. We have engineered a system in yeast cells for the detection of small molecule inhibitors of one attractive drug target of SARS-CoV-2, its main protease, which is required for viral replication. The ability to detect inhibitors in live cells brings the advantage that only compounds capable of entering the cell and remain stable there will score in the system. Moreover, because of its design in yeast cells, the system is rapidly adaptable for tuning the detection level and eventual modification of the protease cleavage site in the case of future mutant variants of the SARSCoV-2 main protease or even for other proteases

Assembly and analysis of the genome sequence of the yeast Brettanomyces naardenensis CBS 7540

Brettanomyces naardenensis is a spoilage yeast with potential for biotechnological applications for production of innovative beverages with low alcohol content and high attenuation degree. Here, we present the first annotated genome of B. naardenensis CBS 7540. The genome of B. naardenensis CBS 7540 was assembled into 76 contigs, totaling 11,283,072 nucleotides. In total, 5168 protein-coding sequences were annotated. The study provides functional genome annotation, phylogenetic analysis, and discusses genetic determinants behind notable stress tolerance and biotechnological potential of B. naardenensis

Proteome analysis of xylose metabolism in Rhodotorula toruloides during lipid production

Background: Rhodotorula toruloides is a promising platform organism for production of lipids from lignocellulosic substrates. Little is known about the metabolic aspects of lipid production from the lignocellolosic sugar xylose by oleaginous yeasts in general and R. toruloides in particular. This study presents the first proteome analysis of the metabolism of R. toruloides during conversion of xylose to lipids. Results: Rhodotorula toruloides cultivated on either glucose or xylose was subjected to comparative analysis of its growth dynamics, lipid composition, fatty acid profiles and proteome. The maximum growth and sugar uptake rate of glucose-grown R. toruloides cells were almost twice that of xylose-grown cells. Cultivation on xylose medium resulted in a lower final biomass yield although final cellular lipid content was similar between glucose- and xylose-grown cells. Analysis of lipid classes revealed the presence of monoacylglycerol in the early exponential growth phase as well as a high proportion of free fatty acids. Carbon source-specific changes in lipid profiles were only observed at early exponential growth phase, where C18 fatty acids were more saturated in xylose-grown cells. Proteins involved in sugar transport, initial steps of xylose assimilation and NADPH regeneration were among the proteins whose levels increased the most in xylose-grown cells across all time points. The levels of enzymes involved in the mevalonate pathway, phospholipid biosynthesis and amino acids biosynthesis differed in response to carbon source. In addition, xylose-grown cells contained higher levels of enzymes involved in peroxisomal beta-oxidation and oxidative stress response compared to cells cultivated on glucose. Conclusions: The results obtained in the present study suggest that sugar import is the limiting step during xylose conversion by R. toruloides into lipids. NADPH appeared to be regenerated primarily through pentose phosphate pathway although it may also involve malic enzyme as well as alcohol and aldehyde dehydrogenases. Increases in enzyme levels of both fatty acid biosynthesis and beta-oxidation in xylose-grown cells was predicted to result in a futile cycle. The results presented here are valuable for the development of lipid production processes employing R. toruloides on xylose-containing substrates

Identification and characterisation of two high-affinity glucose transporters from the spoilage yeast Brettanomyces bruxellensis

The yeast Brettanomyces bruxellensis (syn. Dekkera bruxellensis) is an emerging and undesirable contaminant in industrial low-sugar ethanol fermentations that employ the yeast Saccharomyces cerevisiae. High-affinity glucose import in B. bruxellensis has been proposed to be the mechanism by which this yeast can outcompete S. cerevisiae. The present study describes the characterization of two B. bruxellensis genes (BHT1 and BHT3) believed to encode putative high-affinity glucose transporters. In vitro-generated transcripts of both genes as well as the S. cerevisiae HXT7 high-affinity glucose transporter were injected into Xenopus laevis oocytes and subsequent glucose uptake rates were assayed using 14C-labelled glucose. At 0.1 mM glucose, Bht1p was shown to transport glucose five times faster than Hxt7p. pH affected the rate of glucose transport by Bht1p and Bht3p, indicating an active glucose transport mechanism that involves proton symport. These results suggest a possible role for BHT1 and BHT3 in the competitive ability of B. bruxellensis

Interpreting protein abundance in Saccharomyces cerevisiae through relational learning.

Funder: Knut and Alice Wallenberg Foundation; DOI: https://doi.org/10.13039/501100004063MOTIVATION: Proteomic profiles reflect the functional readout of the physiological state of an organism. An increased understanding of what controls and defines protein abundances is of high scientific interest. Saccharomyces cerevisiae is a well-studied model organism, and there is a large amount of structured knowledge on yeast systems biology in databases such as the Saccharomyces Genome Database, and highly curated genome-scale metabolic models like Yeast8. These datasets, the result of decades of experiments, are abundant in information, and adhere to semantically meaningful ontologies. RESULTS: By representing this knowledge in an expressive Datalog database we generated data descriptors using relational learning that, when combined with supervised machine learning, enables us to predict protein abundances in an explainable manner. We learnt predictive relationships between protein abundances, function and phenotype; such as α-amino acid accumulations and deviations in chronological lifespan. We further demonstrate the power of this methodology on the proteins His4 and Ilv2, connecting qualitative biological concepts to quantified abundances. AVAILABILITY AND IMPLEMENTATION: All data and processing scripts are available at the following Github repository: https://github.com/DanielBrunnsaker/ProtPredict

LGEM+: A First-Order Logic Framework for Automated Improvement of Metabolic Network Models Through Abduction

Scientific discovery in biology is difficult due to the complexity of the systems involved and the expense of obtaining high quality experimental data. Automated techniques are a promising way to make scientific discoveries at the scale and pace required to model large biological systems. A key problem for 21st century biology is to build a computational model of the eukaryotic cell. The yeast Saccharomyces cerevisiae is the best understood eukaryote, and genome-scale metabolic models (GEMs) are rich sources of background knowledge that we can use as a basis for automated inference and investigation.We present LGEM+, a system for automated abductive improvement of GEMs consisting of: a compartmentalised first-order logic framework for describing biochemical pathways (using curated GEMs as the expert knowledge source); and a two-stage hypothesis abduction procedure.We demonstrate that deductive inference on logical theories created using LGEM+, using the automated theorem prover iProver, can predict growth/no-growth of S. cerevisiae strains in minimal media. LGEM+ proposed 2094 unique candidate hypotheses for model improvement. We assess the value of the generated hypotheses using two criteria: (a) genome-wide single-gene essentiality prediction, and (b) constraint of flux-balance analysis (FBA) simulations. For (b) we developed an algorithm to integrate FBA with the logic model. We rank and filter the hypotheses using these assessments. We intend to test these hypotheses using the robot scientist Genesis, which is based around chemostat cultivation and high-throughput metabolomics

Genome-scale model of Rhodotorula toruloides metabolism.

The basidiomycete red yeast Rhodotorula toruloides is a promising platform organism for production of biooils. We present rhto-GEM, the first genome-scale model (GEM) of R. toruloides metabolism, that was largely reconstructed using RAVEN toolbox. The model includes 852 genes, 2,731 reactions, and 2,277 metabolites, while lipid metabolism is described using the SLIMEr formalism allowing direct integration of lipid class and acyl chain experimental distribution data. The simulation results confirmed that the R. toruloides model provides valid growth predictions on glucose, xylose, and glycerol, while prediction of genetic engineering targets to increase production of linolenic acid, triacylglycerols, and carotenoids identified genes-some of which have previously been engineered to successfully increase production. This renders rtho-GEM valuable for future studies to improve the production of other oleochemicals of industrial relevance including value-added fatty acids and carotenoids, in addition to facilitate system-wide omics-data analysis in R. toruloides. Expanding the portfolio of GEMs for lipid-accumulating fungi contributes to both understanding of metabolic mechanisms of the oleaginous phenotype but also uncover particularities of the lipid production machinery in R. toruloides

High-throughput metabolomics for the design and validation of a diauxic shift model

Abstract Saccharomyces cerevisiae is a very well studied organism, yet ∼20% of its proteins remain poorly characterized. Moreover, recent studies seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only automation but fully autonomous systems in which active learning is applied to guide high-throughput experimentation. Development of tools and methods for these types of systems is of paramount importance. In this study we use constrained dynamical flux balance analysis (dFBA) to select ten regulatory deletant strains that are likely to have previously unexplored connections to the diauxic shift. We then analyzed these deletant strains using untargeted metabolomics, generating profiles which were then subsequently investigated to better understand the consequences of the gene deletions in the metabolic reconfiguration of the diauxic shift. We show that metabolic profiles can be utilised to not only gaining insight into cellular transformations such as the diauxic shift, but also on regulatory roles and biological consequences of regulatory gene deletion. We also conclude that untargeted metabolomics is a useful tool for guidance in high-throughput model improvement, and is a fast, sensitive and informative approach appropriate for future large-scale functional analyses of genes. Moreover, it is well-suited for automated approaches due to relative simplicity of processing and the potential to make massively high-throughput