Genome scale model reconstruction of the methylotrophic yeast Ogataea polymorpha

Ogataea polymorpha is a thermotolerant, methylotrophic yeast with significant industrial applications. It is a promising host to generate platform chemicals from methanol, derived e.g. from carbon capture and utilization streams. Full development of the organism into a production strain requires additional strain design, supported by metabolic modeling on the basis of a genome-scale metabolic model. However, to date, no genome-scale metabolic model is available for O. polymorpha. To overcome this limitation, we used a published reconstruction of the closely related yeast Pichia pastoris as reference and corrected reactions based on KEGG annotations. Additionally, we conducted phenotype microarray experiments to test O. polymorpha’s metabolic capabilities to grown on or respire 192 different carbon sources. Over three-quarter of the substrate usage was correctly reproduced by the model. However, O. polymorpha failed to metabolize eight substrates and gained 38 new substrates compared to the P. pastoris reference model. To enable the usage of these compounds, metabolic pathways were inferred from literature and database searches and potential enzymes and genes assigned by conducting BLAST searches. To facilitate strain engineering and identify beneficial mutants, gene-protein-reaction relationships need to be included in the model. Again, we used the P. pastoris model as reference to extend the O. polymorpha model with this relevant information. The final metabolic model of O. polymorpha supports the engineering of synthetic metabolic capabilities and enabling the optimization of production processes, thereby supporting a sustainable future methanol econom

Model-guided metabolic engineering of Pseudomonas taiwanensis VLB120 for the production of methyl ketones

Aliphatic methyl ketones are discussed as promising novel diesel blendstocks because of their favorable cetane numbers. To achieve sustainable production of these compounds, bio-based production in engineered microbes is followed and successful synthesis in Escherichia coli1,2,3 and Pseudomonas putida4 has recently been shown. In this presentation, we report on the metabolic engineering of Pseudomonas taiwanensis VLB1205 for the production of saturated and monounsaturated medium chain methyl ketones (C11, C13, C15, C17). Major arguments for the use of this microbe are its metabolic versatility, high tolerance of organic solvents5 and ease of cultivation. P. taiwanensis VLB120 can grow on various carbon sources besides glucose such as glycerol, an important by-product of biodiesel production, as well as on major components of biomass hydrolysate such as xylose, organic acids and aromatic compounds, e.g., 4-hydroxybenzoate4. Further, its superior redox cofactor regeneration capability6 might benefit the synthesis of the reduced, aliphatic target compounds. The transformation of P. taiwanensis VLB120 into a microbial cell factory for methyl ketone production was achieved by: (i) overproduction of the fatty acyl-CoA synthetase FadB to increase acyl-CoA availability, (ii) oxidation of acyl-CoA to a trans-2-enoyl-CoA by a heterologously expressed acyl-CoA oxidase from Micrococcus luteus, (iii) conversion of this intermediate to β-hydroxyacyl-CoA and further oxidation to a β -ketoacyl-CoA by overexpression of the native fadB gene, (iv) increased thioesterase activity by overexpression of fadM to form free β -keto acids, which spontaneously decarboxylate to methyl ketones. The 1st generation production strain yielded 550 mg L-1aq methyl ketones in a batch fermentation with in situ product extraction into a second organic layer of n-decane. Further strain optimization was guided by metabolic modeling, which suggested an additional deletion of the acyl-CoA thioesterase II (tesB). TesB hydrolyzes acyl-CoA to free fatty acids, hence, reverses the desired FadD reaction. In a simple batch fermentation, the proposed gene deletion resulted in a 2.5-fold increased product titer of 1.4 g L-1aq while 9.4 g L-1aq were reached in fed-batch cultivations. Additional, successful strategies tested in parallel were the deletion of the pha operon, responsible for polyhydroxyalkanoate synthesis and deletion of a fadA homologue in the 1st generation production strain, with the later resulting in an even 4-fold improvement of the product titer. While the production of 9.4 g L-1aq is already the highest reported titer of recombinantly produced methyl ketones so far, consolidation of all successfully tested engineering strategies holds great promise to significantly boost methyl ketone production in P. taiwanensis VLB120 to even higher titers. Overall, the results of this study underline the high potential of P. taiwanensis VLB120 for the production of methyl ketones and highlight model-guided metabolic engineering as a means to rationalize and accelerate strain optimization efforts. 1Dong et al. 2018: doi:10.1101/496497 2Goh et al. 2012: doi: 10.1128/AEM.06785-11 3Goh et al. 2014: doi: 10.1016/j.ymben.2014.09.003. 4Goh et al. 2018: doi: 10.1002/bit.26558. 5Rühl et al. 2009: doi: 10.1128/AEM.00225-09 6Blank et al. 2008: doi: 10.1111/j.1742-4658.2008.06648.x

Cyclic triterpenoid production with tailored Saccharomyces cerevisiae

Triterpenoids are secondary plant metabolites derived from squalene and consist of six isoprene units (C30). Many of them or their synthetic derivatives are currently being investigated as medicinal products for various diseases. The cyclic triterpenoid betulinic acid is of special interest for the pharmaceutical and nutritional industry as it has antiretroviral, antimalarial, and anti-inflammatory properties and has potential as an anticancer agent (Muffler et al. 2011, Mullauer et al. 2010). Despite their obvious industrial potential, the application is often hindered by their low abundance in natural plant sources. This poses challenges in a biosustainable production of such compounds due to wasteful and costly product purification. Here, we present a novel biotechnological process for the production of betulinic acid using tailored Saccharomyces cerevisiae strains. The multi-scale optimization of this microbial process included: - pathway engineering by determination of optimal gene combination and dosage, - compartment engineering to increase the reaction space of the betulinic acid pathway, and - strain engineering by implementation of different push, pull and block strategies. In parallel we developed the fermentation process and were able to boost the performance of the engineered yeast by optimization of medium composition, cultivation conditions, carbon source and mode of fermentation operation in lab scale bioreactors. Product purification was achieved by a one-step extraction with acetone. The final process was evaluated in terms of economic and ecological efficiency and rated to be competitive with existing plant extraction procedures with potential for further performance improvement. Please click Additional Files below to see the full abstract

Synthetically-primed adaptation of Pseudomonas putida to a non-native substrate D-xylose

To broaden the substrate scope of microbial cell factories towards renewable substrates, rational genetic interventions are often combined with adaptive laboratory evolution (ALE). However, comprehensive studies enabling a holistic understanding of adaptation processes primed by rational metabolic engineering remain scarce. The industrial workhorse Pseudomonas putida was engineered to utilize the non-native sugar D-xylose, but its assimilation into the bacterial biochemical network via the exogenous xylose isomerase pathway remained unresolved. Here, we elucidate the xylose metabolism and establish a foundation for further engineering followed by ALE. First, native glycolysis is derepressed by deleting the local transcriptional regulator gene hexR. We then enhance the pentose phosphate pathway by implanting exogenous transketolase and transaldolase into two lag-shortened strains and allow ALE to finetune the rewired metabolism. Subsequent multilevel analysis and reverse engineering provide detailed insights into the parallel paths of bacterial adaptation to the non-native carbon source, highlighting the enhanced expression of transaldolase and xylose isomerase along with derepressed glycolysis as key events during the process.We thank Dr. Adam Feist and Dr. Hyungyu Lim for valuable discussions on genomic data of engineered and evolved P. putida strains, and Dr. Ludmilla Aristilde for valuable discussion on flux analyses. This work was funded by Czech Science Foundation Project 22-12505 S and Grant Agency of Masaryk University GAMU Project MASH Junior 2022 (MUNI/J/0003/2021) granted to P.D. and Brno Ph.D. Talent granted to B.B. CIISB. This work was also supported by the project National Institute of Virology and Bacteriology (Programme EXCELES, ID Project No. LX22NPO5103), funded by the European Union - Next Generation EU. Instruct-CZ Centre of Instruct-ERIC EU consortium, funded by MEYS CR infrastructure project LM2023042, is gratefully acknowledged for the financial support of the measurements at the CEITEC Proteomics Core Facility.Peer reviewe

Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

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MEMOTE for standardized genome-scale metabolic model testing

Supplementary information is available for this paper at https://doi.org/10.1038/s41587-020-0446-yReconstructing metabolic reaction networks enables the development of testable hypotheses of an organisms metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Geneproteinreaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality.We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjánsdóttir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503).info:eu-repo/semantics/publishedVersio

Determination of growth-coupling strategies and their underlying principles

Metabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. To support the implementation of growth-coupling strain designs, we seek to identify biologically relevant, metabolic principles that enforce strong growth-coupling on the basis of reaction knockouts.We adapted an established bilevel programming framework to maximize the minimally guaranteed production rate at a fixed, medium growth rate. Using this revised formulation, we identified various GC intervention strategies for metabolites of the central carbon metabolism, which were examined for GC generating principles under diverse conditions. Curtailing the metabolism to render product formation an essential carbon drain was identified as one major strategy generating strong coupling of metabolic activity and target synthesis. Impeding the balancing of cofactors and protons in the absence of target production was the underlying principle of all other strategies and further increased the GC strength of the aforementioned strategies.Maximizing the minimally guaranteed production rate at a medium growth rate is an attractive principle for the identification of strain designs that couple growth to target metabolite production. Moreover, it allows for controlling the inevitable compromise between growth coupling strength and the retaining of microbial viability. With regard to the corresponding metabolic principles, generating a dependency between the supply of global metabolic cofactors and product synthesis appears to be advantageous in enforcing strong GC for any metabolite. Deriving such strategies manually, is a hard task, due to which we suggest incorporating computational metabolic network analyses in metabolic engineering projects seeking to determine GC strain designs

Current Metabolic Engineering Strategies for Photosynthetic Bioproduction in Cyanobacteria

Cyanobacteria are photosynthetic microorganisms capable of using solar energy to convert CO2 and H2O into O2 and energy-rich organic compounds, thus enabling sustainable production of a wide range of bio-products. More and more strains of cyanobacteria are identified that show great promise as cell platforms for the generation of bioproducts. However, strain development is still required to optimize their biosynthesis and increase titers for industrial applications. This review describes the most well-known, newest and most promising strains available to the community and gives an overview of current cyanobacterial biotechnology and the latest innovative strategies used for engineering cyanobacteria. We summarize advanced synthetic biology tools for modulating gene expression and their use in metabolic pathway engineering to increase the production of value-added compounds, such as terpenoids, fatty acids and sugars, to provide a go-to source for scientists starting research in cyanobacterial metabolic engineering