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  • 51. Zhou, Yongjin J.
    et al.
    Buijs, Nicolaas A.
    Zhu, Zhiwei
    Qin, Jiufu
    Siewers, Verena
    Nielsen, Jens
    KTH, Skolan för bioteknologi (BIO), Genteknologi.
    Production of fatty acid-derived oleochemicals and biofuels by synthetic yeast cell factories2016Inngår i: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 7, artikkel-id 11709Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Sustainable production of oleochemicals requires establishment of cell factory platform strains. The yeast Saccharomyces cerevisiae is an attractive cell factory as new strains can be rapidly implemented into existing infrastructures such as bioethanol production plants. Here we show high-level production of free fatty acids (FFAs) in a yeast cell factory, and the production of alkanes and fatty alcohols from its descendants. The engineered strain produces up to 10.4 gl(-1) of FFAs, which is the highest reported titre to date. Furthermore, through screening of specific pathway enzymes, endogenous alcohol dehydrogenases and aldehyde reductases, we reconstruct efficient pathways for conversion of fatty acids to alkanes (0.8 mgl(-1)) and fatty alcohols (1.5 gl(-1)), to our knowledge the highest titres reported in S. cerevisiae. This should facilitate the construction of yeast cell factories for production of fatty acids derived products and even aldehyde-derived chemicals of high value.

  • 52. Zhow, Yongjin J.
    et al.
    Buijs, Nicolaas A.
    Zhu, Zhiwei
    Gomez, Diego Orol
    Boonsombuti, Akarin
    Siewers, Verena
    Nielsen, Jens
    KTH, Skolan för bioteknologi (BIO), Genteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Sweden; Technical University of Denmark, Denmark.
    Harnessing Yeast Peroxisomes for Biosynthesis of Fatty-Acid-Derived Biofuels and Chemicals with Relieved Side-Pathway Competition2016Inngår i: Journal of the American Chemical Society, ISSN 0002-7863, E-ISSN 1520-5126, Vol. 138, nr 47, s. 15368-15377Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Establishing efficient synthetic pathways for microbial production of biochemicals is often hampered by competing pathways and/or insufficient precursor supply. Compartmentalization in cellular organelles can isolate synthetic pathways from competing pathways, and provide a compact and suitable environment for biosynthesis. Peroxisomes are cellular organelles where fatty acids are degraded, a process that is inhibited under typical fermentation conditions making them an interesting workhouse for production of fatty-acid-derived molecules. Here, we show that targeting synthetic pathways to peroxisomes can increase the production of fatty-acid-derived fatty alcohols, alkanes and olefins up to 700%. In addition, we demonstrate that biosynthesis of these chemicals in the peroxisomes results in significantly decreased accumulation of byproducts formed by competing enzymes. We further demonstrate that production can be enhanced up to 3-fold by increasing the peroxisome population. The strategies described here could be used for production of other chemicals, especially acyl-CoA-derived molecules.

  • 53. Zhu, Z.
    et al.
    Zhou, Y. J.
    Kang, M. -K
    Krivoruchko, A.
    Buijs, N. A.
    Nielsen, Jens
    KTH, Centra, Science for Life Laboratory, SciLifeLab. Chalmers University of Technology, Technical University of Denmark.
    Enabling the synthesis of medium chain alkanes and 1-alkenes in yeast2017Inngår i: Metabolic engineering, ISSN 1096-7176, E-ISSN 1096-7184, Vol. 44, s. 81-88Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Microbial synthesis of medium chain aliphatic hydrocarbons, attractive drop-in molecules to gasoline and jet fuels, is a promising way to reduce our reliance on petroleum-based fuels. In this study, we enabled the synthesis of straight chain hydrocarbons (C7–C13) by yeast Saccharomyces cerevisiae through engineering fatty acid synthases to control the chain length of fatty acids and introducing heterologous pathways for alkane or 1-alkene synthesis. We carried out enzyme engineering/screening of the fatty aldehyde deformylating oxygenase (ADO), and compartmentalization of the alkane biosynthesis pathway into peroxisomes to improve alkane production. The two-step synthesis of alkanes was found to be inefficient due to the formation of alcohols derived from aldehyde intermediates. Alternatively, the drain of aldehyde intermediates could be circumvented by introducing a one-step decarboxylation of fatty acids to 1-alkenes, which could be synthesized at a level of 3 mg/L, 25-fold higher than that of alkanes produced via aldehydes.

  • 54. Ågren, Rasmus
    et al.
    Mardinoglu, Adil
    Asplund, Anna
    Kampf, Caroline
    Uhlén, Mathias
    KTH, Skolan för bioteknologi (BIO), Proteomik och nanobioteknologi. KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Nielsen, Jens
    KTH, Centra, Science for Life Laboratory, SciLifeLab.
    Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling2014Inngår i: Molecular Systems Biology, ISSN 1744-4292, E-ISSN 1744-4292, Vol. 10, nr 3, s. A721-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Synopsis image Personalized GEMs for six hepatocellular carcinoma patients are reconstructed using proteomics data and a task-driven model reconstruction algorithm. These GEMs are used to predict antimetabolites preventing tumor growth in all patients or in individual patients. The presence of proteins encoded by 15,841 genes in tumors from 27 HCC patients is evaluated by immunohistochemistry. Personalized GEMs for six HCC patients and GEMs for 83 healthy cell types are reconstructed based on HMR 2.0 and the tINIT algorithm for task-driven model reconstruction. 101 antimetabolites are predicted to inhibit tumor growth in all patients. Antimetabolite toxicity is tested using the 83 cell type-specific GEMs. An l-carnitine analog inhibits the proliferation of HepG2 cells. Abstract Genome-scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype-phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task-driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type-specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty-two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line.

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