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      Industrial brewing yeast engineered for the production of primary flavor determinants in hopped beer

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          Abstract

          Flowers of the hop plant provide both bitterness and “hoppy” flavor to beer. Hops are, however, both a water and energy intensive crop and vary considerably in essential oil content, making it challenging to achieve a consistent hoppy taste in beer. Here, we report that brewer’s yeast can be engineered to biosynthesize aromatic monoterpene molecules that impart hoppy flavor to beer by incorporating recombinant DNA derived from yeast, mint, and basil. Whereas metabolic engineering of biosynthetic pathways is commonly enlisted to maximize product titers, tuning expression of pathway enzymes to affect target production levels of multiple commercially important metabolites without major collateral metabolic changes represents a unique challenge. By applying state-of-the-art engineering techniques and a framework to guide iterative improvement, strains are generated with target performance characteristics. Beers produced using these strains are perceived as hoppier than traditionally hopped beers by a sensory panel in a double-blind tasting.

          Abstract

          Production of aromatic monoterpene molecules in hop flowers is affected by genetic, environmental, and processing factors. Here, the authors engineer brewer’s yeast for the production of linalool and geraniol, and show pilot-scale beer produced by engineered strains reconstitutes some qualities of hop flavor.

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          Most cited references29

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          Production of the antimalarial drug precursor artemisinic acid in engineered yeast.

          Malaria is a global health problem that threatens 300-500 million people and kills more than one million people annually. Disease control is hampered by the occurrence of multi-drug-resistant strains of the malaria parasite Plasmodium falciparum. Synthetic antimalarial drugs and malarial vaccines are currently being developed, but their efficacy against malaria awaits rigorous clinical testing. Artemisinin, a sesquiterpene lactone endoperoxide extracted from Artemisia annua L (family Asteraceae; commonly known as sweet wormwood), is highly effective against multi-drug-resistant Plasmodium spp., but is in short supply and unaffordable to most malaria sufferers. Although total synthesis of artemisinin is difficult and costly, the semi-synthesis of artemisinin or any derivative from microbially sourced artemisinic acid, its immediate precursor, could be a cost-effective, environmentally friendly, high-quality and reliable source of artemisinin. Here we report the engineering of Saccharomyces cerevisiae to produce high titres (up to 100 mg l(-1)) of artemisinic acid using an engineered mevalonate pathway, amorphadiene synthase, and a novel cytochrome P450 monooxygenase (CYP71AV1) from A. annua that performs a three-step oxidation of amorpha-4,11-diene to artemisinic acid. The synthesized artemisinic acid is transported out and retained on the outside of the engineered yeast, meaning that a simple and inexpensive purification process can be used to obtain the desired product. Although the engineered yeast is already capable of producing artemisinic acid at a significantly higher specific productivity than A. annua, yield optimization and industrial scale-up will be required to raise artemisinic acid production to a level high enough to reduce artemisinin combination therapies to significantly below their current prices.
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            mProphet: automated data processing and statistical validation for large-scale SRM experiments.

            Selected reaction monitoring (SRM) is a targeted mass spectrometric method that is increasingly used in proteomics for the detection and quantification of sets of preselected proteins at high sensitivity, reproducibility and accuracy. Currently, data from SRM measurements are mostly evaluated subjectively by manual inspection on the basis of ad hoc criteria, precluding the consistent analysis of different data sets and an objective assessment of their error rates. Here we present mProphet, a fully automated system that computes accurate error rates for the identification of targeted peptides in SRM data sets and maximizes specificity and sensitivity by combining relevant features in the data into a statistical model.
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              Improving industrial yeast strains: exploiting natural and artificial diversity

              Yeasts have been used for thousands of years to make fermented foods and beverages, such as beer, wine, sake, and bread. However, the choice for a particular yeast strain or species for a specific industrial application is often based on historical, rather than scientific grounds. Moreover, new biotechnological yeast applications, such as the production of second-generation biofuels, confront yeast with environments and challenges that differ from those encountered in traditional food fermentations. Together, this implies that there are interesting opportunities to isolate or generate yeast variants that perform better than the currently used strains. Here, we discuss the different strategies of strain selection and improvement available for both conventional and nonconventional yeasts. Exploiting the existing natural diversity and using techniques such as mutagenesis, protoplast fusion, breeding, genome shuffling and directed evolution to generate artificial diversity, or the use of genetic modification strategies to alter traits in a more targeted way, have led to the selection of superior industrial yeasts. Furthermore, recent technological advances allowed the development of high-throughput techniques, such as ‘global transcription machinery engineering’ (gTME), to induce genetic variation, providing a new source of yeast genetic diversity.
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                Author and article information

                Contributors
                charles@bbsbeer.com
                jdkeasling@lbl.gov
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                20 March 2018
                20 March 2018
                2018
                : 9
                : 965
                Affiliations
                [1 ]ISNI 0000 0001 2181 7878, GRID grid.47840.3f, California Institute of Quantitative Biosciences (QB3), , University of California, ; Berkeley, CA 94720 USA
                [2 ]ISNI 0000 0004 0407 8980, GRID grid.451372.6, Joint BioEnergy Institute, ; Emeryville, CA 94608 USA
                [3 ]ISNI 0000 0001 2181 7878, GRID grid.47840.3f, Department of Plant and Microbial Biology, , University of California, ; Berkeley, CA 94720 USA
                [4 ]Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA 94720 USA
                [5 ]ISNI 0000 0001 2181 7878, GRID grid.47840.3f, Department of Bioengineering, , University of California, ; Berkeley, CA 94720 USA
                [6 ]DOE Agile BioFoundry, Emeryville, CA 94608 USA
                [7 ]ISNI 0000 0004 1936 9684, GRID grid.27860.3b, Department of Food Science and Technology, , University of California Davis, ; Davis, CA 95616 USA
                [8 ]Lagunitas Brewing Company, Petaluma, CA 94954 USA
                [9 ]Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA 94720 USA
                [10 ]ISNI 0000 0001 2181 7878, GRID grid.47840.3f, Department of Chemical and Biomolecular Engineering, , University of California, ; Berkeley, CA 94720 USA
                [11 ]ISNI 0000 0001 2181 8870, GRID grid.5170.3, Novo Nordisk Foundation Center for Sustainability, , Technical University of Denmark, ; 2900 Hellerup, Denmark
                Author information
                http://orcid.org/0000-0002-8270-5228
                http://orcid.org/0000-0002-4556-9685
                http://orcid.org/0000-0003-4170-6088
                Article
                3293
                10.1038/s41467-018-03293-x
                5861129
                29559655
                0b6ae228-bf5e-4a6d-9eb2-fe1f152e96dd
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 August 2017
                : 2 February 2018
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