Dokument: Development of a transcriptional biosensor and reengineering of its ligand specificity using fluorescence-activated cell sorting
| Titel: | Development of a transcriptional biosensor and reengineering of its ligand specificity using fluorescence-activated cell sorting | |||||||
| Weiterer Titel: | Entwicklung eines transkriptionellen Biosensors und Reengineering seiner Ligandspezifität unter Verwendung fluoreszenzaktivierter Zellsortierung | |||||||
| URL für Lesezeichen: | https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=54404 | |||||||
| URN (NBN): | urn:nbn:de:hbz:061-20201012-100916-0 | |||||||
| Kollektion: | Dissertationen | |||||||
| Sprache: | Englisch | |||||||
| Dokumententyp: | Wissenschaftliche Abschlussarbeiten » Dissertation | |||||||
| Medientyp: | Text | |||||||
| Autor: | M. Sc. Flachbart, Lion [Autor] | |||||||
| Dateien: |
| |||||||
| Beitragende: | Prof. Dr. Bott, Michael [Gutachter] Prof. Dr. Gohlke, Holger [Gutachter] | |||||||
| Stichwörter: | Enzyme engineering; fluorescence-activated cell sorting; directed evolution | |||||||
| Dewey Dezimal-Klassifikation: | 500 Naturwissenschaften und Mathematik » 570 Biowissenschaften; Biologie | |||||||
| Beschreibungen: | Important chemical compounds of our daily life such as amino acids, antibiotics or vitamins are produced by microorganisms at large-scale. Also, there is growing interest in the microbial synthesis of many other compounds including pharmaceutically interesting secondary metabolites from plants. However, development and improvement of the microbial producer strains is time-consuming and cost-intensive. In this context, biosensor-based fluorescence-active cell sorting (FACS) to identify suitable production strain variants represents a promising approach to tackle these challenges.
In this dissertation, the application of transcription factor-based biosensors in combination with FACS for high-throughput screening of enzyme libraries was investigated in Escherichia coli. Furthermore, the construction of biosensors with modified ligand spectrum from an existing biosensor was pursued to expand the repertoire of biosensor-detectable substances. Initially, the transcription factor-based biosensor pSenCA which can be used to convert cytosolic concentrations of the phenylpropanoid trans-cinnamic acid (CA) to a fluorescence output signal, was constructed and characterized. The biosensor is composed of the transcriptional regulator HcaR from Escherichia coli and its target promoter PhcaE, transcriptionally fused with the eyfp gene encoding an autofluorescent protein. This biosensor was subsequently used to optimize an L phenylalanine/L tyrosine ammonia lyase from Trichosporon cutaneum (XalTc), by a directed evolution approach. Aromatic amino acid ammonia lyases represent the key enzyme in many plant polyphenol biosynthetic pathways. The use of an expression system with titratable expression strength of the ammonia lyase gene as well as a significant reduction of the initial cell density prior to screening were prerequisites for an effective isolation of CA producers from mixed cultures with non-producers. The established screening method was subsequently used to screen a randomly mutagenized ammonia lyase library of 2.4×106 variants for improved fluorescence. All 182 clones isolated by FACS were CA producers, 138 produced at least 10 % more CA compared to the parent strain. The best strain showed a 60 % increase in CA production. Seven XalTc variants investigated in vitro exhibited up to 12 % increased specific activity and up to 20 % increased substrate affinity. In the second project, 15 amino acids in the ligand binding site of the regulator protein HcaR, which were identified by in silico structure analysis, were randomized by site saturation mutagenesis. The resulting HcaR biosensor libraries were screened for variants with increased specificity for 3,5 dihydroxyphenylpropionate using FACS. These experiments resulted in the isolation of pSenGeneral, a sensor variant with a significantly broadened ligand spectrum. In a second round of biosensor evolution, additional libraries based on pSenGeneral were constructed and screened for variants with specificity for various compounds of biotechnological interest. As a result, biosensor variants for the detection of 4-hydroxybenzoic acid, 6-methylsalicylate, p-coumaric acid, or 5-bromoferulic acid could be isolated. In the future, these newly designed biosensors for small aromatic compounds could find an application during microbial strain development and might represent a good starting point for the development of additional biosensors for other aromatic molecules of biotechnological interest.Wichtige chemische Verbindungen unseres täglichen Lebens wie Aminosäuren, Antibiotika oder Vitamine werden von Mikroorganismen im großen Maßstab produziert. Darüber hinaus wächst das Interesse an der mikrobiellen Synthese zahlreicher weiterer Substanzen, einschließlich pharmazeutisch interessanter sekundärer Metaboliten aus Pflanzen. Die Entwicklung und Verbesserung der mikrobiellen Produzentenstämme ist jedoch zeitaufwendig und kostenintensiv. In diesem Zusammenhang stellt die biosensorbasierte, Fluoreszenz-aktivierte Zellsortierung (FACS) einen vielversprechenden Ansatz zur Bewältigung dieser Herausforderungen dar. In dieser Arbeit wurde die Anwendung von Transkriptionsfaktor-basierten Biosensoren in Kombination mit FACS für das Hochdurchsatzscreening von Enzymbibliotheken in Escherichia coli untersucht. Darüber hinaus wurde die Konstruktion von Biosensoren mit modifiziertem Ligandenspektrum, ausgehend von einem bestehenden Biosensor, verfolgt, um das Repertoire an Substanzen zu erweitern, die duch Biosensoren detektierbar sind Zunächst wurde der transkriptionelle Biosensor pSenCA, der zytosolische Konzentrationen des Phenylpropanoids trans-zimtsäure (CA) in ein Fluoreszenzsignal umwandelt, konstruiert und charakterisiert. Der Biosensor besteht aus dem Transkriptionsregulator HcaR aus Escherichia coli und dessen Zielpromotor PhcaE, transkriptionell fusioniert mit dem eyfp-Gen, das ein fluoreszierendes Reporterprotein kodiert. Im Anschluss wurde pSenCA genutzt, um eine L-Phenylalanin/L-Tyrosin-Ammoniak-Lyase aus Trichosporon cutaneum (XalTc), durch einen gerichteten Evolutionsansatz zu optimieren. L-Phenylalanin/L-Tyrosin-Ammoniak-Lyasen katalysieren einen Schlüsselschritt in der Biosynthese vieler pflanzlicher Polyphenole. Die Verwendung eines Expressionssystems mit titrierbarer Expressionsstärke des Ammoniak-Lyase-Gens sowie eine signifikante Reduktion der anfänglichen Zelldichte vor dem Screening waren Voraussetzung für eine effektive Isolierung von CA-Produzenten aus Mischkulturen mit Nicht Produzenten. Die etablierte Screeningmethode wurde anschließend verwendet, um eine ungerichtet mutagenisierte Ammoniak-Lyase-Bibliothek mit 2,4×106 Varianten auf erhöhte Fluoreszenz zu überprüfen. Alle 182 Klone, die mittels FACS isoliert wurden, waren CA-Produzenten, 138 produzierten signifikant (> 10 %) mehr CA als der Ausgangsstamm. Der beste Stamm zeigte einen Anstieg der CA Produktion um 60 %. Sieben in vitro untersuchte XalTc-Mutanten zeigten eine bis zu 12 % erhöhte Aktivität und eine bis zu 20 % erhöhte Substrataffinität. Im zweiten Projekt wurden 15 Aminosäuren in der Ligandenbindestelle des Regulatorproteins HcaR, die mittels in silico-Strukturanalyse identifiziert wurden, durch eine Sättigungsmutagenese zielgerichtet mutagenisiert. Die resultierenden HcaR-Biosensorbibliotheken wurden mittels FACS auf Varianten mit erhöhter Spezifität für 3,5 Dihydroxyphenylpropionat durchmustert. Diese Experimente führten zur Isolation von pSenGeneral, einer Sensorvariante mit einem deutlich vergrößerten Spektrum detektierbarer Liganden. In einer zweiten Runde der Biosensorevolution wurden zusätzliche Bibliotheken auf Basis von pSenGeneral erstellt und nach Sensoren mit Spezifität für verschiedene Verbindungen von biotechnologischem Interesse durchsucht. So konnten Biosensorvarianten für den Nachweis von 4-Hydroxybenzoesäure, 6-Methylsalicylat, p-Cumarsäure oder 5 Bromoferulasäure isoliert werden. Diese neu entwickelten Biosensoren für kleine Aromaten könnten in Zukunft eine Anwendung bei der mikrobiellen Stammentwicklung finden und auch einen guten Ausgangspunkt für die Entwicklung weiterer Biosensoren für andere aromatische Moleküle von biotechnologischem Interesse darstellen. | |||||||
| Quelle: | Aboul-ela, F., Huang, W., Abd Elrahman, M., Boyapati, V., Li, P., 2015. Linking aptamer-ligand binding and expression platform folding in riboswitches: prospects for mechanistic modeling and design. Wiley Interdiscip. Rev. RNA 6, 631–650. https://doi.org/10.1002/wrna.1300
Abreu-Goodger, C., Merino, E., 2005. RibEx: a web server for locating riboswitches and other conserved bacterial regulatory elements. Nucleic Acids Res. 33, W690-2. https://doi.org/10.1093/nar/gki445 Abreu, V.A.C., Almeida, S., Tiwari, S., Hassan, S.S., Mariano, D., Silva, A., Baumbach, J., Azevedo, V., Röttger, R., 2015. CMRegNet–An interspecies reference database for corynebacterial and mycobacterial regulatory networks. BMC Genomics 16, 452. https://doi.org/10.1186/s12864-015-1631-0 Ameen, S., Ahmad, M., Mohsin, M., Qureshi, M.I., Ibrahim, M.M., Abdin, M.Z., Ahmad, A., 2016. Designing, construction and characterization of genetically encoded FRET-based nanosensor for real time monitoring of lysine flux in living cells. J. Nanobiotechnology 14, 49. https://doi.org/10.1186/s12951-016-0204-y An, G.-H., Bielich, J., Auerbach, R., Johnson, E.A., 1991. Isolation and Characterization of Carotenoid Hyperproducing Mutants of Yeast by Flow Cytometry and Cell Sorting. Nat. Biotechnol. 9, 70–73. https://doi.org/10.1038/nbt0191-70 Antoine, R., Locht, C., 1992. Isolation and molecular characterization of a novel broad-host-range plasmid from Bordetella bronchiseptica with sequence similarities to plasmids from Gram-positive organisms. Mol. Microbiol. 6, 1785–1799. https://doi.org/10.1111/j.1365-2958.1992.tb01351.x Arnold, F.H., Georgiou, G., 2003. Directed Enzyme Evolution. Humana Press, New Jersey. https://doi.org/10.1385/1592593968 Aschenbrenner, J., Marx, P., Pietruszka, J., Marienhagen, J., 2018. Microbial production of natural and non‐natural monolignols with Escherichia coli. ChemBioChem cbic.201800673. https://doi.org/10.1002/cbic.201800673 Bengert, P., Dandekar, T., 2004. Riboswitch finder--a tool for identification of riboswitch RNAs. Nucleic Acids Res. 32, W154-9. https://doi.org/10.1093/nar/gkh352 Bilan, D.S., Pase, L., Joosen, L., Gorokhovatsky, A.Y., Ermakova, Y.G., Gadella, T.W.J., Grabher, C., Schultz, C., Lukyanov, S., Belousov, V. V, 2013. HyPer-3: a genetically encoded H(2)O(2) probe with improved performance for ratiometric and fluorescence lifetime imaging. ACS Chem. Biol. 8, 535–42. https://doi.org/10.1021/cb300625g Billinton, N., Barker, M.G., Michel, C.E., Knight, A.W., Heyer, W.D., Goddard, N.J., Fielden, P.R., Walmsley, R.M., 1998. Development of a green fluorescent protein reporter for a yeast genotoxicity biosensor. Biosens. Bioelectron. 13, 831–838. https://doi.org/10.1016/S0956-5663(98)00049-9 Binder, S., Schendzielorz, G., Stäbler, N., Krumbach, K., Hoffmann, K., Bott, M., Eggeling, L., 2012. A high-throughput approach to identify genomic variants of bacterial metabolite producers at the single-cell level. Genome Biol. 13, R40. https://doi.org/10.1186/gb-2012-13-5-r40 Bornscheuer, U.T., Hauer, B., Jaeger, K.E., Schwaneberg, U., 2019. Directed Evolution Empowered Redesign of Natural Proteins for the Sustainable Production of Chemicals and Pharmaceuticals. Angew. Chemie Int. Ed. 58, 36–40. https://doi.org/10.1002/anie.201812717 Bott, M., 2015. Need for speed - finding productive mutations using transcription factor-based biosensors, fluorescence-activated cell sorting and recombineering. Microb. Biotechnol. 8, 8–10. https://doi.org/10.1111/1751-7915.12248 Bulter, T., Alcalde, M., Sieber, V., Meinhold, P., Schlachtbauer, C., Arnold, F.H., 2003. Functional Expression of a Fungal Laccase in Saccharomyces cerevisiae by Directed Evolution. Appl. Environ. Microbiol. 69, 987–995. https://doi.org/10.1128/AEM.69.2.987-995.2003 Carroll, D., 2011. Genome Engineering With Zinc-Finger Nucleases. Genetics 188, 773–782. https://doi.org/10.1534/GENETICS.111.131433 Chan, C.T.Y., Lee, J.W., Cameron, D.E., Bashor, C.J., Collins, J.J., 2016. “Deadman” and “Passcode” microbial kill switches for bacterial containment. Nat. Chem. Biol. 12, 82–86. https://doi.org/10.1038/nchembio.1979 Chou, H.H., Keasling, J.D., 2013. Programming adaptive control to evolve increased metabolite production. Nat. Commun. 4, 2595. https://doi.org/10.1038/ncomms3595 Church, G.M., Elowitz, M.B., Smolke, C.D., Voigt, C.A., Weiss, R., 2014. Realizing the potential of synthetic biology. Nat. Rev. Mol. Cell Biol. 15, 289–294. https://doi.org/10.1038/nrm3767 Cirino, P.C., Mayer, K.M., Umeno, D., 2003. Generating mutant libraries using error-prone PCR, in: Directed Evolution Library Creation. Springer, pp. 3–9. Cossarizza, A., Chang, H.-D., Radbruch, A., Akdis, M., Andrä, I., Annunziato, F., Bacher, P., Barnaba, V., Battistini, L., Bauer, W.M., Baumgart, S., Becher, B., Beisker, W., Berek, C., Blanco, A., Borsellino, G., Boulais, P.E., Brinkman, R.R., Büscher, M., Busch, D.H., Bushnell, T.P., Cao, X., Cavani, A., Chattopadhyay, P.K., Cheng, Q., Chow, S., Clerici, M., Cooke, A., Cosma, A., Cosmi, L., Cumano, A., Dang, V.D., Davies, D., De Biasi, S., Del Zotto, G., Della Bella, S., Dellabona, P., Deniz, G., Dessing, M., Diefenbach, A., Di Santo, J., Dieli, F., Dolf, A., Donnenberg, V.S., Dörner, T., Ehrhardt, G.R.A., Endl, E., Engel, P., Engelhardt, B., Esser, C., Everts, B., Dreher, A., Falk, C.S., Fehniger, T.A., Filby, A., Fillatreau, S., Follo, M., Förster, I., Foster, J., Foulds, G.A., Frenette, P.S., Galbraith, D., Garbi, N., García-Godoy, M.D., Geginat, J., Ghoreschi, K., Gibellini, L., Goettlinger, C., Goodyear, C.S., Gori, A., Grogan, J., Gross, M., Grützkau, A., Grummitt, D., Hahn, J., Hammer, Q., Hauser, A.E., Haviland, D.L., Hedley, D., Herrera, G., Herrmann, M., Hiepe, F., Holland, T., Hombrink, P., Houston, J.P., Hoyer, B.F., Huang, B., Hunter, C.A., Iannone, A., Jäck, H.-M., Jávega, B., Jonjic, S., Juelke, K., Jung, S., Kaiser, T., Kalina, T., Keller, B., Khan, S., Kienhöfer, D., Kroneis, T., Kunkel, D., Kurts, C., Kvistborg, P., Lannigan, J., Lantz, O., Larbi, A., LeibundGut-Landmann, S., Leipold, M.D., Levings, M.K., Litwin, V., Liu, Y., Lohoff, M., Lombardi, G., Lopez, L., Lovett-Racke, A., Lubberts, E., Ludewig, B., Lugli, E., Maecker, H.T., Martrus, G., Matarese, G., Maueröder, C., McGrath, M., McInnes, I., Mei, H.E., Melchers, F., Melzer, S., Mielenz, D., Mills, K., Mirrer, D., Mjösberg, J., Moore, J., Moran, B., Moretta, A., Moretta, L., Mosmann, T.R., Müller, S., Müller, W., Münz, C., Multhoff, G., Munoz, L.E., Murphy, K.M., Nakayama, T., Nasi, M., Neudörfl, C., Nolan, J., Nourshargh, S., O’Connor, J.-E., Ouyang, W., Oxenius, A., Palankar, R., Panse, I., Peterson, P., Peth, C., Petriz, J., Philips, D., Pickl, W., Piconese, S., Pinti, M., Pockley, A.G., Podolska, M.J., Pucillo, C., Quataert, S.A., Radstake, T.R.D.J., Rajwa, B., Rebhahn, J.A., Recktenwald, D., Remmerswaal, E.B.M., Rezvani, K., Rico, L.G., Robinson, J.P., Romagnani, C., Rubartelli, A., Ruckert, B., Ruland, J., Sakaguchi, S., Sala-de-Oyanguren, F., Samstag, Y., Sanderson, S., Sawitzki, B., Scheffold, A., Schiemann, M., Schildberg, F., Schimisky, E., Schmid, S.A., Schmitt, S., Schober, K., Schüler, T., Schulz, A.R., Schumacher, T., Scotta, C., Shankey, T.V., Shemer, A., Simon, A.-K., Spidlen, J., Stall, A.M., Stark, R., Stehle, C., Stein, M., Steinmetz, T., Stockinger, H., Takahama, Y., Tarnok, A., Tian, Z., Toldi, G., Tornack, J., Traggiai, E., Trotter, J., Ulrich, H., van der Braber, M., van Lier, R.A.W., Veldhoen, M., Vento-Asturias, S., Vieira, P., Voehringer, D., Volk, H.-D., von Volkmann, K., Waisman, A., Walker, R., Ward, M.D., Warnatz, K., Warth, S., Watson, J. V., Watzl, C., Wegener, L., Wiedemann, A., Wienands, J., Willimsky, G., Wing, J., Wurst, P., Yu, L., Yue, A., Zhang, Q., Zhao, Y., Ziegler, S., Zimmermann, J., 2017. Guidelines for the use of flow cytometry and cell sorting in immunological studies. Eur. J. Immunol. 47, 1584–1797. https://doi.org/10.1002/eji.201646632 Crane, R.K., 1977. The gradient hypothesis and other models of carrier-mediated active transport, in: Reviews of Physiology, Biochemistry and Pharmacology, Volume 78. Springer-Verlag, Berlin/Heidelberg, pp. 99–159. https://doi.org/10.1007/BFb0027722 da Silva, T.L., Reis, A., Medeiros, R., Oliveira, A.C., Gouveia, L., 2009. Oil production towards biofuel from autotrophic microalgae semicontinuous cultivations monitorized by flow cytometry. Appl. Biochem. Biotechnol. 159, 568–78. https://doi.org/10.1007/s12010-008-8443-5 De Lorenzo, V., Fernandez, S., Herrero, M., Jakubzik, U., Timmis, K.N., 1993. Engineering of alkyl- and haloaromatic-responsive gene expression with mini-transposons containing regulated promoters of biodegradative pathways of Pseudomonas. Gene 130, 41–46. https://doi.org/10.1016/0378-1119(93)90344-3 Díaz, E., Ferrández, A., Prieto, M.A., García, J.L., 2001. Biodegradation of aromatic compounds by Escherichia coli. Microbiol. Mol. Biol. Rev. 65, 523–69. https://doi.org/10.1128/MMBR.65.4.523-569.2001 Dietrich, J.A., McKee, A.E., Keasling, J.D., 2010. High-throughput metabolic engineering: advances in small-molecule screening and selection. Annu. Rev. Biochem. 79, 563–90. https://doi.org/10.1146/annurev-biochem-062608-095938 Dietrich, J.A., Shis, D.L., Alikhani, A., Keasling, J.D., 2013. Transcription factor-based screens and synthetic selections for microbial small-molecule biosynthesis. ACS Synth. Biol. 2, 47–58. https://doi.org/10.1021/sb300091d Ellington, A.D., Szostak, J.W., 1990. In vitro selection of RNA molecules that bind specific ligands. Nature 346, 818–822. https://doi.org/10.1038/346818a0 Elowitz, M.B., Leibler, S., 2000. A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–8. https://doi.org/10.1038/35002125 Endy, D., 2005. Foundations for engineering biology. Nature 438, 449–453. https://doi.org/10.1038/nature04342 Eudes, A., Juminaga, D., Baidoo, E.E.K., Collins, F., Keasling, J.D., Loqué, D., 2013. Production of hydroxycinnamoyl anthranilates from glucose in Escherichia coli. Microb. Cell Fact. 12, 62. https://doi.org/10.1186/1475-2859-12-62 Fasan, R., Chen, M.M., Crook, N.C., Arnold, F.H., 2007. Engineered Alkane-Hydroxylating Cytochrome P450BM3 Exhibiting Nativelike Catalytic Properties. Angew. Chemie Int. Ed. 46, 8414–8418. https://doi.org/10.1002/anie.200702616 Feng, J., Jester, B.W., Tinberg, C.E., Mandell, D.J., Antunes, M.S., Chari, R., Morey, K.J., Rios, X., Medford, J.I., Church, G.M., Fields, S., Baker, D., 2015. A general strategy to construct small molecule biosensors in eukaryotes. Elife 4. https://doi.org/10.7554/eLife.10606 Fouchet, P., Jan, S., Courtois, J., Courtois, B., Frelat, G., Barbotin, J.., 1995. Quantitative single-cell detection of poly(Î2-hydroxybutyrate) accumulation in Rhizobium meliloti by flow cytometry. FEMS Microbiol. Lett. 126, 31–35. https://doi.org/https://doi.org/10.1002/anie.200702616 Fredens, J., Wang, K., de la Torre, D., Funke, L.F.H., Robertson, W.E., Christova, Y., Chia, T., Schmied, W.H., Dunkelmann, D.L., Beránek, V., Uttamapinant, C., Llamazares, A.G., Elliott, T.S., Chin, J.W., 2019. Total synthesis of Escherichia coli with a recoded genome. Nature 569, 514–518. https://doi.org/10.1038/s41586-019-1192-5 Furusawa, C., Horinouchi, T., Hirasawa, T., Shimizu, H., 2013. Systems metabolic engineering: the creation of microbial cell factories by rational metabolic design and evolution. Adv. Biochem. Eng. Biotechnol. 131, 1–23. https://doi.org/10.1007/10_2012_137 Gaj, T., Gersbach, C.A., Barbas, C.F., 2013. ZFN, TALEN, and CRISPR/Cas-based methods for genome engineering. Trends Biotechnol. 31, 397–405. https://doi.org/10.1016/J.TIBTECH.2013.04.004 Gardner, T.S., Cantor, C.R., Collins, J.J., 2000. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–42. https://doi.org/10.1038/35002131 Ghribi, D., Zouari, N., Jaoua, S., 2004. Improvement of bioinsecticides production through mutagenesis of Bacillus thuringiensis by u.v. and nitrous acid affecting metabolic pathways and/or delta-endotoxin synthesis. J. Appl. Microbiol. 97, 338–346. https://doi.org/10.1111/j.1365-2672.2004.02323.x Gibson, D.G., Benders, G.A., Andrews-Pfannkoch, C., Denisova, E.A., Baden-Tillson, H., Zaveri, J., Stockwell, T.B., Brownley, A., Thomas, D.W., Algire, M.A., Merryman, C., Young, L., Noskov, V.N., Glass, J.I., Venter, J.C., Hutchison, C.A., Smith, H.O., 2008. Complete chemical synthesis, assembly, and cloning of a Mycoplasma genitalium genome. Science 319, 1215–20. https://doi.org/10.1126/science.1151721 Gouveia, L., Marques, A.E., da Silva, T.L., Reis, A., 2009. Neochloris oleabundans UTEX #1185: a suitable renewable lipid source for biofuel production. J. Ind. Microbiol. Biotechnol. 36, 821–6. https://doi.org/10.1007/s10295-009-0559-2 Gutmann, M., Hoischen, C., Krämer, R., 1992. Carrier-mediated glutamate secretion by Corynebacterium glutamicum under biotin limitation. Biochim. Biophys. Acta - Biomembr. 1112, 115–123. https://doi.org/10.1016/0005-2736(92)90261-J Hall, B.G., 1981. Changes in the substrate specificities of an enzyme during directed evolution of new functions. Biochemistry 20, 4042–9. https://doi.org/10.1021/bi00517a015 Hamers, D., van Voorst Vader, L., Borst, J.W., Goedhart, J., 2014. Development of FRET biosensors for mammalian and plant systems. Protoplasma 251, 333–47. https://doi.org/10.1007/s00709-013-0590-z Harmsen, P.F.H., Hackmann, M.M., Bos, H.L., 2014. Green building blocks for bio-based plastics. Biofuels, Bioprod. Biorefining 8, 306–324. https://doi.org/10.1002/bbb.1468 Hessels, A.M., Merkx, M., 2015. Genetically-encoded FRET-based sensors for monitoring Zn 2+ in living cells. Metallomics 7, 258–266. https://doi.org/10.1039/C4MT00179F Hoppe, G.K., Hansford, G.S., 1984. The effect of micro-aerobic conditions on continuous ethanol production by Saccharomyces cerevisiae. Biotechnol. Lett. 6, 681–686. https://doi.org/10.1007/BF00133837 Hughes, S.R., Gibbons, W.R., Bang, S.S., Pinkelman, R., Bischoff, K.M., Slininger, P.J., Qureshi, N., Kurtzman, C.P., Liu, S., Saha, B.C., Jackson, J.S., Cotta, M.A., Rich, J.O., Javers, J.E., 2012. Random UV-C mutagenesis of Scheffersomyces (formerly Pichia) stipitis NRRL Y-7124 to improve anaerobic growth on lignocellulosic sugars. J. Ind. Microbiol. Biotechnol. 39, 163–173. https://doi.org/10.1007/s10295-011-1012-x Ikariyama, Y., Nishiguchi, S., Koyama, T., Kobatake, E., Aizawa, M., Tsuda, M., Nakazawa, T., 1997. Fiber-optic-based biomonitoring of benzene derivatives by recombinant E. coli bearing luciferase gene-fused TOL-plasmid immobilized on the fiber-optic end. Anal. Chem. 69, 2600–5. https://doi.org/10.1021/ac961311o Jahn, M., Vorpahl, C., Hübschmann, T., Harms, H., Müller, S., 2016. Copy number variability of expression plasmids determined by cell sorting and Droplet Digital PCR. Microb. Cell Fact. 15, 211. https://doi.org/10.1186/s12934-016-0610-8 Jakočiūnas, T., Pedersen, L.E., Lis, A. V., Jensen, M.K., Keasling, J.D., 2018. CasPER, a method for directed evolution in genomic contexts using mutagenesis and CRISPR/Cas9. Metab. Eng. 48, 288–296. https://doi.org/10.1016/j.ymben.2018.07.001 Jang, S., Yang, J., Seo, S.W., Jung, G.Y., 2015. Riboselector: riboswitch-based synthetic selection device to expedite evolution of metabolite-producing microorganisms. Methods Enzymol. 550, 341–62. https://doi.org/10.1016/bs.mie.2014.10.039 Jendresen, C.B., Stahlhut, S.G., Li, M., Gaspar, P., Siedler, S., Förster, J., Maury, J., Borodina, I., Nielsen, A.T., 2015. Novel highly active and specific tyrosine ammonia-lyases from diverse origins enable enhanced production of aromatic compounds in bacteria and yeast. Appl. Environ. Microbiol. AEM.00405-15. https://doi.org/10.1128/AEM.00405-15 Jinek, M., Chylinski, K., Fonfara, I., Hauer, M., Doudna, J.A., Charpentier, E., 2012. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–21. https://doi.org/10.1126/science.1225829 Kallscheuer, N., Menezes, R., Foito, A., da Silva, M.H., Braga, A., Dekker, W., Sevillano, D.M., Rosado-Ramos, R., Jardim, C., Oliveira, J., Ferreira, P., Rocha, I., Silva, A.R., Sousa, M., Allwood, J.W., Bott, M., Faria, N., Stewart, D., Ottens, M., Naesby, M., Nunes Dos Santos, C., Marienhagen, J., 2019. Identification and Microbial Production of the Raspberry Phenol Salidroside that Is Active against Huntington’s Disease. Plant Physiol. 179, 969–985. https://doi.org/10.1104/pp.18.01074 Kallscheuer, N., Vogt, M., Stenzel, A., Gätgens, J., Bott, M., Marienhagen, J., 2016. Construction of a Corynebacterium glutamicum platform strain for the production of stilbenes and (2S)-flavanones. Metab. Eng. 38, 47–55. https://doi.org/10.1016/j.ymben.2016.06.003 Kang, Z., Zhang, C., Zhang, J.J., Jin, P., Zhang, J.J., Du, G., Chen, J., 2014. Small RNA regulators in bacteria: powerful tools for metabolic engineering and synthetic biology. Appl. Microbiol. Biotechnol. 98, 3413–24. https://doi.org/10.1007/s00253-014-5569-y Kaplan, N.L., Hudson, R.R., Langley, C.H., 1989. The “hitchhiking effect” revisited. Genetics 123, 887–99. Kaschubowski, K.E., Kraft, A.E., Nikolaev, V.O., Haag, F., 2020. Using FRET-Based Fluorescent Sensors to Monitor Cytosolic and Membrane-Proximal Extracellular ATP Levels, in: Pelegrín, P. (Ed.), Purinergic Signaling: Methods and Protocols. Springer New York, New York, NY, pp. 223–231. https://doi.org/10.1007/978-1-4939-9717-6_16 Keseler, I.M., Mackie, A., Peralta-Gil, M., Santos-Zavaleta, A., Gama-Castro, S., Bonavides-Martínez, C., Fulcher, C., Huerta, A.M., Kothari, A., Krummenacker, M., Latendresse, M., Muñiz-Rascado, L., Ong, Q., Paley, S., Schröder, I., Shearer, A.G., Subhraveti, P., Travers, M., Weerasinghe, D., Weiss, V., Collado-Vides, J., Gunsalus, R.P., Paulsen, I., Karp, P.D., 2013. EcoCyc: fusing model organism databases with systems biology. Nucleic Acids Res. 41, D605–D612. https://doi.org/10.1093/nar/gks1027 Khlebnikov, A., Datsenko, K.A., Skaug, T., Wanner, B.L., Keasling, J.D., 2001. Homogeneous expression of the P(BAD) promoter in Escherichia coli by constitutive expression of the low-affinity high-capacity AraE transporter. Microbiology 147, 3241–7. https://doi.org/10.1099/00221287-147-12-3241 Khlebnikov, A., Risa, O., Skaug, T., Carrier, T.A., Keasling, J.D., 2000. Regulatable arabinose-inducible gene expression system with consistent control in all cells of a culture. J. Bacteriol. 182, 7029–34. https://doi.org/10.1128/JB.182.24.7029-7034.2000.Updated Kiviet, D.J., Nghe, P., Walker, N., Boulineau, S., Sunderlikova, V., Tans, S.J., 2014. Stochasticity of metabolism and growth at the single-cell level. Nature 514, 376–9. https://doi.org/10.1038/nature13582 Knight, T., 2003. Idempotent vector design for standard assembly of biobricks. Koopman, F., Beekwilder, J., Crimi, B., van Houwelingen, A., Hall, R.D., Bosch, D., van Maris, A.J., Pronk, J.T., Daran, J.-M., 2012. De novo production of the flavonoid naringenin in engineered Saccharomyces cerevisiae. Microb. Cell Fact. 11, 155. https://doi.org/10.1186/1475-2859-11-155 Kopniczky, M.B., Moore, S.J., Freemont, P.S., 2015. Multilevel Regulation and Translational Switches in Synthetic Biology. IEEE Trans. Biomed. Circuits Syst. 9, 485–96. https://doi.org/10.1109/TBCAS.2015.2451707 Korkina, L., Kostyuk, V., De Luca, C., Pastore, S., 2011. Plant Phenylpropanoids as Emerging Anti-Inflammatory Agents. Mini-Reviews Med. Chem. 11, 823–835. https://doi.org/10.2174/138955711796575489 Kucherov, F.A., Romashov, L. V, Galkin, K.I., Ananikov, V.P., 2018. Chemical Transformations of Biomass-Derived C6-Furanic Platform Chemicals for Sustainable Energy Research, Materials Science, and Synthetic Building Blocks. ACS Sustain. Chem. Eng. 6, 8064–8092. https://doi.org/10.1021/acssuschemeng.8b00971 Lee, S.-W., Oh, M.-K., 2015. A synthetic suicide riboswitch for the high-throughput screening of metabolite production in Saccharomyces cerevisiae. Metab. Eng. 28, 143–150. https://doi.org/10.1016/j.ymben.2015.01.004 Lee, S.Y., Kim, H.U., Park, J.H., Park, J.M., Kim, T.Y., 2009. Metabolic engineering of microorganisms: general strategies and drug production. Drug Discov. Today 14, 78–88. https://doi.org/10.1016/J.DRUDIS.2008.08.004 Leonard, E., Yan, Y., Fowler, Z.L., Li, Z., Lim, C.-G., Lim, K.-H., Koffas, M.A.G., 2008. Strain Improvement of Recombinant Escherichia coli for Efficient Production of Plant Flavonoids. Mol. Pharm. 5, 257–265. https://doi.org/10.1021/mp7001472 Liang, J.C., Bloom, R.J., Smolke, C.D., 2011. Engineering biological systems with synthetic RNA molecules. Mol. Cell 43, 915–26. https://doi.org/10.1016/j.molcel.2011.08.023 Lin, J.-L., Wagner, J.M., Alper, H.S., 2017. Enabling tools for high-throughput detection of metabolites: Metabolic engineering and directed evolution applications. Biotechnol. Adv. 0–1. https://doi.org/10.1016/j.biotechadv.2017.07.005 Lin, Y., Yan, Y., 2012. Biosynthesis of caffeic acid in Escherichia coli using its endogenous hydroxylase complex. Microb. Cell Fact. 11, 42. https://doi.org/10.1186/1475-2859-11-42 Liu, Ya’nan, Li, Q., Zheng, P., Zhang, Z., Liu, Yongfei, Sun, C., Cao, G., Zhou, W., Wang, X., Zhang, D., Zhang, T., Sun, J., Ma, Y., 2015. Developing a high-throughput screening method for threonine overproduction based on an artificial promoter. Microb. Cell Fact. 14, 121. https://doi.org/10.1186/s12934-015-0311-8 Lutz, S., Iamurri, S.M., 2018. Protein Engineering: Past, Present, and Future, in: Protein Engineering. Springer, pp. 1–12. https://doi.org/10.1007/978-1-4939-7366-8_1 Mahr, R., Frunzke, J., 2015. Transcription factor-based biosensors in biotechnology: current state and future prospects. Appl. Microbiol. Biotechnol. https://doi.org/10.1007/s00253-015-7090-3 Mahr, R., Gätgens, C., Gätgens, J., Polen, T., Kalinowski, J., Frunzke, J., 2015. Biosensor-driven adaptive laboratory evolution of l-valine production in Corynebacterium glutamicum. Metab. Eng. 32, 184–94. https://doi.org/10.1016/j.ymben.2015.09.017 Mahr, R., von Boeselager, R.F., Wiechert, J., Frunzke, J., 2016. Screening of an Escherichia coli promoter library for a phenylalanine biosensor. Appl. Microbiol. Biotechnol. https://doi.org/10.1007/s00253-016-7575-8 Martin, V.J.J., Pitera, D.J., Withers, S.T., Newman, J.D., Keasling, J.D., 2003. Engineering a mevalonate pathway in Escherichia coli for production of terpenoids. Nat. Biotechnol. 21, 796–802. https://doi.org/10.1038/nbt833 McVey, M., Lee, S.E., 2008. MMEJ repair of double-strand breaks (director’s cut): deleted sequences and alternative endings. Trends Genet. 24, 529–38. https://doi.org/10.1016/j.tig.2008.08.007 Michener, J.K., Smolke, C.D., 2012. High-throughput enzyme evolution in Saccharomyces cerevisiae using a synthetic RNA switch. Metab. Eng. 14, 306–316. https://doi.org/10.1016/j.ymben.2012.04.004 Milke, L., Aschenbrenner, J., Marienhagen, J., Kallscheuer, N., 2018. Production of plant-derived polyphenols in microorganisms: current state and perspectives. Appl. Microbiol. Biotechnol. 102, 1575–1585. https://doi.org/10.1007/s00253-018-8747-5 Milke, L., Ferreira, P., Kallscheuer, N., Braga, A., Vogt, M., Kappelmann, J., Oliveira, J., Silva, A.R., Rocha, I., Bott, M., Noack, S., Faria, N., Marienhagen, J., 2019a. Modulation of the central carbon metabolism of Corynebacterium glutamicum improves malonyl-CoA availability and increases plant polyphenol synthesis. Biotechnol. Bioeng. 116, 1380–1391. https://doi.org/10.1002/bit.26939 Milke, L., Kallscheuer, N., Kappelmann, J., Marienhagen, J., 2019b. Tailoring Corynebacterium glutamicum towards increased malonyl-CoA availability for efficient synthesis of the plant pentaketide noreugenin. Microb. Cell Fact. 18, 71. https://doi.org/10.1186/s12934-019-1117-x Miller, J.C., Tan, S., Qiao, G., Barlow, K.A., Wang, J., Xia, D.F., Meng, X., Paschon, D.E., Leung, E., Hinkley, S.J., Dulay, G.P., Hua, K.L., Ankoudinova, I., Cost, G.J., Urnov, F.D., Zhang, H.S., Holmes, M.C., Zhang, L., Gregory, P.D., Rebar, E.J., 2011. A TALE nuclease architecture for efficient genome editing. Nat. Biotechnol. 29, 143–148. https://doi.org/10.1038/nbt.1755 Möglich, A., Hegemann, P., 2013. Programming genomes with light. Nature 500, 406–408. https://doi.org/10.1038/500406a Mohsin, M., Abdin, M.Z., Nischal, L., Kardam, H., Ahmad, A., 2013. Genetically encoded FRET-based nanosensor for in vivo measurement of leucine. Biosens. Bioelectron. 50, 72–7. https://doi.org/10.1016/j.bios.2013.06.028 Mohsin, M., Ahmad, A., 2014. Genetically-encoded nanosensor for quantitative monitoring of methionine in bacterial and yeast cells. Biosens. Bioelectron. 59, 358–64. https://doi.org/10.1016/j.bios.2014.03.066 Munch, R., Hiller, K., Barg, H., Heldt, D., Linz, S., Wingender, E., Jahn, D., 2003. PRODORIC: prokaryotic database of gene regulation. Nucleic Acids Res. 31, 266–269. https://doi.org/10.1093/nar/gkg037 Mustafi, N., Grünberger, A., Kohlheyer, D., Bott, M., Frunzke, J., 2012. The development and application of a single-cell biosensor for the detection of l-methionine and branched-chain amino acids. Metab. Eng. 14, 449–57. https://doi.org/10.1016/j.ymben.2012.02.002 Mustafi, N., Grünberger, A., Mahr, R., Helfrich, S., Nöh, K., Blombach, B., Kohlheyer, D., Frunzke, J., 2014. Application of a genetically encoded biosensor for live cell imaging of L-valine production in pyruvate dehydrogenase complex-deficient Corynebacterium glutamicum strains. PLoS One 9, e85731. https://doi.org/10.1371/journal.pone.0085731 Nadler, D.C., Morgan, S.-A., Flamholz, A., Kortright, K.E., Savage, D.F., 2016. Rapid construction of metabolite biosensors using domain-insertion profiling. Nat. Commun. 7, 12266. https://doi.org/10.1038/ncomms12266 Nahvi, A., Sudarsan, N., Ebert, M.S., Zou, X., Brown, K.L., Breaker, R.R., 2002. Genetic Control by a Metabolite Binding mRNA. Chem. Biol. 9, 1043–1049. https://doi.org/10.1016/S1074-5521(02)00224-7 Neylon, C., 2004. Chemical and biochemical strategies for the randomization of protein encoding DNA sequences: library construction methods for directed evolution. Nucleic Acids Res. 32, 1448–59. https://doi.org/10.1093/nar/gkh315 Niedenführ, S., Wiechert, W., Nöh, K., 2015. How to measure metabolic fluxes: a taxonomic guide for 13C fluxomics. Curr. Opin. Biotechnol. 34, 82–90. https://doi.org/10.1016/J.COPBIO.2014.12.003 Nielsen, J., 2001. Metabolic engineering. Appl. Microbiol. Biotechnol. 55, 263–283. https://doi.org/10.1007/s002530000511 Nonomura, A.M., Coder, D.M., 1988. Improved phycocatalysis of carotene production by flow cytometry and cell sorting. Biocatalysis 1, 333–338. Novichkov, P.S., Kazakov, A.E., Ravcheev, D.A., Leyn, S.A., Kovaleva, G.Y., Sutormin, R.A., Kazanov, M.D., Riehl, W., Arkin, A.P., Dubchak, I., Rodionov, D.A., 2013. RegPrecise 3.0 – A resource for genome-scale exploration of transcriptional regulation in bacteria. BMC Genomics 14, 745. https://doi.org/10.1186/1471-2164-14-745 Pasteur, L., 1857. Mémoire sur la fermentation appelée lactique (Extrait par l’auteur). Comptes rendus des seances l’Academie des Sci. 45, 913–916. Petzold, C.J., Chan, L.J.G., Nhan, M., Adams, P.D., 2015. Analytics for Metabolic Engineering. Front. Bioeng. Biotechnol. 3, 135. https://doi.org/10.3389/fbioe.2015.00135 Pfleger, B.F., Pitera, D.J., Newman, J.D., Martin, V.J.J., Keasling, J.D., 2007. Microbial sensors for small molecules: development of a mevalonate biosensor. Metab. Eng. 9, 30–8. https://doi.org/10.1016/j.ymben.2006.08.002 Pitera, D.J., Paddon, C.J., Newman, J.D., Keasling, J.D., 2007. Balancing a heterologous mevalonate pathway for improved isoprenoid production in Escherichia coli. Metab. Eng. 9, 193–207. https://doi.org/10.1016/j.ymben.2006.11.002 Potzkei, J., Kunze, M., Drepper, T., Gensch, T., Jaeger, K.-E.E., Buechs, J., Büchs, J., 2012. Real-time determination of intracellular oxygen in bacteria using a genetically encoded FRET-based biosensor. BMC Biol. 10, 28. https://doi.org/10.1186/1741-7007-10-28 Reisch, C.R., Prather, K.L.J., 2015. The no-SCAR (Scarless Cas9 Assisted Recombineering) system for genome editing in Escherichia coli. Sci. Rep. 5, 15096. https://doi.org/10.1038/srep15096 Rogers, J.K., Church, G.M., 2016. Genetically encoded sensors enable real-time observation of metabolite production. Proc. Natl. Acad. Sci. U. S. A. 113, 2388–93. https://doi.org/10.1073/pnas.1600375113 Ronda, C., Pedersen, L.E., Sommer, M.O.A., Nielsen, A.T., 2016. CRMAGE: CRISPR Optimized MAGE Recombineering. Sci. Rep. 6, 19452. https://doi.org/10.1038/srep19452 Rovner, A.J., Haimovich, A.D., Katz, S.R., Li, Z., Grome, M.W., Gassaway, B.M., Amiram, M., Patel, J.R., Gallagher, R.R., Rinehart, J., Isaacs, F.J., 2015. Recoded organisms engineered to depend on synthetic amino acids. Nature 518, 89–93. https://doi.org/10.1038/nature14095 Roy, A., Kucukural, A., Zhang, Y., 2010. I-TASSER: a unified platform for automated protein structure and function prediction. Nat. Protoc. 5, 725–38. https://doi.org/10.1038/nprot.2010.5 Salgado, H., Gama-Castro, S., Peralta-Gil, M., Díaz-Peredo, E., Sánchez-Solano, F., Santos-Zavaleta, A., Martínez-Flores, I., Jiménez-Jacinto, V., Bonavides-Martínez, C., Segura-Salazar, J., Martínez-Antonio, A., Collado-Vides, J., 2006. RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions. Nucleic Acids Res. 34, D394–D397. https://doi.org/10.1093/nar/gkj156 San Martín, A., Ceballo, S., Ruminot, I., Lerchundi, R., Frommer, W.B., Barros, L.F., 2013. A genetically encoded FRET lactate sensor and its use to detect the Warburg effect in single cancer cells. PLoS One 8, e57712. https://doi.org/10.1371/journal.pone.0057712 Santos, C.N.S., Koffas, M., Stephanopoulos, G., 2011. Optimization of a heterologous pathway for the production of flavonoids from glucose. Metab. Eng. 13, 392–400. https://doi.org/10.1016/J.YMBEN.2011.02.002 Sauer, U., 2001. Evolutionary Engineering of Industrially Important Microbial Phenotypes, in: Advances in Biochemical Engineering/Biotechnology. pp. 129–169. https://doi.org/10.1007/3-540-45300-8_7 Scarlat, N., Dallemand, J.F., Monforti-Ferrario, F., Nita, V., 2015. The role of biomass and bioenergy in a future bioeconomy: Policies and facts. Environ. Dev. 15, 3–34. https://doi.org/10.1016/j.envdev.2015.03.006 Schallmey, M., Frunzke, J., Eggeling, L., Marienhagen, J., 2014. Looking for the pick of the bunch: high-throughput screening of producing microorganisms with biosensors. Curr. Opin. Biotechnol. 26, 148–54. https://doi.org/10.1016/j.copbio.2014.01.005 Schendzielorz, G., Dippong, M., Grünberger, A., Kohlheyer, D., Yoshida, A., Binder, S., Nishiyama, C., Nishiyama, M., Bott, M., Eggeling, L., 2014. Taking control over control: use of product sensing in single cells to remove flux control at key enzymes in biosynthesis pathways. ACS Synth. Biol. 3, 21–9. https://doi.org/10.1021/sb400059y Schürrle, K., 2018. History, Current State, and Emerging Applications of Industrial Biotechnology. Springer Fachmedien, Wiesbaden, pp. 1–39. https://doi.org/10.1007/10_2018_81 Serganov, A., Nudler, E., 2013. A decade of riboswitches. Cell 152, 17–24. https://doi.org/10.1016/j.cell.2012.12.024 Sibbesson, E., 2019. Reclaiming the Rotten: Understanding Food Fermentation in the Neolithic and Beyond. Environ. Archaeol. 1–12. https://doi.org/10.1080/14614103.2018.1563374 Siedler, S., Schendzielorz, G., Binder, S., Eggeling, L., Bringer, S., Bott, M., 2014. SoxR as a single-cell biosensor for NADPH-consuming enzymes in Escherichia coli. ACS Synth. Biol. 3, 41–7. https://doi.org/10.1021/sb400110j Snoek, T., Romero-Suarez, D., Zhang, J., Ambri, F., Skjoedt, M.L., Sudarsan, S., Jensen, M.K., Keasling, J.D., 2018. An Orthogonal and pH-Tunable Sensor-Selector for Muconic Acid Biosynthesis in Yeast. ACS Synth. Biol. 7, 995–1003. https://doi.org/10.1021/acssynbio.7b00439 Sowa, S.W., Gelderman, G., Contreras, L.M., 2015. Advances in synthetic dynamic circuits design: using novel synthetic parts to engineer new generations of gene oscillations. Curr. Opin. Biotechnol. 36, 161–167. https://doi.org/10.1016/J.COPBIO.2015.08.020 Sticher, P., Jaspers, M.C.M., Stemmler, K., Harms, H., Zehnder, A.J.B., Van Der Meer, J.R., 1997. Development and characterization of a whole-cell bioluminescent sensor for bioavailable middle-chain alkanes in contaminated groundwater samples. Appl. Environ. Microbiol. 63, 4053–4060. Turlin, E., Perrotte-piquemal, M., Danchin, A., Biville, F., 2001. Regulation of the early steps of 3-phenylpropionate catabolism in Escherichia coli. J. Mol. Microbiol. Biotechnol. 3, 127–33. Uchiyama, T., Miyazaki, K., 2010. Product-induced gene expression, a product-responsive reporter assay used to screen metagenomic libraries for enzyme-encoding genes. Appl. Environ. Microbiol. 76, 7029–35. https://doi.org/10.1128/AEM.00464-10 Ukibe, K., Katsuragi, T., Tani, Y., Takagi, H., 2008. Efficient screening for astaxanthin-overproducing mutants of the yeast Xanthophyllomyces dendrorhous by flow cytometry. FEMS Microbiol. Lett. 286, 241–248. Valli, M., Sauer, M., Branduardi, P., Borth, N., Porro, D., Mattanovich, D., 2006. Improvement of lactic acid production in Saccharomyces cerevisiae by cell sorting for high intracellular pH. Appl. Environ. Microbiol. 72, 5492–5499. Valli, M., Sauer, M., Branduardi, P., Borth, N., Porro, D., Mattanovich, D., 2005. Intracellular pH distribution in Saccharomyces cerevisiae cell populations, analyzed by flow cytometry. Appl. Environ. Microbiol. 71, 1515–1521. Vidal-Mas, J., Resina-Pelfort, O., Haba, E., Comas, J., Manresa, A., Vives-Rego, J., 2001. Rapid flow cytometry--Nile red assessment of PHA cellular content and heterogeneity in cultures of Pseudomonas aeruginosa 47T2 (NCIB 40044) grown in waste frying oil. Antonie Van Leeuwenhoek 80, 57–63. Wang, H.H., Isaacs, F.J., Carr, P.A., Sun, Z.Z., Xu, G., Forest, C.R., Church, G.M., 2009. Programming cells by multiplex genome engineering and accelerated evolution. Nature 460, 894–898. https://doi.org/10.1038/nature08187 Watts, N., Adger, W.N., Agnolucci, P., Blackstock, J., Byass, P., Cai, W., Chaytor, S., Colbourn, T., Collins, M., Cooper, A., Cox, P.M., Depledge, J., Drummond, P., Ekins, P., Galaz, V., Grace, D., Graham, H., Grubb, M., Haines, A., Hamilton, I., Hunter, A., Jiang, X., Li, M., Kelman, I., Liang, L., Lott, M., Lowe, R., Luo, Y., Mace, G., Maslin, M., Nilsson, M., Oreszczyn, T., Pye, S., Quinn, T., Svensdotter, M., Venevsky, S., Warner, K., Xu, B., Yang, J., Yin, Y., Yu, C., Zhang, Q., Gong, P., Montgomery, H., Costello, A., 2015. Health and climate change: policy responses to protect public health. Lancet 386, 1861–1914. https://doi.org/10.1016/S0140-6736(15)60854-6 Wieschalka, S., Blombach, B., Bott, M., Eikmanns, B.J., 2013. Bio-based production of organic acids with Corynebacterium glutamicum. Microb. Biotechnol. 6, 87–102. https://doi.org/10.1111/1751-7915.12013 Williamson, L.L., Borlee, B.R., Schloss, P.D., Guan, C., Allen, H.K., Handelsman, J., 2005. Intracellular screen to identify metagenomic clones that induce or inhibit a quorum-sensing biosensor. Appl. Environ. Microbiol. 71, 6335–44. https://doi.org/10.1128/AEM.71.10.6335-6344.2005 Wilson, D., Charoensawan, V., Kummerfeld, S.K., Teichmann, S.A., 2008. DBD––taxonomically broad transcription factor predictions: new content and functionality. Nucleic Acids Res. 36, D88–D92. https://doi.org/10.1093/nar/gkm964 Wink, M., 2018. Introduction: Biochemistry, Physiology and Ecological Functions of Secondary Metabolites, in: Annual Plant Reviews Online. John Wiley & Sons, Ltd, Chichester, UK, pp. 1–19. https://doi.org/10.1002/9781119312994.apr0423 Winkler, W.C., Cohen-Chalamish, S., Breaker, R.R., 2002a. An mRNA structure that controls gene expression by binding FMN. Proc. Natl. Acad. Sci. U. S. A. 99, 15908–13. https://doi.org/10.1073/pnas.212628899 Winkler, W.C., Nahvi, A., Breaker, R.R., 2002b. Thiamine derivatives bind messenger RNAs directly to regulate bacterial gene expression. Nature 419, 952–6. https://doi.org/10.1038/nature01145 Woolston, B.M., Edgar, S., Stephanopoulos, G., 2013. Metabolic engineering: past and future. Annu. Rev. Chem. Biomol. Eng. 4, 259–88. https://doi.org/10.1146/annurev-chembioeng-061312-103312 Xiao, Y., Bowen, C.H., Liu, D., Zhang, F., 2016. Exploiting nongenetic cell-to-cell variation for enhanced biosynthesis. Nat. Chem. Biol. 12, 339–44. https://doi.org/10.1038/nchembio.2046 Xu, P., Li, L., Zhang, F., Stephanopoulos, G., Koffas, M., 2014. Improving fatty acids production by engineering dynamic pathway regulation and metabolic control. Proc. Natl. Acad. Sci. U. S. A. 111, 11299–304. https://doi.org/10.1073/pnas.1406401111 Yang, J., Seo, S.W., Jang, S., Shin, S.-I., Lim, C.H., Roh, T.-Y., Jung, G.Y., 2013. Synthetic RNA devices to expedite the evolution of metabolite-producing microbes. Nat. Commun. 4, 1413. https://doi.org/10.1038/ncomms2404 Yang, J., Yan, R., Roy, A., Xu, D., Poisson, J., Zhang, Y., 2015. The I-TASSER Suite: protein structure and function prediction. Nat. Methods 12, 7–8. https://doi.org/10.1038/nmeth.3213 Yano, T., Oku, M., Akeyama, N., Itoyama, A., Yurimoto, H., Kuge, S., Fujiki, Y., Sakai, Y., 2010. A novel fluorescent sensor protein for visualization of redox states in the cytoplasm and in peroxisomes. Mol. Cell. Biol. 30, 3758–66. https://doi.org/10.1128/MCB.00121-10 Yeom, S.-J., Kim, M., Kwon, K.K., Fu, Y., Rha, E., Park, S.-H., Lee, H., Kim, H., Lee, D.-H., Kim, D.-M., Lee, S.-G., 2018. A synthetic microbial biosensor for high-throughput screening of lactam biocatalysts. Nat. Commun. 9, 5053. https://doi.org/10.1038/s41467-018-07488-0 Yonekura-Sakakibara, K., Saito, K., 2009. Functional genomics for plant natural product biosynthesis. Nat. Prod. Rep. 26, 1466. https://doi.org/10.1039/b817077k Zhang, J., Sonnenschein, N., Pihl, T.P.B., Pedersen, K.R., Jensen, M.K., Keasling, J.D., 2016. Engineering an NADPH/NADP+ Redox Biosensor in Yeast. ACS Synth. Biol. 5, 1546–1556. https://doi.org/10.1021/acssynbio.6b00135 Zhang, Y., 2008. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics 9, 40. https://doi.org/10.1186/1471-2105-9-40 Zhou, L.-B., Zeng, A.-P., 2015. Engineering a Lysine-ON Riboswitch for Metabolic Control of Lysine Production in Corynebacterium glutamicum. ACS Synth. Biol. 4, 1335–40. https://doi.org/10.1021/acssynbio.5b00075 Zhou, S., Liu, P., Chen, J., Du, G., Li, H., Zhou, J., 2016. Characterization of mutants of a tyrosine ammonia-lyase from Rhodotorula glutinis. Appl. Microbiol. Biotechnol. 100, 10443–10452. https://doi.org/10.1007/s00253-016-7672-8 Zhu, Q., Wang, L., Dong, Q., Chang, S., Wen, K., Jia, S., Chu, Z., Wang, H., Gao, P., Zhao, H., Han, S., Wang, Y., 2017. FRET-based glucose imaging identifies glucose signalling in response to biotic and abiotic stresses in rice roots. J. Plant Physiol. 215, 65–72. https://doi.org/10.1016/j.jplph.2017.05.007 | |||||||
| Rechtliche Vermerke: | Ich versichere an Eides Statt, dass die Dissertation von mir selbständig und ohne unzulässige fremde Hilfe unter Beachtung der „Grundsätze zur Sicherung guter wissenschaftlicher Praxis an der Heinrich-Heine-Universität Düsseldorf“ erstellt worden ist. Die Dissertation wurde in der vorgelegten oder einer ähnlichen Form noch bei keiner anderen Institution eingereicht. Ich habe bisher keine erfolglosen Promotionsversuche unternommen. | |||||||
| Lizenz: |  Urheberrechtsschutz | |||||||
| Bezug: | May 2015 until June 2019 | |||||||
| Fachbereich / Einrichtung: | Mathematisch- Naturwissenschaftliche Fakultät | |||||||
| Dokument erstellt am: | 12.10.2020 | |||||||
| Dateien geändert am: | 12.10.2020 | |||||||
| Promotionsantrag am: | 28.06.2019 | |||||||
| Datum der Promotion: | 14.10.2019 |
