Delft University of Technology
Under pressure: evolutionary engineering of yeast strains for improved performance in
fuels and chemicals production
Mans, Robert; Daran, Jean Marc G.; Pronk, Jack T. DOI
10.1016/j.copbio.2017.10.011 Publication date
2018
Document Version Final published version Published in
Current Opinion in Biotechnology
Citation (APA)
Mans, R., Daran, J. M. G., & Pronk, J. T. (2018). Under pressure: evolutionary engineering of yeast strains for improved performance in fuels and chemicals production. Current Opinion in Biotechnology, 50, 47-56. https://doi.org/10.1016/j.copbio.2017.10.011
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Under
pressure:
evolutionary
engineering
of
yeast
strains
for
improved
performance
in
fuels
and
chemicals
production
Robert
Mans,
Jean-Marc
G
Daran
and
Jack
T
Pronk
Evolutionaryengineering,whichuseslaboratoryevolutionto selectforindustriallyrelevanttraits,isapopularstrategyinthe developmentofhigh-performingyeaststrainsforindustrial productionoffuelsandchemicals.Byintegrating
whole-genomesequencing,bioinformatics,classicalgeneticsand
genome-editingtechniques,evolutionaryengineeringhasalso
becomeapowerfulapproachforidentificationandreverse
engineeringofmolecularmechanismsthatunderlieindustrially relevanttraits.Newtechniquesenableaccelerationofinvivo mutationrates,bothacrossyeastgenomesandatspecificloci. Recentstudiesindicatethatphenotypictrade-offs,whichare oftenobservedafterevolutionunderconstantconditions,can bemitigatedbyusingdynamiccultivationregimes.Advancesin researchonsyntheticregulatorycircuitsofferexciting possibilitiestoextendtheapplicabilityofevolutionary engineeringtoproductsofyeastswhosesynthesisrequiresa netinputofcellularenergy.
Address
DepartmentofBiotechnology,DelftUniversityofTechnology,Vander Maasweg9,2629HZDelft,TheNetherlands
Correspondingauthor:Pronk,JackT(j.t.pronk@tudelft.nl)
CurrentOpinioninBiotechnology2018,50:47–56
ThisreviewcomesfromathemedissueonEnergybiotechnology EditedbyAkihikoKondoandHalAlper
https://doi.org/10.1016/j.copbio.2017.10.011
0958-1669/ã2017TheAuthors.PublishedbyElsevierLtd.Thisisan openaccessarticleundertheCCBY-NC-NDlicense( http://creative-commons.org/licenses/by-nc-nd/4.0/).
Introduction
Geneticallyengineeredyeaststrainsareincreasinglyused to produce commodity chemicals and biofuels such as succinicacid,isoprenoid-basedhydrocarbons,isobutanol and ethanol [1–3]. Strain improvement is essential to achieve the product yields and productivities required for economic viabilityof these processes. Especially in Saccharomycescerevisiae,CRISPR/Cas9-mediatedgenome editing and DNA-assembly methods enable fast and precise introduction of targeted genetic modifications [4,5].Evolutionary engineering, alsoknown as adaptive laboratoryevolution,isacomplementary strain-improve-ment strategy, which exploits plasticity of microbial
genomes bydesigningand applyingcultivationregimes thatconferaspecificselectiveadvantagetomutantswith an industriallyrelevanttrait[6,7].The selective advan-tageofsuchmutantsrelativetoothercellsinthe popula-tioncanbebasedonahigherspecificgrowthrate,lower deathrateand/orincreasedretentionintheculture(e.g. by sedimentation or biofilm formation, Table 1; [8,9]). Evolutionaryengineeringhasbeenintensivelyappliedto wild-type and engineered yeast strains, for example to improvestresstolerance,substrate-consumptionkinetics and catabolic product-formation rates (Table 1). This paperdiscussesrecentdevelopmentsandkeychallenges inevolutionaryengineeringforimprovedperformanceof yeastsintheindustrialproductionoffuelsandchemicals.
Evolutionary
engineering:
batch
and
continuous
cultivation
strategies
Serialtransferinsimpleshake flasksortubes(Figure1) remainsapowerfulapproachforyeastevolutionary engi-neering. Especially when each new cycle is inoculated fromanexponentiallygrowingculture,whilemaintaining a constant or continually increasing selective pressure, serialtransferselectsformutantswithahighermaximum specific growth rate (mmax). Serial-transfer experiments
haveyieldedyeaststrainswithimprovedstresstolerance (e.g. to high product and inhibitor concentrations,high temperature, low pH) and with improved rates of sub-strate consumption and/or catabolic product formation (Table 1). In fundamental research, automated serial transferenabledmassiveparallelyeastevolution experi-ments[10].Recentstudiesonevolutionaryengineeringof bacteria[11–13]underlinethepotentialofautomationfor intensifyingyeastevolutionaryengineering.
Sequentialbatchreactors(SBRs)combineautomationof repeated batchcultivation with accurate control of pro-cessparameters.Automatedempty–refill cyclescan, for example, be based on the actual CO2output, where a
decreaseoftheCO2concentrationintheoffgasindicates
nutrientdepletionandtriggerstheonsetofthenextcycle [8]. After emptying, a small remaining fraction of the culturethenactsasinoculumforthenextcycle(Figure1). Attentionistherefore requiredto preventaccumulation offast-sedimentingmutantsthatbypassselectionforfast growth[8].
Evolutionaryengineeringcanalsobeperformedin well-establishedsystemsforselectionoffast-growingmutants
Availableonlineatwww.sciencedirect.com
48
Energy
biotechnol
ogy
Table 1
Recent applications of various strategies for evolutionary engineering in Saccharomyces cerevisiae. Abbreviations: WGS, whole-genome sequencing; SNP, single-nucleotide polymorphism; HMF, hydroxymethylfurfural.
Target Strategy Evolution time Evolved phenotype Genotype analysis Proven causal mutations Reference Serial transfer in shake flasks
Faster glycerol utilization Serial transfer in synthetic medium with glycerol as sole carbon source
55 generations Growth on glycerol (0.12–0.13 h1)
3 Rounds of
backcrossing and WGS of independently evolved mutants
SNPs in GUT1 and UBR2 [38]
3-Hydroxypropionic acid (3-HP) tolerance
Serial transfer in complex medium supplemented with 3-HP at pH 3.5
Circa 200 generations Growth in YPD medium at pH 3.5 with 50 g L1 3-HP (0.18–0.20 h1) WGS of independently evolved mutants Mutations in SFA1 [35] High-temperature tolerance
Serial transfer in synthetic medium at 39.5C
326–375 generations 1.5–2-fold increased growth rate at 40C
WGS of independently evolved mutants
Early stop codon in ERG3 [43] Evolution of HXT
transporter into glucose-insensitive xylose transporter
Serial transfer of a strain unable to metabolize glucose on synthetic medium with xylose as carbon source in the presence of increasing concentrations of glucose 7 transfers Growth on 10 g L1 xylose in the presence of up to 50 g L1glucose Sequencing of GAL2, HXT5, HXT7 genes (independent evolution experiments). SNPs in GAL2, HXT5 and HXT7 [24]
Sequential batch reactor (SBR) cultivation
Full biotin prototrophy SBR cultivation on synthetic medium without biotin. Empty-refill cycles based on CO2concentration in off gas
11 transfers 32-fold increased growth rate in the absence of biotin (0.32 h1)
WGS 19-fold amplification of BIO1 gene and inactivation of TPO1
[16]
Fast biomass sedimentation
Anaerobic SBR cultivation with glucose or galactose as carbon source. Effluent pipe for empty-refill cycles above bottom of bioreactor. Empty-refill cycles based on CO2
concentration in off gas
Circa 500 and circa 900 generations Complete sedimentation after 5 min of static incubation WGS of independently evolved mutants Whole-genome duplication and mutations in ACE2 [8] Continuous cultivation
Full biotin prototrophy Accelerostat with feedback-controlled dilution rate based on CO2concentration in the
off gas on synthetic medium without biotin
48–77 days 25–36-fold increased growth rate in the absence of biotin (0.25–0.36 h1)
WGS of independently evolved mutants
8–42-fold amplification of BIO genes inactivation of PDR12 and TPO1 [16] Improved xylose fermentation in the presence of lignocellulosic inhibitors
Mutagenesis and anaerobic chemostat cultivation on non-detoxified straw hydrolysate with 20 g L1 xylose
100 generations 7.5-fold reduction of lag phase and complete removal of HMF, furfural and acetic acid in 24 h (0.12 h1)
Not performed Not performed [52]
Current Opinion in Biote chnology 2018, 50 :47–56 www.sci encedirect.com
such as pH-auxostats, turbidostats [14,15] and related continuous-cultivationset-ups(Figure1).Glucose-grown continuouscultures,whosedilutionrateswerefeed-back controlled based on on-line CO2-production
measure-ments, were recently used for selecting fast-growing biotin-prototrophic S. cerevisiae strains (Table 1) [16]. Inanotherexample,S.cerevisiaechemostatculturesgrown on asynthetic glucose–acetic acid–ammoniummedium without pHcontrol, in which ammonium consumption ledtoacidification,wereusedtoautomaticallymaximize selective pressure for tolerance to acetic acid, a key inhibitor of yeast performance in lignocellulosic hydro-lysates. After 400 generations of selective growth, the permissiveacetic-acidconcentrationofevolvingcultures had increased by three fold [17]. In industrial batch processes, low substrate affinity (mmax/Ks; [18]) causes
extended fermentation times. Nutrient-limited chemo-stat cultivation (Figure 1), which strongly selects for mutantswithanimprovedaffinityforthegrowth-limiting nutrient,hasbeenextensivelyappliedtoimproveaffinity of wild-type [19,20] and engineered yeasts for various carbonsources,includingD-xyloseandsucrose(Table1)
[21–23].
Selectivepressurecanbefocusedonaspecificenzymeor cellular process by genetic engineering. For instance, deletion of all four genes encoding glucose-phosphory-latingenzymesinpentose-fermentingS.cerevisiaestrains enabledin vivoevolutionof hexose-transportervariants thatefficientlytransportedD-xyloseandL-arabinose,two
keysugarsinlignocellulosichydrolysates,inthepresence of glucose(Table1)[24,25,26].
Identifying
causal
mutations:
genome
sequencing
and
classical
genetics
Availability of near-complete, high-quality and strain-specific reference genomes [27,28], accurate whole-genome resequencing technologies and bioinformatics platformshavetransformedevolutionaryengineeringinto apowerfulapproachforunderstandingthegeneticbasis ofcomplex,industriallyrelevanttraits[29,30].Moreover, CRISPR/Cas9-mediated, simultaneous introduction of multipledifferenttargetedmutations[31]strongly accel-erates functionalanalysisof theidentifiedmutationsvia theirintroductionintonon-evolvedstrains(Figure2). Inadditiontosingle-nucleotidemutations,insertionsand deletions,evolvedyeaststrainsfrequentlyharbour whole-chromosomeorsegmentalaneuploidies[32–34]. Particu-larly in thelatter cases, only few of the affectedgenes may contributeto the phenotypeof interest.Analysing multiple evolution experiments can help to identify causal mutations, especiallywhen they affect thesame genes orcellularprocessesinparallelexperiments (Fig-ure2)[29].Forexample,3-hydroxypropionate-tolerantS. cerevisiae strains, isolated from three independent repeated-batch cultures, carried different mutations in Evolutionaryengineeringofyeastforimprovedproduction 49
Table 1 (Continued ) Target Strategy Evolution time Evolved phenotype Genotype analysis Proven causal mutations Reference Improved ethanol tolerance Aerobic turbidostat cultivation on complex medium with gradually increasing concentrations of ethanol (6% up to 12%) 200 generations Up to 2.5-fold increased fitness in medium containing 9% ethanol and growth in the presence of 12% ethanol WGS of independently evolved mutants and cultures Increased ploidy and mutations in PRT1 , MAX67 and VPS70 [ 40 ] Dynamic selection regimes Constitutive tolerance to high concentrations of acetic acid Serial transfer in synthetic medium with and without acetic acid (alternating) in shake flasks 50–55 transfers Growth in synthetic medium containing > 12.5 g L 1 acetic acid at pH 4.5 WGS of independently evolved mutants and backcrossing Mutations in ASG1 , ADH3, SKS1 and GIS4 [ 37 ] Synthetic selection circuits Strains with improved aromatic amino acid (AAA) pathway flux. Mutagenesis and serial transfer of a strain with an AAA-biosensor (pARO9-KanNeo) in the presence of increasing levels of G418 and 4-fluorophenylalanine (4-FP) Circa 11 transfers Growth in the presence of 1 g L 1 G418 and 7.5 mM 4-FP and a 4–9-fold increased AAA pool. Not performed Not performed [ 77 ]
theS-(hydroxymethyl)-glutathionedehydrogenase gene SFA1.This observation enabled elucidationof a gluta-thione-dependent mechanism for 3-HP tolerance (Table1)[35].
Classical yeast genetics can accelerate identification of causal mutations (Figure 2). Back-crossing of evolved haploidstrainswithanon-evolvedstrainandsubsequent tracking ofmutated allelesinsegregants thatexpressed the selected trait, enabled identification of mutations contributing to acetic-acid tolerance, butanol tolerance andfastglycerolutilizationinevolvedS.cerevisiaestrains (Table1)[36,37,38].Whenevolvedgenotypesare com-plex,forexample,asaresultofprolongedgrowthunder conditionsthatincreasemutationfrequency,quantitative
traitloci(QTL)analysis[39]is apowerfulapproach, as illustrated by a genetic analysis of improved ethanol tolerance in S. cerevisiae strains obtained in a 2-year evolutionaryengineeringcampaign(Table1)[40]. End-point analyses of evolution experiments do not necessarilycaptureallbeneficialmutationsthatoccurred duringevolutionandanalysisofevolvingpopulationscan provide valuable additional information [41,42]. For instance, in a 450-generation experiment that selected for enhanced tolerance towards a mixture of inhibitors occurringinlignocellulosichydrolysates,strainsisolated atearlytimepointsshowedamarkedlyhighertolerance tosomeoftheindividualinhibitorsthanthefinalevolved population[41].
50 Energybiotechnology
Figure1
Current Opinion in Biotechnology Feedback loop
Serial shake flask (SF) cultivation
Sequential batch reactor (SBR)
Chemostat cultivation
Accelero-/turbido-/auxostat
Dynamic selection pressure (SF/SBR) µ (h-1) Batch (#) µ (h-1) Batch (#) Time → Cs (mM) Time → µ (h-1) µ (h-1) Batch (#) •Simple •Cheap
•Compatible with robotization
•Controlled cultivation
•Empty-refill cycles easily automated
•On-line analysis of e.g. CO2 production
•No empty-refill cycles
•Selection for substrate affinity
•Selection for mixed substrate utilization
•No empty-refill cycles
•On-line feedback to control dilution (growth) rate
•Selection for mixed substrate utilization
•Selection for constitutive improved phenotype
•Selection for mixed substrate utilization
Strategy Typical output Characteristics
Overviewofcultivationstrategiesusedforevolutionaryengineering.Redandgreencolorsreflectculturesgrownunderdifferentselective pressures(e.g.presenceandabsenceofaninhibitingcompoundorgrowthondifferentsubstrates).Themiddlecolumnillustratestypical developmentofspecificgrowthrate(m)orresidualnutrientconcentration(forchemostatcultivation,Cs)inevolvingyeastcultures.
Trade-offs
and
context
dependency:
benefits
of
dynamic
selection
regimes
Evolutionary engineering often reveals trade-offs betweenaselectedtraitandotheraspectsofyeast phys-iology.Trade-offshavebeenintensivelystudiedin evo-lutionaryengineeringofS.cerevisiaeforhigh-temperature tolerance,animportantcharacteristicforbioethanol pro-duction. After serial transfer at supra-optimal tempera-tures,erg3nullmutationswereshowntostrongly contrib-utetoimprovedgrowthat40Cbycausingreplacement ofergosterol,themajorsterolinwild-typemembranes,by fecosterol (Table 1) [43]. Initial characterization
demonstratedrespiratorydeficiencyoftheevolved ther-motolerantstrains[43],whilefurtheranalysisrevealeda reduced growth rate at 30C and increased glycerol production[44,45].
Selectiononsinglesubstratesfavoursmutantsthat pref-erentially allocate cellular resources to processes that directly contribute to growth on that substrate [46]. Such a preferential resource allocation can go at the expense of theexpression ofproteins involved inother pathways[47].Indeed,yeaststrainsevolvedforimproved growthoneitherD-xyloseorL-arabinoseoftenconsumed Evolutionaryengineeringofyeastforimprovedproduction 51
Figure2
Current Opinion in Biotechnology
Whole genome sequencing Whole genome sequencing Star ting population
Evol utionary engi nee ring in parallel independent cultures
Cultures with improved phenotype
Backcross ing, sequ enci ng and reverse engineering
Direc t sequ enci ng and reverse engineering
Testing of (reverse) engineered phenotype Unevolved cell
Mutants with improved phenotype Reverse engineered strain Mutations:
Contributing to evolved phenotype Not contributing to evolved phenotype
Identificationofcausalmutationsafterevolutionaryengineeringofyeaststrains.Inhaploidevolvedstrains,causalmutationscanfirstbeenriched byrepeatedbackcrossingofevolvedstrainswithanon-evolvedstrainoftheoppositematingtype(left).Alternatively,directgenomesequencing ofstrainsoriginatingfromparallelevolutionexperimentscanidentifymutationsthataffectthesamegenesorprocessesinindependentlyevolved strains(right).
glucose and/ortheother pentosesugaratreduced rates [48–51]. This trade-off was addressed by alternatingly growingan engineered,pentose-fermenting S. cerevisiae strain on different mixtures of glucose, xylose and/or arabinoseinSBRs. By balancingthenumberof genera-tionsofgrowthoneachofthethreesugars,thisdynamic selectionregime yieldedastrainthatrapidlyfermented sugarmixtures[49].Prolongednutrient-limitedgrowthat low specific growthrates hasbeenreportedto result in reduced performance when the nutrient limitation was relieved(Table 1)[20,52,53].Alsoin thiscase,dynamic selectionregimes,forexamplebasedonalternatingSBR and chemostat cultivation cycles, can prevent extreme trade-offs[22,54].
Yeast strainsevolved for stress tolerance do not always expresstheacquiredphenotypewhentheselective pres-sure is alleviated. Increased acetic-acid tolerance acquiredafterprolongedanaerobiccontinuouscultivation ofaxylose-fermentingS.cerevisiaestrainonacetic acid-containing medium, was not expressed in acetate-free medium[17].Suchaninducibletoleranceisnot compat-iblewithindustrialprocessesthatinvolveayeast propa-gationphaseonacetic-acid-freemediapriortoconversion ofaceticacid-containinglignocellulosichydrolysates[3]. A dynamic serial-transfer strategy (Figure 1), in which cultivationcyclesinacetic-acid-containingmediumwere alternated with cycles on acetic-acid-free medium, yielded strains with increased, constitutive acetic-acid tolerance[37].
Accelerating
evolution
of
yeast
cultures
Whilechemicalmutagens andradiationhavelong been used to increase mutation rates in microbes, genetic engineering offers new options to increase mutation frequencies in evolving cultures. Mutator yeast strains that increase mutation frequencies in a genome-wide manner can stimulate specific types of mutations. For example,S.cerevisiaemsh2Dstrainsexhibitedacirca 40-foldincreasedfrequencyof single-nucleotidemutations and indels, while a mec1D tel1D genotype specifically stimulated large structural variations and chromosomal aneuploidy [55]. When target sites of a heterologous recombinase are introduced at multiple genomic loci, expression of the associated recombinase causes dele-tions, inversions, duplications and more complex chro-mosomalrearrangements[56].TheSc2.0project,which designs and constructs synthetic S. cerevisiae genomes, exploits this feature by introducing hundreds of loxP sites. In the resulting strains, ‘genome scrambling’ induced by expression of the Cre recombinase can be usedtogenerategeneticdiversityinscreeningand evo-lutionaryengineeringexperiments[57–59].Toincreasemutation ratesataspecificlocus, theDNA glycosylase Mag1, which functions in base-excision repair, was fused to the Tet repressor, thus allowing
precise targeting of the glycosylase to the 19-bp tet operator (tetO) sequence. Indeed, integration of a 240-copytetO arrayefficientlyrecruited Mag1to thetarget locus. Mag1-mediated base excision and subsequent repairbytheerror-proneDNApolymerasez,causedover 800-foldhighermutationratesina20-kbregion surround-ingthe tetO array[60].In astudy on retrotransposon-mediated, targeted mutagenesis [61] heterologous DNA was integrated between Ty1RT and the 30-LTR of a galactose-inducible Ty1 retrotransposon in S. cerevisiae.Galactoseinductionresulted inmRNA forma-tion,error-pronereversetranscriptionandreintegrationof (mutated)cDNA,causingmutationratesofca.0.15kb1 perinductioncycleinthetargetsequenceandgenerating nearly 20 million distinct mutants per litre of culture. This method was successfully employed for in vivo mutagenesis of the S. cerevisiae global transcriptional regulator gene SPT15 in an evolutionary engineering study on improving 1-butanol tolerance [61]. A very preciselylocalizedincreaseinmutationratewasrecently achievedbyfusinganuclease-deficientCas9(dCas9)to anactivation-inducedcytidinedeaminase(AID). Expres-sion of this complex in the presence of a guide RNA resultedinanincreasedoccurrenceofCtoTmutationsin arangeof 3–5basesin thedCas9targetsite[62]. The incidence of copy-number variations of specific sequences can be enhanced by different techniques. When, during evolutionary engineering for fast growth on xylose, an expression cassette for a heterologous xyloseisomerase (XylA)was integratedcloseto anARS sequence, extrachromosomal circular DNA elements (eccDNA)carryingXylA wereformed.TheseeccDNAs facilitated multi-copy chromosomal integration of XylA, after which the unstably replicating eccDNA was lost [63].Integrationofrelevant genes in closeproximityto eccDNA-forming ARS sequences, which frequently occurintheS.cerevisiaegenome[64],offersaninteresting approachforevolutionary‘tuning’ofexpressionlevelsof relevantgenesinengineeredstrains.Copy-number vari-ation of relevantgenes can also be facilitated by their integrationbetweenrepetitive DNAsequences such as retrotransposons,as copy-numbervariationratesatsuch sites can be up to 5 orders of magnitude higher than elsewhere in the yeast genome [65,66]. Alternatively, tandem integration of multiple expression cassettes enables rapid copy-number expansion or compression byhomologous recombination[67].
A
holy
grail
in
evolutionary
engineering:
improving
anabolic
product
formation
Designofevolutionaryengineeringstrategiesthatenable selectionfortraitsthatdonotconferaselectiveadvantage inwild-typegeneticcontextsrepresents keyconceptual challenges. In particular,it would behighly interesting and relevant to harness evolutionary engineering for improving productivities and/or yields of ‘anabolic’ 52 Energybiotechnology
products, whosesynthesisrequires anetinputof meta-bolicenergy.
Someanabolicproductshavespecificpropertiesthatcan be used for designing selective growth regimes. For example,antioxidantpropertiesofcarotenoidshavebeen exploitedbyevolvingcarotenoid-producing,engineered S. cerevisiae under hydrogen-peroxide stress [68] and increased buoyancy has been elegantly used to select for lipid-hyperaccumulating Yarrowia lipolytica mutants [69].Otherstudieshavesoughttostoichometrically cou-ple anabolic product formation to essential metabolic processes by genetic engineering and, thereby, enable growth-based selectionregimes.Inanearlystudy, path-ways for mitochondrial oxidation of cytosolic NADHwereeliminatedinatriose-phosphate-isomerase negativeS.cerevisiaestrain,thusleavingglycerol produc-tion as sole mechanism for NADH reoxidation. Serial transferoftheresultingstrainatincreasingglucose con-centrationsenabledisolationofastrainthataccumulated over 200gL1 glycerol at a yield close to 1mol (molglucose)1[70].Tocoupleproductionof succinate to growth, Otero et al. constructed a S. cerevisiae ser3D ser33Dsdh3Dstrain,inwhichisocitratelyasewasessential for glycine and serine biosynthesis [71]. While subse-quent evolutionary engineering improved succinate yields, stoichiometric coupling of product formation andgrowthwasconfinedtoalimitedrangeof succinate yields.
Recentdevelopmentsinresearchonsyntheticregulatory circuitsmayenablethedevelopmentofgenerically appli-cable strategiesfor evolutionaryengineeringofanabolic productformation.Sensorproteinsthat,uponbindingofa compoundofinterest,activateexpressionofafluorescent proteinare alreadyintensivelyappliedfor fluorescence-activated cell sorting of high-producing mutants [72]. Newsensor systemscontinueto bedeveloped for rele-vantcompounds,asexemplifiedbytherecent construc-tionoftwodose-dependent1-butanolresponsive promo-ters and their application for quantifying 1-butanol production by engineered S. cerevisiae strains [73]. In principle,syntheticsensor/promotersystems,forexample basedonproduct-specificriboswitches[74],couldalsobe usedto tightlycoupleproductformationto anessential cellularprocess(Figure3)[75,76].Inapioneeringyeast evolutionary engineeringstudy, an aromatic amino-acid (AAA)-responsive hybrid promoter was used to control expression of the KanNeo gene, which confers weak resistance togeneticin.Serialtransferatincreasing con-centrations of geneticin and 4-fluorophenylalanine, an anti-metabolite ofaromaticaminoacid(AAA)synthesis, combined with random mutagenesis, yielded strains with a deregulated AAA pathway, which were used to improve precursor supply for muconic acid production (Table 1)[77].
While use of synthetic regulatory circuits holds great promiseforevolutionaryengineeringofanabolicproduct Evolutionaryengineeringofyeastforimprovedproduction 53
Figure3 Inactive selection gene (SG) Expressed selection gene (SG)
+
SG SG(Increasing) selective pressure Starting
population
Introduction of (engineered) product pathway
Evolution under selective pressure to select for (over)producing mutants
Non induced cell
(a)
(b)
SG SG SG SG
Mutants with increased product formation
Genetic mutation
Current Opinion in Biotechnology
Applicationofsyntheticregulatorycircuitsinyeastevolutionaryengineering,asschematicallyillustratedbytheuseofaproduct-responsive riboswitch.(a)Bindingofthetargetproducttotheriboswitchcausesdose-dependentexpression(indicatedbythedarkershadesofgreeninb)of aselectiongene(SG),whichencodesaproteinwhoseintracellularlevelcontrolsspecificgrowthrateunderselectiveconditions.(b)Inengineered yeaststhatco-expressthetargetproductpathwayandthesyntheticregulatorycircuit,spontaneousmutantsthatproducehigherlevelsofthe targetproduct(indicatedbythedarkershadesoforange)exhibitahigherspecificgrowthrateandthuscanbeselectedforinevolutionary engineeringexperiments.
formation, several design criteria remain to be further investigated.Topreventselectionofmutantsthatescape selectivepressurebybypassingor‘killing’theregulatory circuit, introduction of multiple, redundant regulatory circuitsislikelytoberequired.Forinstance,expression ofmultipleessentialgenesmaybecoupledtomultiple, independentproduct-sensor/promotercombinations[78] andthedynamic rangeoftheregulatory circuitsshould matchorbeeasilyadaptabletoindustriallyrelevant intra-cellularand/orextracellularconcentrationsofproductsor intermediatesofinterest.Designing,buildingandtesting suchstrategiesprovidesanindustriallyrelevantscientific challengeattheinterfaceofsyntheticbiology,microbial physiologyandevolutionbiology.
Conclusions
Rapiddevelopmentsinsequencing,analysisandediting of yeast genomes have transformed evolutionary engi-neering from a simple ‘black box’ strain-improvement strategy into an invaluableasset for understanding and rationallyengineeringyeastcellfactories.Recentstudies demonstratehowgeneticengineeringcanconfera selec-tiveadvantageto yeaststrainswithspecific, industrially relevant phenotypes. Integration of carefully designed (dynamic)cultivationregimes,basedoninsightin yeast physiologyandecology,withmethodsforacceleratingin vivo mutation rates at specific loci or across the yeast genomewill furtherincrease theimpact ofevolutionary engineering on yeast biotechnology. While still in its infancy, implementationof product-responsive, growth-coupled synthetic regulatory loops has the potential to addressingthelongstandingchallengeofharnessingthe powerofevolutionaryengineeringtoenhanceproduction of compounds whose synthesis by yeast cells does not conferadirectselectiveadvantage.Designing, building andtestingsuchcircuitswillinvolveexcitingresearchat theinterfaceof synthetic biology, yeast physiology and experimentalevolution.
Conflicts
of
interest
None.Acknowledgements
ThisworkwassupportedbyanAdvancedGrantoftheEuropeanResearch Council(grant#694633).JMDacknowledgesCHASSY:Model-Based ConstructionAndOptimisationOfVersatileChassisYeastStrainsFor ProductionOfValuableLipidAndAromaticCompounds.Thisprojecthas receivedfundingfromtheEuropeanUnion’sHorizon2020researchand innovationprogramundergrantagreementNo.720824.
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