Synthetic Biology of Yeast - Biochemistry (ACS Publications)

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Synthetic Biology of Yeast Zihe Liu, Yueping Zhang, and Jens Nielsen Biochemistry, Just Accepted Manuscript • DOI: 10.1021/acs.biochem.8b01236 • Publication Date (Web): 08 Jan 2019 Downloaded from http://pubs.acs.org on January 8, 2019

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Biochemistry

Synthetic Biology of Yeast Zihe Liu1, Yueping Zhang1, Jens Nielsen1,2,3,* 1Beijing

Advanced Innovation Center for Soft Matter Science and Engineering,

College of Life Science and Technology, Beijing Key Laboratory of Bioprocess, Beijing University of Chemical Technology, Beijing, China. 2Department

of Biology and Biological Engineering, Chalmers University of

Technology, SE41296 Gothenburg, Sweden 3Novo

Nordisk Foundation Center for Biosustainability, Technical University of

Denmark, DK2800 Kgs. Lyngby, Denmark *Corresponding Author Jens Nielsen, Chalmers University of Technology, Kemivägen 10, SE 412 96, Gothenburg, Sweden, Tel: +46 (31) 772 38 04, e-mail: [email protected].

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KEYWORDS: Synthetic Biology, yeast Saccharomyces cerevisiae, building blocks, biosensors, gene circuits

ABSTRACT:

With the rapid development of DNA synthesis and next-generation sequencing, synthetic biology that aims to standardize, modularize and innovate cellular functions, has achieved vast progress. Here we review key advances in synthetic biology of the yeast Saccharomyces cerevisiae, which serves as an important eukaryal model organism and widely applied cell factory. This covers the development of new building blocks, i.e. promoters, terminators and enzymes, pathway engineering, tools developments, and gene circuits utilization. We will also summarize impacts of synthetic biology on both basic and applied biology, and end with further directions for advancing synthetic biology in yeast.


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Biochemistry

Synthetic biology aims to standardize and modularize the design and engineering of organisms to achieve novel functions, or de novo construct genomes or even organisms from scratch, through rational laboratory procedures or through automation. Synthetic biology plays a central role in both basic and applied research, with increasing impacts on each step of the DesignBuild-Test-Learn (DBTL) cycle. These include new designs based on genomic and kinetic models, new building methods, and more robust biosensors for both build and test. Saccharomyces cerevisiae, often referred as yeast, is an important eukaryal model organism as well as a widely-used cell factory for production of fuels, chemicals, pharmaceuticals and food ingredients. Many synthetic biology tools and concepts have therefore been pioneered in this organism, either for demonstration of the applicability of new methods or directly as cell factories. In this review, we will present and discuss recent advances in synthetic biology of this important yeast. Engineering of Building Blocks: Promoters, Terminators and Enzymes

Figure 1. Building blocks for synthetic biology of yeast. There are three levels for the building blocks: expression level, protein level, and pathway level.


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Synthetic biology distinguishes from metabolic engineering, as it aims to precisely define building blocks at all levels, including expression levels, protein levels and pathway levels. This then allows modulation of cellular behaviors, building of gene circuits and eventually construction of recombinant or de novo cells through automation (Figure 1). For example, on the expression level three excellent examples are: 1) developing synthetic minimal promoters and terminators that could reduce lengths of native yeast promoters by 80-90%, yet retain high levels of expressions,1 2) increasing the expression level of a strong glyceraldehyde-3-phosphate dehydrogenase (TDH3) promoter by 50-fold, by fusing the 3’ region of the TDH3 promoter with the intron of a 40S ribosomal subunit component (RPS25A),2 and 3) predicting the relationship between nucleosome positioning and terminator function to construct synthetic terminators with a 4-fold increase in expression and a 2-fold increase in the termination efficiency.3 Synthetic biology has also been used to alter enzymatic activities or functions at the protein level. Thus, Zhu et al. demonstrated a showcase to modularize engineering of fatty acid synthases (FASs) by incorporating the activity of a shortchain thioesterase (sTE) into the reaction chamber, converting the synthesis of canonical fatty acids (C16, C18) to short/medium-chain fatty acids (C6-C12).4 Papagiannakis et al. reported the incorporation of auxin-inducible degradation tags for inducible and reversible control of protein turnover.5 Similar tags, such as light responsive tags, have also been reported.6 Moreover, protein


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Biochemistry

engineering has also been used for obtaining new transporters by mutating the binding pocket of the ligand. Wang et al. reported a highly active xylose transporter with a 43-fold increase in xylose transportation capacity, high xylose specificity and with almost no glucose inhibition.7,

8

Young et al. identified a

conserved motif across different xylose transporters and demonstrated that the preference and kinetics of sugar transporters can be reprogrammed through motif mutagenesis.9 Farwick et al. set up a growth-based screening system and identified mutant transporters for efficient co-fermentation of glucose and xylose.10 Hu et al. discovered a fatty alcohol transporter FATP1 from humans that could export fatty alcohol from yeast, and through constructing various chimeric proteins the functional domain of FATP1 was also identified.11 Besides enzymes and transporters, polymerases and transcriptional regulators have also been engineered to alter cell metabolism. For example, Qiu et al. reported improving the ethanol tolerance of yeast through mutagenesis of its central RNA polymerase II and hereby reprogramming its transcription profile.12 In another study, Shi et al. reported abolishing an AMPactivated S/T protein kinase (Snf1) dependent regulation of acetyl-CoA carboxylase (Acc1) through introducing two point mutations in the phosphorylation sites of Acc1 and hereby increased malonyl-CoA derived products, including fatty acid ethyl esters and 3-hydroxypropionic acid.13 It is also often possible to alter cellular phenotypes by targeting transcription factors, as illustrated in a study where 1-hexadecanol production was enhanced by 98%


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by knocking out histone deacetylase Rpd3, the negative regular of an inositol3-phosphate synthase gene (INO1) in phospholipid metabolism.14 Engineering of Pathways Pathway

level

engineering

can

be

roughly

divided

into

pathway

compartmentalization, combinatorial pathway engineering, genome mining and model-based designs. Pathway compartmentalization. Briefly, compartmentalization can provide a compact and more suitable environment, concentrate pathway intermediates, as well as avoid inhibiting chemicals and competing pathways that may interact with pathway intermediates. Compartmentalization of synthetic pathways in mitochondrion, peroxisome and endoplasmic reticulum have been applied for overproduction of terpenoids,15 fatty acids derived molecules,16 and triterpenoids,17 respectively. Combinatorial pathway engineering. combinatorial libraries with varied enzyme levels have been reported with a 4-fold increase in the production of itaconic acid, to a titer of 1.3 g/L.18 The precise expression control for each gene of the itaconic acid pathway was enabled by using a promoter-terminator library with expression variations across a 174-fold range. Moreover, tuning the expression of several pathway genes by combining promoter strength finetuning, copy numbers and integration locus variations were also used to improve the production of geranylgeraniol to a level of 1.3 g/L.19


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Biochemistry

Genome mining. Beside method development in the wet lab, the vast development of next-generation sequencing and bioinformatics analysis have greatly accelerated synthetic biology, especially when discovering novel synthetic pathways. Many of these pathways would be obvious to refactor in yeast, which has few endogenous enzymes involved in secondary metabolites and there will therefore not be any side reactions occurring. For example, through genome mining of 24 Penicillium species, Nielsen et al. identified 1317 biosynthetic gene clusters (BGCs) and experimentally validated the production of antibiotic yanuthones, and moreover identified a novel compound from the same pathway.20 Similarly, through comparative transcriptomic analysis of four strains of the marine actinomycete genus Salinispora, 49 BGCs were identified.21 Furthermore, Awan et al. expressed penicillin gene clusters in

Saccharomyces cerevisiae and generated a yeast strain secreting bioactive benzylpenicillin against Streptococcus bacteria.22 Ren et al. developed a plug‐and‐play pathway refactoring workflow in a high‐throughput manner in both Escherichia coli and Saccharomyces cerevisiae, and built 96 pathways for combinatorial carotenoid biosynthesis.23 Model-based designs. Model-based design is also finding increased applications in synthetic biology, e. g. to help in de novo pathway design, to optimize cell growth or production, etc. For example, Pandit et al. reported an orthogonal network with minimal interactions between cell growth and succinate production.24 They demonstrated that their design had significant advantages


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for improving succinate production, compared with the traditional growthcoupled approach. Furthermore, Sánchez et al. expanded the concept of genome-scale metabolic modeling to encompass quantitative proteomics data and kinetic information of all the enzymes, so that each metabolic flux could not exceed its maximum capacity.25 It has been demonstrated that this model could precisely predict phenotypes that traditional models could not, such as growing on different carbon sources in excess, under stressed conditions, or overexpressing specific pathways.25 To summarize, engineering of building blocks helps in standardizing and modulating cell metabolisms and recombinant pathways. However, how to precisely and effectively incorporate the building blocks into yeast cell factories represents another challenge in metabolic engineering and synthetic biology. In the following, we will take strain engineering as an example to summarize the tools development principles in S. cerevisiae. Tools Development in Strain Engineering


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Biochemistry

Figure 2. Tools Development for strain engineering in yeast. These systems can be divided into genome disruptions, point mutations, integrations, in vivo assembly, transcriptional regulations, genome-wide mutagenesis, and directed evolution, as well as automation. DSB, double-strand break; AD, activation domain; and RD, repression domain.

Table 1. A representative summary of the recent development of strain

engineering in yeast S. cerevisiae. Strain engineering System features

Application and efficiency

systems Disruption and deletion Cas9 with gRNAs

Simultaneously delete 4-gene with 96%

cleaved by Csy4

efficiencies

Ferreira et al.71

Simultaneously disrupt 4-gene in diploid strains (8 Hi-CRISPR system: Lian et

al.26

alleles) and triploid strains (12 alleles) with ~100% Cas9 with gRNAs efficiencies


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Jakočiūnas et al.72,

CasEMBLR: Cas9 and

Simultaneously disrupt up to 5 targets with 50-

Cloning-free GTR-

Simultaneously disrupt 8-gene using constructed

73

gRNAs in individual

100% efficiencies

Zhang et al. (In

CRISPR system: Cas9

plasmid with 80% efficiencies.

cassettes publication)

with gRNAs flanked with

Simultaneously clone-free disrupt 6-gene

tRNAs

disruptions in 3 days with 60% efficiencies

Integration and in vivo assembly Cas9 and gRNAs in

Simultaneous in vivo assembly and integration of

individual cassettes

6 genes

Cas9 with single guide

integration of 18-copy of the 24kb construct

RNA targeting multiple

comprised with xylose utilization and butanediol

delta sites

production

Mans et al.30

Shi et al.29

Point mutation create point mutations from cytosine to Nishida et al.28

dCas9 fused with AID guanine/thymine precisely edit bases up to 53 bp from the nicking Cas9 Nickase with donor

Satomura et

al.74

site including PAM sequence with low off-target on the same plasmid efficiency

Transcriptional regulation 38 and 78-fold activation of promoters PHED1 and Chavez et

al.75

dCas9-VPR single gRNA PGAL7


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Biochemistry

dCas9 with VP64 20 to 50-fold higher than the activation achieved Zalatan et al.31

recruitment domains into with dCas9-VP64 fusion. the guide RNA combinatorial tri-CRISPR based strategy to improved β-carotene production and yeast surface

Lian et

al.33

combine genome display of an endoglucanase by 3 and 2.5-fold disruption, transcriptional activation and reduction

Automation and directed evolution Cas9 and donor with increased the editing efficiency by 5-fold by barcodes (MAGESTIC), Roy et

al.27

recruiting the LexA-Fkh1p fusion protein and LexA-Fkh1p fusion achieved single-nucleotide mutations protein error-prone PCR and integration mutant

Jakočiūnas et

al.34

efficiencies reaching 98-99% libraries in single or multiple genome sites RNA interference achieved multiplex optimization of yeast genomes

Si et al.

35

(RNAi)-assisted genome for diverse phenotypes evolution in yeast


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CRISPR-Cas9-and

Bao et al.38

homology-directed-

efficiently output tens of thousands of genetic

repair-assisted genome-

variants genome-wide with single-nucleotide

scale engineering

resolution

(CHAnGE)

Tools development has achieved vast progress in strain engineering, especially with the help of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR). There are many different strategies to engineer cell factories, i.e. gene disruptions, genes integrations, introduction of point mutations, in vivo assembly of DNA constructs, transcriptional regulations, genome-wide mutagenesis and directed evolution, and recently there have been attempts on automating many of these engineering strategies, Figure 2 and Table 1. For example, Lian et al. reported multiplex genome editing in polyploid

industrial

yeast

strains

with

~100%

efficiencies,

including

simultaneous 4-gene disruptions in diploid strains (8 alleles) and triploid strains (12 alleles).26 Moreover, we recently developed the gRNA-tRNA-array CRISPR/Cas9 (GTR-CRISPR) that could achieve clone-free engineering for simultaneously 6-gene disruptions in 3 days. The strategy avoids the cloning step in Escherichia coli by directly transforming the Golden Gate reaction mix to yeast (in revision). Moreover, Roy et al. reported the use of array synthesized oligos for high-throughput editing and barcode integration for robust


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Biochemistry

phenotyping.27 Briefly, the fusion protein of LexA DNA binding domain and forkhead-associated domain (LexA-Fkh1p) was introduced to bring the repairing dsDNA close to the breaking site, resulting in a 5-fold increase in the editing efficiency.27 Notably, single-nucleotide mutations were also achieved independently on the protospacer adjacent motif (PAM).27 Moreover, also for point mutations Nishida et al. fused activation-induced cytidine deaminase (AID) to dCas9, and could hereby deaminate deoxycytidine to deoxyuridine and effectively create point mutations from cytosine to guanine/thymine.28 Regarding integration and in vivo assembly, Shi et al. developed the delta integration CRISPR-Cas (Di-CRISPR) that could achieve efficient and markerless integration of 18-copy of the 24kb construct.29 Mans et al. introduced the CRISPR based molecular Swiss army knife to achieve simultaneous two gene disruptions and in vivo assembly and integration of six genes.30 Besides genome editing, transcriptional regulation also plays an important role in strain engineering, especially in separating cell phases, timely controls and gene circuits. For example, Zalatan et al. constructed modular scaffold RNAs including both guide RNAs and effector protein recruitment sites.31 They demonstrated this system through combinatorial regulations, and enabled establishing five distinct output states of the highly branched violacein biosynthetic pathway.31 Deaner et al. reported a dual-mode activation and repression system through multiplexed targeting the dCas9-VPR activator to


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the promoter region and the ORF region, respectively.32 Moreover, Lian et al. describes a combinatorial tri-CRISPR based strategy to combine genome disruption, transcriptional activation and reduction, and improved β-carotene production and yeast surface display of an endoglucanase by 3 and 2.5-fold, respectively.33 With efficient genome editing and regulation tools, it has become possible to apply these in directed evolution and automation. For example, Jakočiūnas et

al. reported a robust directed evolution method through error-prone PCR and integration mutant libraries in single or multiple genome sites, with efficiencies reaching 98-99%.34 Si et al. reported RNA interference (RNAi)-assisted genome evolution (RAGE) in yeast.35 Through iterative and automation-based screening of genome-wide cDNA libraries in the sense (overexpression) and anti-sense (knockdown) directions, RAGE enabled directed evolution of the yeast genome, and can continuously improve target phenotypes, including acetic acid tolerance, glycerol utilization, cellulose expression and isobutanol production.36,

37

Moreover, Bao et al. reported a trackable CRISPR-based

method, that could efficiently output tens of thousands of genetic variants with single-nucleotide resolution.38 However, the editing efficiency may be affected by the presence of PolyT and Type IIs BsaI sites, and optimizations of type II RNA promoters and applications of Golden gate independent assembly methods may further improve this method.38 Building of Gene Circuits


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Biochemistry

One of the fundaments of synthetic biology is the reconstruction of completely new regulatory circuits that can modulate gene expressions. The advantage of using such synthetic circuits is that their orthogonal nature often to do not interfere with endogenous regulatory systems and hence can enable novel functions, e.g. decoupling product formation and growth. These genetic circuits employ sensors and regulatory systems to achieve programmable adjustments of cell metabolisms based on environment variations.39 Examples of genetic circuits applied in yeast are: 1) a xylose-sensing/regulation gene circuit with three-node tuning levels, including the expression level of the repressor, the operator position and the operator sequence;7 2) engineered biosensors with dimeric ligand-binding domains as synthetic “AND” gates, with a 20-fold stronger response than either one alone;40 and 3) a light-switchable gene expression system, the yLightOn system, that exhibits a 500-fold On/OFF ratio and fast expression kinetics.41 The application of gene circuits hinges on the availability of specific and sensitive biosensors, and a summary of recent developments of biosensors can be found in Table 2 and Figure 3. Details could also be found in recent reviews.42-45


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Figure 3. Overview of biosensors in yeast S. cerevisiae. Biosensors can be divided into two major categories: transcription dependent biosensors and transcription independent biosensors. Transcription dependent biosensors recognize the analytes of interest, by a native or recombinant receptor to transduce the signal. Transcriptionindependent biosensors will directly recognize and report the analytes, using either protein binding triggered Fluorescence Resonance Energy Transfer (FRET) or byproducts of metabolism (Reaction based). Table 2. A representative summary of the recent development of biosensors in yeast S. cerevisiae. Sensor

Analytes

Reporter

Detection range

Reference

synthetic

C8: 19-250 μM,

promoter with

C10: 2-500 μM,

GFP reporter

C12: 1-250 μM

neurotensin

PFIG1::ZsGreen

~0-0.1μM

77

Angiotensin II

PFIG1::ZsGreen

~10-9-10-2 M

78

5-HT

PFIG1::ZsGreen

~10-6-10-4 M

79

~1-10 nM

80

Transcription-dependent Receptor based free fatty acid receptor (GPR40) or olfactory receptor (OR1G1) Neurotensin receptor type 1 (NTSR1) Angiotensin II (Ang II) type 1 receptor (AGTR1) 5-HT1A receptor (HTR1A) mating receptor from fungal pathogens

medium-chain fatty acids

mating peptide from fungal pathogens

PFUS1::CrtI lycopene

Transcription factor based


76

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Biochemistry

synthetic promoter with bisphenol A-targeted

bisphenol A

receptor (BPA-R)

(BPA)

estrogen response

0.107-100 μM

81

element (ERE) for EGFP expression PTEF1 with FapR

FapR

malonyl-CoA

binding sites for GFP expression PTEF1UAS -fapO-

FapR

malonyl-CoA

GAL1CORE:: yeGFP

FapR

malonyl-CoA

~0-14 μM Cerulenin 0-10 mg/L Cerulenin

PGPM1-

0-8 mg/L

fapO::tdTomato

Cerulenin

82

83

84

synthetic promoter with XylR XylR

xylose

transcriptional

~0-33.3 mM

7

0.2-1.4×10-3

85

Not specified

86

regulators for EGFP expression synthetic BenM

cis, cis-muconic

promoter with

acid

BenO for EGFP expression synthetic

PhlF-VP16

DAPG

promoter with PhlO for ADE2 expression synthetic promoter with

ADA-Gal4AD

methyl iodide

ADA binding site for EGFP

2.8×10-5-4×10-3 M MeI

87

expression synthetic "AND"Gal4BD-PRO0 and DIG1-

digoxigenin and

gate with

VP16

progesterone

luciferase

Not specified

88

10 to 100 μM

89

reporter Yeast native promoter based

PDR5 promoter

extracellular diclofenac

PPDR5::turboGFP


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extracellular

PDR12 promoter

Short- and medium-chain

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C6, C7, and C8 PPDR12::GFP

fatty acids (FA)

FA from 0.01 up to 2, 1.5,

90

and 0.75 mM

Cell Stress including heat

HSP42 promoter

shock, copper

PHSP42::mCherry

Not specified

91

PGPD2::yEGFP3

Not specified

92

FRET

0-10 μM

93

methionine

FRET

Not specified

94

lysine

FRET

Not specified

95

FRET

0.001-1 mM

96

FRET

Not specified

97

Not specified

97

ions, oligomeric amyloid beta

GPD2 promoter

cytosolic NADH/NAD+

Transcription-independent Protein based estrogen receptor α, estrogen receptor β or androgen receptor ligand binding domains fused with

steroidal derivatives

YFP methionine binding protein (MetN) sandwiched between CFP and YFP lysine binding periplasmic protein (LAO) sandwiched between GFP variants cytosolic eCFP-TreR-Venus

trehalose-6phosphate

FoF1-ATP synthase is between FRET donor and

ATP levels

acceptor roGFP emission redox-sensitive GFP

mitochondrial

at different

(roGFP)

redox state

excitation wavelength

inserting GFP into to conformation-sensitive positions of ammonium

Ammonium

GFP

0-200 μM

98

iron

magnetic

Not specified

99

transporters iron storage ferritin (Ft) Reaction based L-tyrosine -> Ltyrosine hydroxylase and

L-DOPA and L-

DOPA -> yellow

L-DOPA 2.5-

DOPA dioxygenase

tyrosine

fluorescent

2,500 μM

betaxanthins


100

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Biochemistry

L-tyrosine -> Ltyrosine hydroxylase and DOPA dioxygenase

L-tyrosine

DOPA -> yellow

L-tyrosine 0-

fluorescent

600 mg/L

101

betaxanthins shikimate and NADP+ shikimate ⇄ NADPH + H(+) + dehydroshikimate

cytosolic NADPH/NADP+

dehydroshikimat e concentrations

Not specified

102

Not specified

103

Not specified

104

Not specified

105, 106

100 μM

107

0-1 μM

108

determined by GC-MS/MS Aequorin emission light

Aequorin

cytosolic Ca2+

with Ca2+ and additional cofactor coelenterazine calculated from

Maltose phosphorylase: maltose + Pi ⇄ glucose + glucose-1-phosphate

cytosolic phosphate

intracellular glucose, G1P and maltose concentrations

dithiol YFP-based GSH biosensor

GSH/GSSG

redox sensitive YFP Redness

AMP pathway

Copper

PCUP1::ADE5,7 in ade2Δ

Probe based Redoxfluor (a FRET-based redox probe)

thioredoxin

FRET

Impacts on Basic and Applied Biology Synthetic biology has made several significant impacts on both basic and applied yeast biology. In basic biology, there are two recent achievements that stand out: 1) the synthetic yeast project (Sc2.0) aiming to build the first synthetic eukaryotic genome from scratch.46 So far, six out of the total sixteen chromosomes, including chromosome II, III, V, VI, X and XII, have been chemically


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synthesized.47-52 Through debugging of synthetic chromosomes and the SCRaMbLE analysis (synthetic chromosome rearrangement and modification by loxP-mediated evolution), new insights into the organization and function of the yeast genome have been achieved. Moreover, a number of yeast mutants with improved functions have also been identified.53-56 2) the singlechromosome project. Shao et al. reported that the chromosome number and the order of the fusion chromosome may have minimal effects on cell fitness.57, 58

Sixteen yeast chromosomes were merged into a single chromosome by

homology recombination and deletion of telomeres and centromeres from different chromosomes.58 Although with different 3D structures, the singlechromosome yeast has almost identical transcriptome profiles with the wildtype, and slightly reduced but still robust cell growth across different environments. Through ligation of the single chromosome ends via CRISPR induced homologous recombination, Shao et al. further created a new yeast strain

with

one

single

circular

chromosome

also

containing

16

chromosomes.59 However, the new yeast showed reduced cell growth rate and fitness at tested conditions, suggesting that linear chromosome, as well as telomeres and telomerase are beneficial for gaining more fitness to environmental challenges. Both of these projects have laid the foundation for better understanding of chromosome organization and function in yeast, and this information can ultimately be used to build customized organisms from the ground up.47


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Biochemistry

The impacts of synthetic biology on the applied biology, e.g. on metabolic engineering, have been extensively reviewed recently.60-63 A few recent examples to mention are work on reconstruction of a 14-step gene cluster from the plant Oapaver somniferum in yeast, and hereby achieving functional expression of a complete pathway leading to the anticancer drug Noscapine,64 engineering yeast to biosynthesize aromatic monoterpene molecules that enable generation of a hoppy taste in beer without addition of hops,65 and extensive work on reprogramming yeast metabolism from traditional alcoholic fermentation to a pure lipogenesis metabolism.66 Another interesting example was the use of thermodynamic and kinetic analysis together with structureguided engineering, to in vitro validate the first synthetic CO2 fixation pathway, called

the

crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA

(CETCH)

cycle.67 This CETCH cycle encompasses 17 enzymes from 9 different organisms, including 3 engineered enzymes, and could fix CO2 at a rate that is comparable to the Calvin cycle in cell extracts. This work opened the possibility of in vivo applications in yeast or other organisms for CO2 fixation. OUTLOOK So far, vast achievements in the engineering of building blocks, tools development in strain engineering and building of gene circuits have provided new opportunities for both basic and applied research. However, there are still challenges remaining: 1) Some basic knowledge, such as yeast transcriptional units (promoters/terminators) and the genome-wide binding sites of


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transcription factors, is still poorly defined, and this generates barriers for tools development (e.g. yeast native promoter-based biosensors) and application (e.g. Sc2.0 project). 2) Compared with bacteria, yeast systems are more complexed and tightly controlled, thus the introduction of synthetic units are more likely to interfere with endogenous systems. Thus, future directions may include deep understanding of yeast annotating mechanisms, construction of more robust genome scale and kinetic models to achieve more precise simulations, developing of RNA-targeting CRISPR systems for efficient and multiplex post-transcriptional engineering,68 developing of bioelectronics with real-time sensing and control of biological functions,69 and construction of molecular machines with bio-inspired mechanisms.70 In conclusion, we are confident that the immense advancements in synthetic biology of yeast in recent years is only the beginning of further development of the synthetic biology field, and the era of customized synthetic biology and automation will soon begin. AUTHOR INFORMATION *Corresponding Author Jens Nielsen, Chalmers University of Technology, Kemivägen 10, SE 412 96, Gothenburg, Sweden, Tel: +46 (31) 772 38 04, e-mail: [email protected]. Author Contributions JN conceived the outline. ZL and YZ wrote the manuscript. All authors edited the manuscript.


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Funding Sources This work was supported by the Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, the Beijing Municipal Natural Science Foundation (5182017), the Fundamental Research Funds for the Central Universities (buctrc201801), State Key Laboratory of Microbial Technology Open Projects Fund (Project NO. M201706), State Key Laboratory of Chemical Resource Engineering, the Novo Nordisk Foundation (Grant NO. NNF10CC1016517) and the Knut and Alice Wallenberg Foundation. Notes The authors declare no competing financial interest.


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graphic for use in the Table of Contents only


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Synthetic Biology of Yeast - Biochemistry (ACS Publications)

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