Multiplex genome engineering for optimizing bio-production in

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Multiplex genome engineering for optimizing bio-production in Saccharomyces cerevisiae Jamie Auxillos, Eva Garcia-Ruiz, Sally Jones, Tianyi Li, Shuangying Jiang, Junbiao Dai, and Yizhi Cai Biochemistry, Just Accepted Manuscript • DOI: 10.1021/acs.biochem.8b01086 • Publication Date (Web): 28 Feb 2019 Downloaded from http://pubs.acs.org on March 1, 2019

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Biochemistry

Multiplex genome engineering for optimizing bioproduction in Saccharomyces cerevisiae Jamie Y. Auxillos †‡, Eva Garcia-Ruiz†, Sally Jones†, Tianyi Li#, Shuangying Jiang#, Junbiao Dai#, Yizhi Cai†* †Manchester

Institute

of

Biotechnology

(MIB),

School

of

Chemistry, The University of Manchester, 131 Princess Street, Manchester, United Kingdom ‡School of Biological Sciences, University of Edinburgh, King’s Buildings, Edinburgh, United Kingdom # Center for Synthetic Genomics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

ABSTRACT

The

field

of

synthetic

biology

is

already

beginning

to

realize its potential, with a wealth of examples showcasing the successful

genetic

production

of

engineering

highly

valuable

of

microorganisms compounds.

The

for

the

chassis,

Saccharomyces cerevisiae has been engineered to function as a

ACS Paragon Plus Environment

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Page 2 of 49

microfactory for the production of many of these economically and medically relevant compounds. However, strain construction and

optimization

necessitates

a

to

produce

wealth

of

industrially

underpinning

relevant

biological

titers

knowledge

alongside large investments of capital and time. Over the past decade, advances in DNA synthesis and editing tools have enabled multiplex genome engineering of yeast, permitting access to more complex modifications that could not have been easily generated in the past. These genome engineering efforts often result in large populations of strains with genetic diversity which can pose

a

significant

problem

to

individually

screening

via

traditional methods such as mass spectrometry. The large number of samples generated would necessitate screening methods capable of analyzing all of the strains generated in order to maximize the explored genetic space. In this perspectives paper, we focus on

recent

innovations

cerevisiae, screening

together tools,

such

in

multiplex

with as

genome

biosensors droplet

engineering

and

of

S.

high-throughput

microfluidics,

and

their

applications in accelerating chassis optimization.

YEAST AS A MICROFACTORY A

large

proportion

of

pharmaceutical

drugs

currently

available on the market are natural products, many of which are


2

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Biochemistry

extracted from microorganisms or plants (1). However, it becomes challenging to maintain the supply of these drugs when faced with environmental catastrophes, instabilities in agricultural yields and fluctuations in the supply chain. Extraction of the compounds

from

plants

tends

to

result

in

low

yields,

environmental destruction and generation of a large amount of waste,

making

commercialize.

it

difficult

to

industrially

In

addition,

several

produce

microorganisms

and which

naturally produce high value drugs are challenging to grow in a laboratory unfeasible relevant

environment, (1).

More

making

recently,

microorganisms

to

industrial-scale engineering

produce

of

medicinal

production industrially

compounds

has

emerged as a more sustainable alternative for obtaining high value compounds (2,3). The ability to enhance yields and decrease costs

of production

and

extraction, as

well

as reducing end

waste products, has encouraged the adaptation of bio-production in industry (4). Among the tractable microorganisms currently used, S. cerevisiae in particular possesses beneficial traits as a

‘microfactory’

compounds;

it

is

for

the

robust

in

production

of

fermentation

biopharmaceutical conditions;

it

is

generally regarded as safe (GRAS); it does not require expensive media or complicated growth conditions; it is well characterized and there are well established molecular biology tools for its genetic manipulation.


3

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Figure

1.

An

overview

of

the

Page 4 of 49

pipeline

for

designing

and

constructing a yeast ‘microfactory’ for compound production. A) The selection of biosynthetic enzyme genes (BSGs) for compound production and the design of pathways are carried out with the aid of computational tools. B) These BSGs are assembled with yeast

regulatory

complete

pathway

elements can

to

either

control be

gene

assembled

expression.

into

a

vector

The or

genomically integrated. Analytical screening methods are used to isolate strains producing detectable levels of the compound. C) In order to increase titers, pathway optimization by promoterterminator combinatorial engineering can be carried out to finetune expression of BSGs. Additionally, optimization by metabolic


4

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Biochemistry

engineering

can

alter

the

flux

of

precursors

to

alleviate

bottlenecks, improving titers.

The process for the construction of a yeast microfactory begins with pathway design, involving the identification of the biosynthetic

enzyme

genes

(BSGs),

obtained

from

a

single

or

multiple organisms, along with the analysis of BSG interactions with

the

host

metabolism

(Figure

1A).

Several

computational

tools used for pathway design are reviewed by Li et al. (5). The BSGs can either be directly amplified from the original producer or synthesized de novo by a commercial vendor, incorporating modifications

such

as

intron

removal,

codon

optimization

and

restriction enzyme site exclusions. Subsequently, these BSGs are assembled along with yeast regulatory elements (promoters and terminators) into plasmids or integrated into the yeast genome (Figure 1B). The different methods for the assembly of pathways have

been

further

reviewed

by

Chao

et

al.

(6).

Whilst

improvements have been made with in silico design and metabolic modeling tools, alongside developments in DNA assembly methods and increased use of automation, in most cases it can still take many

years

to

express

functional

heterologous

pathways

and

optimize product yields. Nevertheless, much progress has been made

in this field, with many

research groups and

companies


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successfully

engineering

biopharmaceutical

yeast

compounds,

Page 6 of 49

to

produce

including

a

variety

of

(7,8),

antibiotics

antioxidants (9–13) and painkillers (14) within the past decade. Advancements in biopharmaceutical compound production in yeast has been recently reviewed by Walker et al. (15). These examples highlight

the

end

value

of

rational

engineering

approaches,

utilizing knowledge generated from years of work in the area allowing

for

optimization

the of

initial yeast

pathway

strains

expression

to

produce

and high

ultimately yields

of

biopharmaceutical drugs. The optimization of compound production can be carried out with the modification of BSG expression such as the combinatorial assembly of promoters and terminators with different strengths (16–20) (Figure 1C). Alternatively, improved compound production can be achieved by engineering the chassis metabolism

for

the

construction

of

platform

strains

with

enhanced precursor supply to the heterologous pathway (reviewed in

further

detail

by

Lian

et al.

(21))

(Figure

1C).

Several

researchers have demonstrated this rational engineering of yeast metabolism for the improved supply of precursors such as acetylCoA (22) and malonyl-CoA (23). While these optimization approaches have been successfully employed, they are labor intensive and iterative;

changes

in

phenotype

modifications are considered before

due

to

individual

subsequent

gene

steps. Ideally

future efforts will center around approaches that are more rapid


6

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Biochemistry

and

multiplexed,

CRISPR

(Clustered

Repeats)-based genotypically focus

such

as

Regularly

methods, diverse

specifically

oligo-based,

Interspaced

to

enable

production

on

these

recombinase-based

better

strains.

recent

Short

In

and

Palindromic

exploration

of

this

we

innovations

in

paper,

multiplex

genome engineering for the rapid generation of improved producer strains.

Before such approaches can be employed, two questions must be addressed: Do we have the tools to efficiently explore the genetic space and maximize the utility of chassis organisms for the

purpose

appropriate thousands

of

improving

screening to

compound

production?

methodologies

millions

of

for

genetically

Do

sifting

diverse

we

have

through

strains

for

compound production? We address the first question by discussing recent

advances in semi-rational multiplex genome engineering

strategies which enable ‘black-box’ approaches to be employed with

subsequent

modifications

characterization

responsible

for

the

to desired

isolate

genomic

phenotype.

Such

approaches can, with appropriate selection, enable the isolation of strains with a desired phenotype, such as tolerance to heat or high concentrations of ethanol. However, for cases such as compound production, finding appropriate selection methods

to


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Page 8 of 49

isolate high yielding strains can prove challenging. Therefore, in

addressing

biosensors (FACS)

the

coupled

and

second with

droplet

question,

we

discuss

fluorescence-activated

encapsulation

or

the cell

use

of

sorting

fluorescence-activated

droplet sorting (FADS) in order to sieve through these large genetically diverse populations for highly productive strains.

OLIGO BASED GENOME ENGINEERING One approach to introduce genetic mutations is by hijacking the

native

homology

directed

repair

mechanism

to

incorporate

base mutation-containing oligonucleotides, in a method developed by DiCarlo et al. called Yeast Oligo-mediated Genome Engineering (YOGE). Compared to an analogous approach in prokaryotes (24), DiCarlo et al. observed low modification efficiencies, making it challenging locations eukaryotic

to (25).

obtain

mutant

Barbieri

Multiplex

et

strains al.

Automated

and

improved Genome

target on

multiple

this

through

Engineering

(eMAGE)

(Figure 2). By incorporating oligonucleotides complementary to the lagging strand of replication forks in S. cerevisiae, they could

achieve sequence modification efficiencies

greater than

40% when the target is next to an origin of replication (Figure 2A). However, the efficiency drops the further the target site is from the origin of replication (26,27).


8

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Biochemistry

Barbieri et al. demonstrated the possibility to incorporate different types of mutations such as insertions, deletions and mismatches within an endogenous gene (ade2) (26,27). Separately, multiple

locations

within

this

gene

were

simultaneously

targeted, with mutation efficiencies increasing through multiple cycles

of

eMAGE

successful

(26,27).

targeting

terminators

for

the

of

Finally,

the

promoters,

β-carotene

group

coding

demonstrated

sequences

pathway, leading

to

and

strains

yielding different amounts of phytoene, lycopene and β-carotene (26,27). In this work, >10⁵ genetically diverse strain variants were generated cycling

by (27)

applications

multiplex (Figure in

mutagenesis

2B).

tuning

This

through

technique

expression

of

iterative

eMAGE

can

have

further

BSGs

or

chassis

optimization by targeting competing yeast metabolic pathways to ultimately obtain higher yields.


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Page 10 of 49

Figure 2. An overview of the process of oligo-mediated genome engineering. A) The process for mutagenic oligo incorporation in the genome by ssDNA annealing protein (SSAP)-dependent annealing at the replication fork. B) A pathway for the production of a compound is origin

of

introduced into replication

the

element.

yeast Using

genome eMAGE,

proximal the

to an

pathway

of

interest can be mutated through the introduction of mutagenic oligos

(introducing

designed

to

target

insertions, specific

deletions

locations,

or

mismatches)

resulting

in

the

generation of a library of genetically diverse yeast strains. With

appropriate

screening

methods,

strains

with

improved

productivity as a result of specific mutations can be isolated.


10

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Biochemistry

Iterative

rounds

additional

of

eMAGE

multi-site

can

mutations

be

utilized

in

either

to

the

introduce pathway

of

interest or the yeast genome.

CRISPR FOR GENOME ENGINEERING Another method for genome engineering is the introduction of

mutations

Palindromic

using

the

Repeats

Clustered

Regularly

(CRISPR-Cas)

system.

Interspaced In

recent

Short years,

CRISPR-Cas has helped to accelerate pathway engineering through efficient

knock

out

of

target

genes

(28–30),

facilitating

integration of genetic material (31–33) and even over-expression of endogenous genes and BSGs (34,35). CRISPR has also enabled new strategies for semi-rational strain engineering. In particular the

CRISPR–Cas9-

genome-scale

and

homology-directed-repair-

engineering

(CHAnGE)

approach

(HDR)

assisted

enables

rapid

construction and screening of mutant libraries generated using oligo

pools

designed

to

incorporate

the

homology

template

upstream of the targeting gRNA (36) (Figure 3A). The pool of gRNA can be delivered to create a deletion library for all known open reading frames. The original publication showcased the strength of this approach, beyond traditional deletion libraries, when considering

complex

genetic

networks

where

specific

gene

alterations may only provide a phenotype when another gene has


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Page 12 of 49

already been mutated. In this example, the authors showed how CHAnGE

was

including

able

to

siz1

identify

when

previously

investigating

demonstrated furfural

targets

tolerance.

Furthermore, when subjecting a siz1 deletion strain to a second round

of

CHAnGE,

they

were

able

to

unmask

an

epistatic

interaction, showing that the previously unidentified lcb3, when mutated alongside siz1, could provide a cumulative increase in tolerance (36). The CHAnGE system was also shown to be effective at single codon substitutions, with tiling mutations along siz1 leading

to

tolerance

differential (36).

As

such,

enrichment they

of

strains

demonstrated

under

the

furfural

strength

and

flexibility of CHAnGE, for rapid and sequential introduction of gene knockouts and codon substitutions (Figure 3B).


12

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Biochemistry

Figure 3. An overview of the process of CRISPR-mediated genome engineering.

A) Two

introduction

of

designs of

deletions

or

the CHAnGE cassettes

amino

acid

for the

substitutions.

These

CHAnGE cassettes are cloned into the CHAnGE plasmid downstream of the SNR52 promoter. B) The CHAnGE workflow begins with the cloning

of

the

plasmid. This

CHAnGE

pool

cassette

of plasmids

oligo

pool

is then

into

the

CHAnGE

transformed into the

yeast strain of interest and different mutations (deletions or substitutions)

are

introduced.

Yeast

strains

possessing

the

desired phenotype are isolated. A subsequent round of CHAnGE resulted in the introduction of mutations allowing the analysis


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of

epistatic

gene

interactions

which

Page 14 of 49

result

in

the

improved

phenotype. RECOMBINASES FOR GENOME ENGINEERING The preceding methods have showcased the capacity to take pre-existing

strains

and

incorporate

specific

mutations

at

target sites to induce deletions, insertions or edits, with an increase in the occurrence of mutations per strain following multiple iterations of the method. Such approaches have only dealt

with

several

base

mutations

(insertion,

deletion

or

substitution) of one or several genes. This section will focus on

developments

method

called

Modification

by

in

a

recombinase-mediated

Synthetic

Chromosome

LoxP-mediated

Evolution

genome

engineering

Rearrangement

and

(SCRaMbLE).

This

approach induces a different variant of modification – genomic rearrangements including; duplication, deletion, inversion and translocation (Figure 4A) (37,38). Advancements

in

DNA

synthesis

and

assembly

methods

have

facilitated the construction of chromosomes amenable to genome wide rearrangements. Sc2.0 is an international project aiming to design and construct a completely synthetic yeast genome. In 2011, Dymond et al., demonstrated the feasibility of chemically synthesizing an entire synthetic yeast chromosome arm (synIXR) (39). In 2014, Annaluru et al. successfully assembled the first


14

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Biochemistry

‘designer’

yeast

synthetic

chromosome:

(40).

synIII

More

recently, members within the Sc2.0 consortium have completed the construction of synII (41), synV (42), synVI (43), synX (44) and synXII (45). Design principles of the Sc2.0 project include the incorporation of symmetrical loxP sites (loxPsym) downstream of every non-essential gene, ultimately enabling the induction of whole genome rearrangements using SCRaMbLE (46). Upon induction of Cre recombinase, deletions, inversions and duplications may occur

between

detectable

the

losses

loxPsym of

sites.

segments

In

were

the

absence

observed

for

of 50

Cre,

no

sampled

loxPsym-flanked segments in an experiment by Annaluru et al. testing

the

genome

demonstrated

the

integrity

power

of

of

this

synIII

(40).

technique

by

Shen

et

al.

analyzing

the

genetic diversity generated from 43 loxPsym sites on synthetic chromosome IX right arm (synIXR) when recombination was induced (47). Sequencing of 64 SCRaMbLEd strains of synIXR revealed over 156 deletions, 89 inversions and 94 duplication events (47). This genetic diversity has translated to the generation of strains with complex phenotypes such as different growth rates (39) and tolerances

to

temperatures,

different ethanol

and

stress acetic

conditions acid

including

high

(48).

When

tolerance

heterologous pathways for compound production are introduced in these strains, SCRaMbLE has been shown to improve yield. Blount et al. obtained a strain post-SCRaMbLE capable of increasing the


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Page 16 of 49

expression of pathways, such as violacein and penicillin, on 2μ plasmids, ultimately doubling their compound yields (49) (Figure 4B). Building on this approach, Liu et al. developed SCRaMbLEIn,

a

method

not

only

for

the

induction

of

genomic

rearrangements but also the simultaneous integration of pathways flanked

by

loxPsym

sites

into

the

synthetic

chromosomes.

Application of this method with the β-carotene and violacein pathways yielded strains with two-fold (500 µg/L) and ten-fold higher production (10 mg/L) respectively, compared to control strains. An additional round of SCRaMbLE in the violacein strain resulted

in

a

(Figure

4B).

rearrangements

producer

yielding

Analysis including

of

16.8

these whole

mg/L

of

strains chromosome

violacein showed and

(20)

diverse partial

duplications, deletions and inversions (20).


16

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Biochemistry

Figure 4. An overview of Synthetic Chromosome Rearrangement and Modification by LoxP-mediated Evolution. A) The design of the synthetic

yeast

chromosomes

involves

the

incorporation

of

loxPsym sites downstream of every non-essential gene. The Cre recombinase introduced

fused in

a

to

an

estradiol

plasmid.

Without

binding

domain

β-estradiol,

(EBD)

is

Cre

is

the

localized in the cytoplasm. However, upon introduction of βestradiol

in

the

culture

media,

Cre

is

translocated

to

the

nucleus where it can recombine the loxPsym sites resulting in genomic

rearrangements

including

deletions,

duplications,

translocations and inversions. B) In a synthetic yeast strain


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with

a

biosynthetic

pathway

Page 18 of 49

incorporated

(not

containing

any

loxPsym sites), the introduction of β-estradiol results in the rearrangement of the synthetic chromosome to generate a diverse population of yeast strains. With appropriate screening methods, strains producing a higher titer of the compound of interest can be

isolated

for

further

screening.

Iterative

cycles

of

this

method can be used to introduce further genetic mutations (37,38).

HIGH-THROUGHPUT SCREENING USING BIOSENSORS Chassis large

optimization

populations

genotypes. strains pipeline

of

Subsequent

becomes and

the

by

genome

yeast

engineering

strains

with

screening

of

this

bottleneck

in

the

identifying

a

method

to

often

highly

large chassis

navigate

yields diverse

population

of

optimization the

genotypic

space becomes a priority. Individual screening of these strains is labor intensive and often relies on analytical methods such as

mass

spectrometry

identification Despite

and

ongoing

throughput

and

chromatographic

quantification

improvements

lags

behind

in

that

of

products

these of

techniques of

for

interest.

technologies, strain

the

their

construction,

necessitating an intermediary method to enrich the fraction of productive strains.


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Biochemistry

Genome engineering of strains for optimal yield has to date largely

focused

on

compound

production

with

a

colorimetric

output (20,27). Alternatively, evolutionary engineering has been utilized to evolve producer strains. This involves the growth and selection of strains under a stress condition where the high production of a compound confers a fitness advantage. Whilst evolutionary

engineering

approaches

can

be

used

to

rapidly

enrich high production strains, such methods are only applicable for a narrow range of products, where survival can be tethered to production (8,50). When production does not directly confer fitness advantage, isolating individual productive strains can prove

very

challenging

and

costly.

Biosensors

provide

the

ability to screen for strains producing compounds, such as anticancer or anti-inflammatory drugs, that do not impact the growth of yeast or present as a visual screening method. As such, for most products, genetically encoded biosensors offer a promising alternative. Most commonly used biosensors are riboswitches and transcription alterations

factor-based on

the

(TF)

expression

biosensors, of

a

which

reporter

gene

enact (e.g.

fluorescence) as a result of conformational changes induced upon ligand

binding.

Riboswitches

can

also

be

constructed

using

aptamers designed for virtually any compound of interest using the

SELEX

enrichment)

(systematic technique

evolution (51,52).

of

ligands

However,

the

in

by

exponential

vitro

selected


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Page 20 of 49

aptamers with high affinity for a compound may not necessarily function when coupled with a reporter element and may require further

engineering

(53).

Despite

this,

several

aptamers

regulated by small molecules (51,54–58) have been isolated by SELEX and successfully engineered as riboswitches.

Figure

5.

High-throughput

screening

methods

for

the

rapid

discrimination between high producing cells and low producing cells. A) Production and maintenance of a compound within the cell can be screened with in vivo yeast biosensors. Two examples of

biosensors

biosensors

and

are

presented

riboswitch

-

transcription

biosensors.

An

factor-based example

of

transcription factor-biosensors is the LysR-type transcriptional regulator (LTTR), which undergoes a conformational change in the


20

Page 21 of 49 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Biochemistry

presence

of

the

ligand,

allowing

it

to

bind

to

its

target

promoter sequence and upregulate gene expression or a reporter (e.g. GFP). An example of a riboswitch biosensor system is where an

RNA

secondary

structure

exists

in

the

5’UTR

region

of

a

reporter gene (e.g. GFP). In the presence of the ligand, it binds

to the riboswitch

and the RNA

structure is

stabilized

serving as a roadblock for the scanning ribosome, inhibiting translation

of

introduced

into

analyzed

by

FACS

the

reporter

genetically to

screen

gene.

diverse for

high

These yeast

biosensors cells

producers

which

based

on

are are the

reporter. B) For compounds produced which are exported, cells can be encapsulated in a droplet to provide a microenvironment for each individual cell in a population of genetically diverse yeast strains to be screened and sorted. FADS has been used to co-encapsulate yeast strains with a different detection system (an enzymatic reaction with a non-fluorogenic substrate, or a ligand-specific spinach aptamer or a biosensor in a different microorganism such as Escherichia coli) to screen and sort for the desired phenotype (i.e. high compound production, improved enzyme activity).

Li et al. showcased the strength of biosensors by employing a TF based biosensor for malonyl-CoA, a necessary component for


21

Biochemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 49

polyketide and flavonoid biosynthesis. They demonstrated its use for screening a cDNA overexpression library to isolate yeast strains with improved malonyl-CoA production (Figure 5A) (59). Similarly, Skjoedt et al. translated cis, cis-muconic acid (CCM) and

naringenin

prokaryotic

binding

LysR-type

transcriptional

transcriptional

activators

regulators

from

(LTTRs)

for

yeast functionality (Figure 5A) (60). However, when the produced compound

does

inaccuracies intracellular

not

in

accumulate

the

in

measurement

concentration.

the due

Jang

cell, to et

this

can

fluctuations al.

cause in

its

presented

an

alternative via co-culturing a naringenin producer strain with an E. coli strain containing a naringenin riboswitch biosensor (57,58). This variant of co-culturing, however, may not serve as a method amenable for ultra-high-throughput screening due to the need for strain deconvolution prior to screening. These previous examples have relied upon well plate assays, capable of screening tens of strains at a time, which presents a clear

bottleneck

when

generating

millions

of

genotypically

diverse strains. Ultra-high throughput techniques such as FADS represent promising alternatives, to efficiently screen the full genotypic diversity. Since 1998, in vitro compartmentalization has been used to assay mutant gene libraries, encoding different variations of a


22

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Biochemistry

protein,

for

water-in-oil microfluidic

catalytic

activity

emulsions

(61).

systems

enabled

in

aqueous

compartments

Developments the

in

production

of

droplet-based

of

monodisperse

emulsions (62) and the ability to readily split and fuse droplets (63). Subsequently, Baret et al. (2009) developed a technique called

FADS,

which

combined

the

use

of

droplet-based

microfluidic systems with FACS to sort droplets according to their

fluorescence

(64).

In

this

study,

FADS

was

used

to

compartmentalize E. coli cells with either an active or inactive variant

of

substrate,

the and

reporter

enzyme

subsequently

along

sorting

for

with

a

fluorogenic

fluorescent

droplets

(64). FADS has been applied to directed evolution of horseradish peroxidase

to

isolate

the

mutant

variants

with

enhanced

catalytic activity (65). FADS has also been applied for isolating mutant strains with traits of interest such as improved protein export

(66)

recently, producer

and

xylose

Siedler strain

et

with

utilization

al. a

(67)

co-encapsulated

biosensor

(Figure

5B).

the

cerevisiae

containing

S. E.

coli

More

strain

within the same compartment. They subsequently utilized FADS for p-coumaric

acid

detection

and

screening

of

a

library

of

genotypically diverse yeast cells for high producers (Figure 5B) (68). Alternatively, yeast producer strains can be incubated in droplets containing ligand responsive Spinach aptamers (Figure 5B). Abatemarco et al. developed a system called RAPID (RNA-


23

Biochemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 49

aptamers-in-droplets) and demonstrated its utility for isolating high

producers

of

tyrosine,

tryptophan

and

phenylalanine

in

their yeast mutant library (Figure 5B) (69). Importantly this approach, drawing on SELEX and utilizing an in vitro Spinach aptamer, can enable screening of most produced compounds that are excreted from the cell. Coupling biosensors with droplet-based microfluidic systems allows

us

to

sieve

through

thousands

of

genetically

diverse

strains at a time while sorting only for high producers (69). The population of yeast cells exhibiting high yields exemplified by high fluorescence can then be carried forward for subsequent rounds of genome engineering for further diversification of the genotype whilst continuing to select for gains in yield. High producers can then be further analyzed by mass spectrometry and chromatographic techniques for the precise quantification of the compound

of

interest.

sequencing can be

In

carried

addition, out

whole

genome

and

RNA

to elucidate the causal links

between the genotypic alterations and the associated phenotypes.

CONCLUSION Significant strides have been made in both the fields of synthetic biology and metabolic engineering for the production of

high

value

compounds

in

yeast.

The

iterative

process

of


24

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Biochemistry

heterologous

pathway

engineering,

although

steps

obtaining

toward

summarized

advances

expression, labor a

optimization

intensive,

functional

made

in

the

and

remain

indispensable

‘microfactory’.

field

of

rational

Here,

multiplex

we

genome

engineering to readily probe the genetic space for modifications that

can

aid

production

of

engineering

directing the

has

the

metabolic

compound

been

of

shown

flux

interest.

to

allow

towards

improved

Oligo-based

the

genome

introduction

of

insertions, deletions, and substitutions at a high efficiency in a semi-rational fashion. The location of the targeting oligos are

specifically

designed,

however

the

exact

combination

of

mutations cannot be predicted. Individual strains with improved compound production can be isolated, and the genotype can be mapped

to

a

phenotype,

such

as

higher

yields.

However,

the

mutation efficiency varies depending on the proximity of the targeting

oligo

to

an

origin

of

replication.

CRISPR-based

techniques such as CHAnGE do not have this specific restriction. However, when

compared to

eMAGE,

the

technique

has

not been

demonstrated for inducing more than two mutations within a short segment of DNA. In both of these cases, the introduction of multiple cycles of genome engineering, either by eMAGE or by CHAnGE, allows the introduction of a greater number of mutations as well as the opportunity to probe the effect of additional mutation

events.

SCRaMbLE,

on

the

other

hand,

introduces

a


25

Biochemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 49

different type of mutation – genome rearrangement events. This tool allows researchers to analyze the interactions of multiple rearrangements and the contribution of gene location within the genome alongside gene copy number. With

the

sheer

scale

of

genetically

diverse

strains

obtained from genome engineering studies, a strategy to maneuver through the genetic space is crucial for the success of these projects. The recent innovations in biosensors along with years of

research

and

advancements

made

in

droplet-based

sorting

technologies present a promising solution to help open one of the key limiting bottlenecks in order to bring forward genomescale engineering projects for yield optimization of high value compounds.

It

is

therefore

our

belief

that

a

fusion

of

sequential multiplex genome engineering techniques paired with tailored

biosensors

and

high-throughput

droplet

sorting

technologies may offer previously unachievable throughputs, and significantly

boost

the

utilization

of

bio-pharmaceutical

production in industry.

AUTHOR INFORMATION *Correspondence

should

be

addressed

to

Y.

C.

at

[email protected]


26

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Biochemistry

ACKNOWLEDGMENTS The

work

is

funded

through

a

Biotechnology

and

Biological

Sciences Research Council grant (BB/P02114X/1), a Bill & Melinda Gates Foundation award (RB0447) and the University of Manchester President’s Award for Research Excellence to YC. JA is jointly supported with a graduate fellowship from Gates Foundation and the University of Manchester. This work was also supported by the

National

(31725002),

Science by

Bureau

Fund of

for

Distinguished

International

Young

Cooperation,

Scholars Chinese

Academy of Sciences (172644KYSB20170042) and by the Key Research Program of the Chinese Academy of Science (KFZD-SW-215) to JD.

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