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Nature Reviews Genetics | AOP, published online 28 May 2014; doi:10.1038/nrg3746
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OPINION
The dawn of evolutionary genome
engineering
Csaba Pál, Balázs Papp and György Pósfai
Abstract | Genome engineering strategies — such as genome editing, reduction
and shuffling, and de novo genome synthesis — enable the modification of
specific genomic locations in a directed and combinatorial manner. These
approaches offer an unprecedented opportunity to study central evolutionary
issues in which natural genetic variation is limited or biased, which sheds light
on the evolutionary forces driving complex and extremely slowly evolving traits;
the selective constraints on genome architecture; and the reconstruction of
ancestral states of cellular structures and networks.
Laboratory evolution experiments coupled
with whole-genome sequence analyses offer
extremely powerful tools for the investigation of evolution in real time1. Microbial
populations are particularly amenable to
such investigations. As the generation
times of many bacterial species range
from an hour to ten hours2, phenotypic
and molecular changes during the course
of laboratory evolution can be monitored
over thousands of generations. Studies in
microbial populations have offered insights
into key conceptual problems, such as the
extent of convergent evolution, the origin of
key innovations and the mechanisms that
influence evolvability 3. Through precise
control of population size and selection
pressure, experimental evolution enables
the proper testing of theories of evolution.
This approach is immensely successful, not
least because the molecular mechanisms
that underpin evolution of laboratory and
natural populations are related to each other.
However, there are several important
issues that cannot be readily addressed by
microbial experimental evolution.
The major limitations of microbial
experimental evolution are largely due
to the shortage of natural variation in the
laboratory, the limited timescale of such
experiments and the lack of appropriate
control of mutational processes. Laboratory
evolution experiments typically last around
200–1,000 generations, which results in the
accumulation of 4–20 independent mutations per population4. An Escherichia coli
strain adapted to glucose minimal medium
in the laboratory acquired only 45 mutations over 20,000 generations of evolution5.
several important issues …
cannot be readily addressed by
microbial experimental evolution
As a consequence, several molecular innovations lack the intra-population genetic
variation on which selection could act. For
example, the capacity of E. coli to exploit citrate as a carbon source took 33,000 generations of laboratory evolution (that is, more
than 14 years)6. Clearly, researchers focused
on the evolution of a specific molecular
pathway cannot wait years for the fortuitous
occurrence of such extremely rare events in
the laboratory. Moreover, given the limited
timescale of microbial laboratory evolution experiments, it is difficult to compare
the results to macroevolutionary trends of
genome evolution. For example, large-scale
reduction of bacterial genomes occurs
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readily in nature but extremely slowly in
experimental evolution settings7. Therefore,
the driving evolutionary forces and the consequences of massive genomic rearrangements on cellular viability and adaptation to
novel conditions remain a terra incognita.
Most microbial experimental evolution studies focus on complex traits, such
as nutrient limitation and heat stress3, in
which mutations in hundreds of genes
across the genome contribute to fitness.
In these cases, it is difficult to disentangle
beneficial mutations from neutral ones. The
genetic basis of adaptation can only be deciphered in a tedious manner by individual
and combined insertions of the observed
mutations into the ancestral genome.
However, the goal is frequently to study the
evolution of a particular cellular subsystem.
As long as a single gene is concerned, the
standard ‘toolbox’ of directed protein evolution
provides an adequate solution8. Indeed,
conceptual and technical advancements
in this research field have led to a better
understanding of how proteins evolve in
nature8,9. However, when larger genetic
circuits, enzymatic pathways or complex
subcellular structures are concerned, the
generation of mutant libraries of sufficient
size for laboratory evolution remains a
cumbersome exercise.
Genome engineering — the targeted
sequence modification of at least two
distinct genomic regions (reviewed in
REFS 10–12) — provides a complementary
approach to study some of the notoriously
difficult evolutionary problems for two reasons: first, it allows the generation of large
mutant libraries across many predefined
loci; second, it enables the construction of
genomic alterations that do not occur spontaneously in the laboratory (FIG. 1). Genome
engineering can also facilitate the generation of modifications that have never been
explored in nature. Genome engineering
tools — including oligonucleotide-mediated
recombineering (that is, recombination
engineering), engineered nucleases and
de novo genome synthesis — enable the
rapid editing of multiple genomic segments13–15, the reduction of microbial
genomes16, combinatorial shuffling of small
DNA segments or complete genomes17,18,
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and chemical synthesis and integration
of large DNA segments or even complete
genomes into a host organism19 (TABLE 1).
In this Opinion article, we highlight
the potential of genome engineering for
the study of evolution and focus on microbial systems. We present key studies with
evolutionary implications, and discuss the
Evolution of the genetic code
The evolution of the genetic code is particularly difficult to study, as most mutations that alter the genetic code have fatal
consequences. Indeed, natural alterations
to the standard genetic code rarely occur
across the phylogenetic tree21, probably
because such modifications would require
prospects and problems of the emerging
field of evolutionary genome engineering.
Methodological details of genome engineering 10–12, details of microbial experimental evolution3 or the genomic mechanisms
that drive natural evolution20 are not discussed, as these topics have been reviewed
in detail elsewhere.
a
b
Nascent
polypeptide
E. coli
RF1
Growth
Wild-type
E. coli
genome
MAGE
Electroporation and
λ-Red recombination
RF2
mRNA
Selection or
screening
AAGCAGUAG
Recode UAG to UAA
using MAGE and CAGE
MAGE-generated, region-specific mutants
RF1
UAG codons
eliminated
RF2
AAGCAGUAA
Delete RF1 ORF (that is, prfA)
Genome
CAGE
UAG codons
and RF1
eliminated
RF2
AGCAGUAA
∆prfA
Reassign UAG
Hierarchical combination
of modified segments by
conjugation
UAG
reassigned
+
∆prfA
RF2
E. coli with
new traits
UAGCAGUAA
Orthogonal aminoacyl-tRNA
synthase and tRNA
Figure 1 | Genome editing approaches for altering the genetic code
on a genome-wide scale in E. coli. a | Multiplex automated genome
engineering (MAGE) allows production of multiple targeted, small
mutations through oligonucleotide-mediated allelic replacement in an
iterative manner, which results in a large number of allelic combinations. Coloured boxes indicate engineered mutations. Conjugative
assembly genome engineering (CAGE) allows step-wise transfer of individually engineered, marked genomic modules into a single genome.
Full transfer of a genomic segment is controlled by inserting oriT conjugational start sites and selection markers at appropriate positions
(not shown). b | MAGE and CAGE can be used to construct a recoded
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| Genetics
Escherichia coli genome with an expanded Nature
genetic
code. All
occurrences of the UAG codon have been removed, and translation termination at UAG has been eliminated by deleting prfA, which is the open
reading frame (ORF) encoding release factor 1 (RF1). The now blank
UAG codon has been reintroduced, along with an orthogonal set of
aminoacyl-tRNA synthase and tRNA, to encode a non-standard amino
acid. Part b from Lajoie, M. J. et al. Genomically recoded organisms
expand biological functions. Science 342, 357–360 (2013). Reprinted
with permission from AAAS.
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prior reorganization of codon usage across
the whole genome. Nevertheless, it has
become clear that minor deviations from
the universal genetic code occur sporadically in bacteria and archaea, as well as in
both nuclear and mitochondrial genomes
in eukaryotes21. Moreover, beyond the 20
canonical amino acids, at least 2 additional
genetically encoded amino acids contribute
to the proteome of several extant species22
— selenocysteine (which is found in bacteria, archaea and eukaryotes as a component
of selenoproteins) and pyrrolysine (which is
mostly found in methanogenic archaea)22.
These observations show that even the
most fundamental features of the genetic
system are evolvable. What are the evolutionary forces that drive the secondary
evolution of the genetic code, and what
constraints limit its reassignment in nature?
Until recently, these issues were studied
using either theoretical or comparative
methods21. Now, reconstruction of altered
genetic codes with reassigned codons on
a genome-wide scale has emerged as a
strategy to investigate these questions.
Codon reassignment. Recent genome engineering efforts have shown the feasibility
of recoding the genetic information of an
organism while simultaneously expanding
the coding capacity of its genetic code. In
a pioneering series of studies14,15, an in vivo
genome editing approach (FIG. 1) was used to
remove all 321 TAG trinucleotides (which
encode the amber stop codon) from E. coli
DNA (by converting them to TAA, which
encodes the ochre stop codon) as well as
the release factor that recognizes them. For
this purpose, the researchers identified all
314 E. coli genes that contain TAG codons.
Reassigning hundreds of codons requires
highly efficient methods to simultaneously
modify multiple genomic locations. Multiplex
automated genome engineering (MAGE) is
well suited to rapidly edit multiple sites by
oligonucleotide-mediated allelic replacement and was used to introduce subsets of
TAG-to-TAA codon changes into 32 independent strains15. Next, partially recoded
strains that contained distinct sets of codon
modifications were merged into a single
strain using a technique called hierarchical
conjugative assembly genome engineering
(CAGE)14,15 (FIG. 1a). The elementary step
of CAGE involves the transfer of a targeted
genomic region of one strain into a second
strain through conjugation. Iterative assembly of pairs of partially recoded strains in
a hierarchical manner resulted in a single
fully recoded genome. Finally, this procedure yielded a blank codon, which was then
reassigned to encode a non-standard amino
acid. An extension of this approach showed
that numerous sense codons might also be
amenable to removal23. However, conversion
of certain codons to their synonymous counterparts was constrained in various ways23.
Failed replacements were likely to be caused
by the disruption of endogenous regulatory
mechanisms, by codon bias that affect gene
expression or by the perturbation of expression as a result of separating overlapping
genes. Nevertheless, all occurrences of 13
codons in a panel of essential genes could
eventually be changed, which indicates that
genome-wide recoding is feasible23. These
methodological developments are expected
to provide new insights into both the recent
Table 1 | The role of microbial genome engineering in evolutionary research
Engineering
strategy
Goals
Genome
editing
Tools
Recent advances and major
achievements
Evolutionary
implications
•Elucidate genotype–
•Oligonucleotide-mediated
phenotype correlation
recombineering57
•Improve the metabolic
•Engineered nucleases (ZFNs58,
efficiency and/or robustness
TALENs59 and RNA-guided
of industrial producer
nucleases60 based on the
strains
CRISPR–Cas system)
•Mobile group II introns
and Cre–loxP-mediated
recombination61
•Development of highly efficient
methods that allow multiple,
parallel and combinatorial
alterations at specific loci (MAGE13
and TRMR42)
•Improved biomolecule
production13
•Altered use of the genetic code15
•Systematic exploration
of adaptive landscapes
•Evolution of the genetic
code
•Evolutionary
optimization of
metabolic pathways
and complex subcellular
structures
Genome
reduction
•Identify minimal gene sets
•Create a simplified and
programmable cell
•λ-Red-mediated
recombineering
•Suicide plasmid
recombination55
•Bacteriophage P1
transduction
•Meganucleases55
•Targeted streamlining based
on gene essentiality and
comparative genomics31
•Organisms with reduced or
core genomes16
•Genetically stabilized cells56
•Improved production hosts16
•Evolution towards
minimal genomes
•Role of mobile genetic
elements and prophages
in evolution
De novo
genome
synthesis
•Create characterized
libraries of regulatory
elements for predictable
performance
•Assemble modular
pathways
•Synthesize microorganisms
with flexible genomes
•DNA synthesis using chemical
methods
•In vitro and in vivo DNA
assembly methods
•Genome transplantation19,62
•Cre–loxP-mediated
recombination37
•Refactoring of pathways and
genomes63
•Synthetic device libraries64
(such as promoters and
regulatory ‘switches’)
•Chemical synthesis of a full
genome62
•Synthesis of yeast chromosomes
with rearrangeable architecture37
•Combinatorial evolution
of transcriptional
regulatory networks
•Evolution of gene order
Genome
merging or
shuffling
•Create and rapidly improve
complex phenotypes by
exploiting the diversity of
variation across genomes
•Targeted recombination65
•CAGE14
•Genome mass transfer
•Protoplast fusion18
•Construction of a hybrid bacterial
genome65
•Whole-genome recoding15
•Improved producer strain18
•Origin of evolutionary
novelties
•Evolution of symbiotic
genomes
•Role of large-scale gene
transfer in evolution
CAGE, conjugative assembly genome engineering; CRISPR–Cas, clustered regularly interspaced short palindromic repeat–CRISPR-associated protein; MAGE, multiplex
automated genome engineering; TALEN, transcription activator-like effector nuclease; TRMR, trackable multiplex recombineering; ZFN, zinc-finger nuclease.
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history of genetic-code evolution and the
origin of the canonical genetic code (FIG. 1b).
It will become feasible to directly test
evolutionary scenarios for codon reassignment by constructing intermediate
code variants that are unseen in nature
and that would be too slow to evolve in
the laboratory. For example, the codon
capture hypothesis proposes that mutation
bias that affect the genomic guanine and
cytosine content can drive the extinction
of certain codons in a neutral process21. In
a second stage, a vanished codon can reappear and be recognized by a tRNA charged
with a different amino acid21. By engineering genomes with the aim of completely
eliminating a particular codon, we are now
in the position to empirically study which
codons are amenable to removal in the first
stage and to which amino acids they can
be reassigned in the second stage of codon
capture. Other experimental work suggests
that codon ambiguity is an effective mechanism that could drive alterations in the
genetic code24.
The amino acid repertoire. Not only the
structure of codon reassignments but also
the range of encoded amino acids demands
an evolutionary explanation: what evolutionary forces have acted on the size and
content of the canonical amino acid ‘alphabet’?
The facts that numerous non-standard
amino acids have been successfully engineered into natural proteins and that the
protein biosynthetic machinery can be
expanded to translate extra amino acids
in vivo indicate a lack of fundamental barriers against a markedly expanded alphabet 25. According to one hypothesis, the
amino acid repertoire was selected for its
biochemical diversity 26. However, investigating the effect of engineered alphabets on
single proteins gave controversial results:
whereas an expanded code that contained a
non-standard amino acid yielded superior
antibodies in a directed evolution experiment 27, it was also possible to create a functional enzyme using a markedly reduced
alphabet with only nine amino acids28. The
availability of genome engineering tools to
construct alternative genetic codes paves
the way to systematically probe the effect of
both expanded and reduced alphabets on
the evolvability of complete proteomes.
A recent study used an E. coli host
strain that was engineered to incorporate
3‑iodotyrosine at amber stop codons to
augment the genetic code available for an
evolving bacteriophage with a non-standard
amino acid29. Expanding the alphabet
increased the evolvability of the phage by
enabling access to a new beneficial mutation, which was only possible owing to
incorporation of 3‑iodotyrosine into a
phage protein involved in host cell lysis.
Evolution of genome size
The realization of the vast differences in
genome sizes across bacterial species promoted a growing interest in the concept
of minimal genomes. Organisms with nearly
minimal number of genes occur in nature
and are often obligate host-associated bacteria30. For example, the endosymbiotic
bacteria Buchnera spp. are relatives of E. coli.
Since the split of the two lineages 200 million years ago, the free-living ancestor lost
75% of its genome, including mobile genetic
elements30. Buchnera spp. now contain ~580
genes, which shows that such a small
number of genes is sufficient to maintain
cellular life under a constant intracellular
environment provided by the host.
The extent of observed genome reduction in the laboratory is generally small. For
example, in the bacterium Salmonella enterica, the rate of DNA loss during laboratory
evolution was 0.05–2.50 bp per chromosome per generation7. Given such a low rate,
genome engineering could be a more viable
alternative to ‘replay’ the evolution of
massive genome reduction in the laboratory.
The development of basic gene deletion
methods enabled a broad range of genome
reduction projects, which resulted in substantially smaller and increasingly stable,
streamlined bacterial genomes31 (FIG. 2a).
These studies showed that microorganisms are amenable to such large-scale gene
Glossary
Amino acid ‘alphabet’
Directed protein evolution
The set of amino acids used to build genetically
encoded proteins.
A protein engineering method to evolve proteins with
desirable properties. It mimics and accelerates natural
evolutionary processes by applying in vitro
diversification–selection–amplification cycles.
Antagonistic pleiotropy
Pleiotropy occurs when a single gene influences multiple
phenotypic traits that are seemingly unrelated. In
the case of antagonistic pleiotropy, expression of the
pleiotropic gene has mixed, competing effects; some
of these are beneficial but others are detrimental to
the organism.
Epistatic interactions
Interactions between two mutations whereby the
phenotypic effect of one mutation depends on
the presence of another mutation.
Genome editing
Codon ambiguity
An extreme form of mistranslation in which a codon
can be translated as two different amino acids.
Combinatorial explosion
A fundamental problem in evolutionary optimization
and computing. As the size of the investigated
system and the number of corresponding parameters
increase, the number of combinations that one
has to examine grows exponentially, which requires
an intolerable amount of time to examine them.
Modification of the genetic information encoded by the
genome using in vivo, directed modification (such as
replacement, removal or insertion of DNA bases) of a single
locus or multiple loci. It uses synthetic oligonucleotides and
a range of accessory tools, including engineered nucleases,
and DNA repair and recombination enzymes.
Multiplex automated genome engineering
(MAGE). A highly efficient genome editing method that can
generate a large and heterogeneous population of mutant
bacterial genomes within days. Using oligonucleotidemediated allelic replacement technology in a cyclic and
automated manner, MAGE can simultaneously target
and modify multiple genomic locations across a large
population of cells.
Site-specific recombineering
A recombination engineering system that allows efficient
manipulation of genomic DNA at predetermined locations.
It does not require extensive sequence similarity and relies
on site-specific recombinases that catalyse reciprocal
recombination of DNA at short sequences.
Leading DNA strand
The strand of nascent DNA that is being ‘read’ by the DNA
polymerase in the same direction as the replication fork
proceeds. It is being synthesized continuously, as opposed
to the lagging strand.
Convergent evolution
Evolution of similar phenotypes in different
populations or species as a result of adaptation
to similar environments or ecological niches.
Reduction towards a minimal essential gene set can occur
either naturally (for example, in symbionts) or by genetic
engineering.
Minimal genomes
Genomes that carry only the minimal genetic information
necessary for life in a given environmental condition.
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Synthetic chromosome
An artificial chromosome synthesized from simple chemical
building blocks. Owing to limitations in the length of DNA
that is amenable to direct chemical synthesis, construction
of synthetic chromosomes is a hierarchical process, in
which synthetic oligonucleotides are assembled into larger
DNA segments in a step-wise manner using in vitro and
in vivo assembly methods.
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deletions, and that a large proportion of
the genome can be deleted without any
major growth defects. However, the effects
of genome reduction on the transcriptomic
profile, metabolic capacity and stress tolerance across a range of relevant conditions
are mostly unknown, and these should be
the subject of further studies.
Microorganisms with streamlined
genomes can also be used to elucidate some
basic processes in evolution. For example, the E. coli laboratory strain MDS42
was designed for the elimination of most
horizontally derived genomic islands, and
deletions that amounted to 15.3% of the
genome did not interfere with beneficial
growth characteristics16. These results are
consistent with prior arguments suggesting
that horizontally derived genes only have
important roles under special environmental
settings32. Furthermore, as all transposable
genetic elements were removed from E. coli
MDS42 (REF. 16), this strain is especially well
suited to study the role of these elements
a
A
Wild-type
genome
B
Genome
Non-essential genomic
segments to be deleted
E. coli
Synthetic
fragment
Marked, parallel deletions by λ-Red recombineering
and positive selection
Fitness check
λ-Red-mediated
integration and
positive selection
PM and
NM
A
PM
NM
B
Marker removal and
negative selection
A
B
Transduction and
positive selection
Synthetic fragment
λ-Red-mediated
integration and
negative selection
Marker removal and
negative selection
A
Marker removal and negative selection
Reduced
genome
b
Non-directional
IoxP sites
Gene
Cre
Synthetic
genome
Deletions, inversions and translocations
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B
Figure 2 | Examples of large-scale genome
architecture restructuring. a | Systematic
reduction of the Escherichia coli genome generates simplified cells. Non-essential genomic
segments are individually deleted using λ-Red
recombineering, tested for fitness and then
transferred sequentially to the final acceptor
strain by bacteriophage P1 transduction. A and
B represent arbitrary genomic segments. PM
and NM are positive and negative selection
markers, respectively. Black dashed lines
indicate the sites of ‘scarless’ deletions.
b | Synthetic chromosome rearrangement and
modification by loxP-mediated evolution
(SCRaMbLE) generates complex genotypes and
a broad range of phenotypes by massive
restructuring of the yeast genome. Transient
induction of the Cre recombinase causes
recombination between loxP sites inserted in
the synthetic genome.
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Mutant library screen
In silico modelling
Laboratory evolution and
whole-genome sequencing
Serial transfer
E. coli
∆gene B
∆gene E
∆gene A
∆gene C
∆gene D
Identification of relevant gene set
MAGE
Combinatorial mutation library
Figure 3 | Optimization of complex phenotypic traits by identifying
relevant genes and by searching for optimal combinations of mutations within these genes. In the first step, genetic loci that contribute to
the desired phenotype are identified by screening genome-wide mutant
libraries (left panel), by systems-biology modelling of network perturbations43 (middle panel) or by laboratory evolution followed by whole-genome
sequencing (right panel). Computational models in systems biology enable
the performance of large cellular subsystems to be predicted with clear links
during evolution and the implications of
their absence from several ancient endo­
symbionts. Interestingly, genetic adaptation
to a toxic plasmid was delayed in E. coli
MDS42 (REF. 33). The issue of evolvability was
further investigated by reinserting a single,
highly active insertion sequence element
IS1 into the E. coli MDS42 genome that was
devoid of all other mobile genetic elements34.
Subsequent laboratory evolution experiments
revealed that insertion sequence elements
increase mutational supply and occasionally generate variants with especially large
phenotypic effects, but these elements have
a smaller impact on adaptive evolution than
other mutation-promoting mechanisms34.
to changes in the dosage of individual genes in the network. Laboratory
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evolution identifies adaptive mutations that accumulated during selection
for the phenotype of interest. In the second step, combinatorial mutation
libraries are constructed by targeted modifications (represented by coloured boxes) of the genes identified in the first step using multiplex recombineering methods, such as multiplex automated genome engineering
(MAGE). Finally, the resulting libraries can be selected to identify superior
combinations of mutations. E. coli, Escherichia coli.
Future studies should aim to develop
automated genome reduction technologies, which would enable experiments to be
carried out in larger scales and with higher
precision and speed. With such techniques
in hand, researchers would be able to test
several crucial evolutionary issues. For
example, computational models indicate
that several different minimal genomes have
identical fitness in environmental settings35.
Hence, differences in gene content between
intracellular bacteria may partially reflect
alternative solutions to reach the same goal
rather than lineage-specific adaptations35.
It would be exceptionally interesting to
investigate this theory under controlled
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laboratory conditions, not least because
it has important implications for the role
of historical contingencies in genome
evolution.
Evolution of genome architecture
It is increasingly evident that not only the
genome content but also the large-scale
structural organization of the genome is
influenced by selection in bacteria, archaea
and eukaryotes alike20,36. Several genomic
features show nonrandom patterns and
strong signs of conservation across species.
For example, co‑expressed genes tend to
be clustered in eukaryotic genomes and
remain linked more than expected in
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related species on the basis of a neutral null
model36. Similarly, selection for increased
co‑regulation has shaped both the operonic
organization of functionally associated
genes and the ‘uberoperonic’ contiguity
between related operons in bacteria and
archaea20. However, the potential adaptive
values of other aspects of genome organization have remained more elusive. For example, it remains unclear why essential genes
are preferentially located on the leading DNA
strand in bacteria20. It is also uncertain why
eukaryotes show great diversity in the number, size and shape of their chromosomes,
and why they differ in intron content. As
intra-population natural variation in such
genomic traits is either often scarce or
associated with further genetic differences
between individuals, it is generally difficult
to directly investigate the fitness effect of
genome rearrangements. The development
of powerful experimental tools to rearrange
microbial genomes — such as site-specific
recombineering, and synthetic chromosome
rearrangement and modification by loxPmediated evolution (SCRaMbLE) — holds
promise to shed new light on the evolution
of genome architecture37.
Site-specific recombineering. A recent study
used site-specific recombineering to alter the
genome architecture of fission yeast without
changing its coding sequence38. A panel of
ten engineered strains, each containing a
particular chromosomal inversion or translocation, was measured for meiotic viability
and for mitotic fitness under various environmental conditions. Although altered
chromosome structures had a dele­terious
effect during sexual reproduction, some
strains showed a growth advantage during
mitosis, which indicates the presence of
antagonistic pleiotropy. This finding could
potentially provide an explanation for the
way that variation in chromosome structure
is maintained in natural populations.
Synthetic chromosome rearrangement and
modification by loxP-mediated evolution.
The combinatorial mutagenesis method
SCRaMbLE was developed in budding
yeast and is especially well suited for
large-scale restructuring of the eukaryotic
genome37,39. This method relies on constructing a synthetic chromosome on the
basis of the directed modification of the
native sequence (FIG. 2b). Most notably, by
inserting loxP sites after the stop codons
of each non-essential gene and at major
genomic landmarks, the synthetic chromosome includes a Cre recombinase-inducible
evolution system, which allows the formation of many structurally distinct genomes
on demand. In a pioneering study, the
feasibility of this approach was shown by
the construction of two synthetic chromosome arms in budding yeast and the generation of substantial genetic and phenotypic
heterogeneity by inducing site-specific
recombination in these strains37. In principle, alterations in chromosome number,
structure, ploidy and content can all be
achieved using SCRaMbLE, which makes
it a promising tool for mapping the fitness
landscape of eukaryotic genome architecture. Furthermore, the recent construction
of a fully synthetic Saccharomyces cerevisiae
chromosome III that is devoid of introns
shows that introns on this chromosome do
not contribute substantially to fitness39.
Future directions
Several factors will influence the success of future applications. The power of
genome engineering is mostly exemplified
by studies that concentrate on the design
of relatively simple or small-scale genetic
manipulations. Moreover, the scale of generated variants is generally modest. These
limitations will soon be overcome with the
rapid technical advancement of the field
(TABLE 1). Why is this important for future
evolution studies? Identifying the forces
by which complex cellular features (such
as linear metabolic pathways or multi­
meric protein complexes) emerge is one
of the major problems of evolutionary cell
biology 40. Many such complex adaptations
require the simultaneous acquisition of
multiple, specific and rare mutations in a
single lineage, all of which have little or no
beneficial effects individually 40. Thus, the
time needed for the establishment of such
adaptations is expected to be very long in
both natural and laboratory settings. MAGE
could potentially overcome this problem13,
as it can generate >4.3 billion combinatorial
genomic variants per day at selected loci,
thus accelerating the laboratory evolution
of such complex adaptations (FIG. 1a). A
technical challenge associated with MAGE
and related genome editing protocols is
that their efficiency is greatly enhanced by
the inactivation of the endogenous methyldirected mismatch repair system, which in
turn leads to a markedly increased genomic
mutation rate and the consequent accumulation of undesired off-target mutations15.
For example, in one study, 355 fortuitous
mutations were detected in addition to the
321 modifications that were actually being
targeted15. Clearly, more precise genome
editing approaches are required to achieve
increased genome stability during engineering. Multiple strategies have been offered
to ameliorate this problem, including the
transient suppression of DNA repair during mismatch-carrying oligonucleotide
integration41.
Further advances in the field could
permit more efficient optimization of
complex traits that are important for biotechnological applications. However, the
Box 1 | Experimental perturbation of genetic circuit architecture
One of the most striking discoveries of comparative genomics has perhaps been the high
versatility of gene regulatory networks across related bacterial species47. Specifically,
transcription factors are typically less conserved than their target genes and evolve
independently of each other. Moreover, despite extensive changes in regulatory mechanisms, the logical output of the overall circuits frequently remains. Why is a specific network structure
preferred for a given cellular task when alternative circuits could potentially deliver identical
outcomes? In a seminal paper, the researchers constructed 598 recombinations of promoters
(including regulatory regions) with different regulatory genes in Escherichia coli49. Strikingly, 95%
of the new links were neutral or even beneficial under certain stressful conditions. Another study
suggested that, although the population-level behaviour of many alternative circuits is similar,
they show large differences across individual cells. In the case of the Bacillus subtilis circuit that
regulates differentiation into the competent state, natural evolution specifically selected the
circuit with larger output noise50.
Engineering of protein networks can also reveal the relative importance of different mutational
mechanisms during evolution. For example, evolution of signalling pathways may, in principle,
proceed through multiple genetic mechanisms, including point mutations, duplications and
recombination of protein modules. To estimate the potential importance of the last of these
forces, the phenotypic diversity of a signalling response that results from domain recombination
was analysed52. The investigators selected 11 proteins in the yeast mating pathway and
constructed a library of 66 chimeric domain recombinants. Novel linkages between pre-existing
domains had a major impact on signalling phenotype. At least under laboratory conditions,
recombination of protein domains led to strains that mate more efficiently than wild-type strains.
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PERSPECTIVES
Box 2 | Impact of evolution on the design and maintenance of synthetic circuits
Combining directed evolution and synthetic design
The construction of artificial genetic circuits generally requires ‘fine-tuning’ for proper
function, as the biological parts are frequently ill-defined or incompatible with each other51.
This is especially problematic in the case of engineered or hybrid promoters. As a consequence,
the initial circuit constructs show unpredictable behaviour or undesired cell‑to‑cell variability,
which demands optimization of many ill-defined parameters (for example, transcription factor
binding affinities). This optimization is frequently achieved through a slow process of trial and
error. A promising complementary approach is to subject the elements that constitute the
genetic circuit to directed evolution53. To achieve this, the desired output of the circuit needs to
be screened and selected in a high-throughput manner, and the accumulation of mutations
needs to gradually improve circuit performance. If the number of elements in the circuit is fairly
large, and/or if the researcher plans to consider combinations of selected mutations only, then
standard protocols of genome editing are especially suitable for generating variation on which
laboratory selection can act.
Reducing evolvability for maintenance of synthetic devices
Another practical problem is that artificial circuits frequently impose a fitness cost on the host
organism. This burden is often due to energy costs related to synthesis of the elements that are unnecessary for host survival and to interference of the construct with native cellular
processes54. As inactivating mutations are favourable for the host, spontaneous evolution
rapidly breaks the function of synthetic constructs. Indeed, prior studies indicate that the
reliability of simple synthetic devices lasts only for 100 microbial generations55. Multiple
strategies have been suggested to resolve this problem54, including reduction of host mutation
rate33,56, application of elements that are less prone to deactivation and increasing mutational
robustness of the circuit (for example, through increasing copy number of key elements). These
approaches may reduce the speed by which the constructs are inactivated, but they are
unlikely to offer a global solution, as they do not completely eliminate the fitness advantage of
inactivating mutations. We suspect that the most reliable solution will rely on functional
coupling of the output of the synthetic construct with an essential cellular function54.
problem of combinatorial explosion remains
an important issue; finding a desired function by very rare mutational combinations
requires not only the availability of large
mutant libraries but also efficient search
strategies to navigate the genotypic space,
such as those borrowed from the field of
directed evolution8. A framework has been
proposed12,42 that includes steps of generating diversity throughout the genome,
ranking relevant genetic modifications and
combinatorial optimization on selected
loci (FIG. 3). The extent of epistatic relationships among the targeted genes has a key
influence on this search strategy. There
are at least two potential complementary
approaches to identify the gene set that
is relevant for subsequent optimization.
As epistatic interactions can be predicted
using detailed systems-biology modelling 43, future studies should attempt to
extend this framework by computational
modelling of the cellular subsystem studied. Alternatively, researchers could use
standard protocols of microbial laboratory
evolution coupled with whole-genome
sequencing to identify adaptive mutations
that accumulated during selection for the
phenotype of interest (FIG. 3).
Another serious limitation of genome
engineering is that most protocols are
currently applicable to only a few microbial
species, most of which are laboratory model
organisms10. One major issue is the variety
of DNA repair mechanisms present in other
species, which could potentially render
genome engineering inefficient in other
organisms. The extension of the applicability of, for example, genome editing protocols to a wide range of microbial species
would enable researchers to systematically
investigate and compare the effect of specific mutations and their combinations
across species. Such comparative analyses
of mutational effects may illuminate several
unresolved issues, such as the mechanisms
that drive variation in genetic pleiotropy
across taxa44.
With these conceptual and technical
advancements in hand, we expect major
breakthroughs in various areas.
Reconstruction of ancestral subcellular
subsystems. Ancestral protein and even
large-scale genomic sequences can be
inferred using phylogenetic methods.
Reconstruction of these ancestral sequences
through gene synthesis followed by integration into native genomes allows functional
characterization. Successful examples so far
include the reconstruction of enzymes45,
highly conserved proteins46 and protein
8 | ADVANCE ONLINE PUBLICATION
complexes47. Among others, these studies delivered insights into the ecological
niches of ancestral species48 and the mechanisms underlying evolutionary innovations through gene duplication46. The next
steps will be to use genome engineering to
reconstruct larger cellular subsystems of
ancestral species with the aim of rendering
phenotypes that depend on the interplay
of multiple genes and to investigate the
emergence of complex pathways. This can
be achieved in two fundamentally different
ways. The genome of an existing organism
could be edited at specific loci using multiplex recombineering or related techniques.
However, if the ancestral and the edited
genomes are substantially different (that
is, if the number of loci that must be targeted is large), then this approach becomes
extremely tedious. In such cases, complete
de novo synthesis of the ancestral genome is
expected to be a more viable strategy.
Exploring the space of alternative genetic
circuits. A series of studies indicated that
alternative gene regulatory circuits can have
similar logical outcome, albeit not necessarily identical performance49,50 (BOX 1).
Combining network design with laboratory
evolution experiments could potentially
elucidate the extent to which different
topologies can be ‘fine-tuned’, which would
provide insights into why specific network
structures have been preferred during
evolution. In the future, establishment of
libraries of regulatory ‘switches’, promoters and other standard biological parts will
allow high-throughput automated design
and laboratory selection of such circuits.
Conclusions
The examples above show that evolutionary
biology can greatly benefit from concepts
and methods of genome engineering.
However, the reverse is also true: evolutionary thinking can feedback on engineering
principles (BOX 2). We emphasize that the
roles of genome engineering and laboratory
evolution in elucidating difficult evolutionary problems should be complementary to
each other. By constructing rare genomic
alterations or specific combinations of
mutations, genome engineering could facilitate complex alterations of cellular phenotypes, which could later be fine-tuned by
standard protocols of laboratory evolution.
Combination of rational and evolutionary design strategies is important both for
understanding natural systems and for the
construction of genetic regulatory circuits
for biotechnological purposes51.
www.nature.com/reviews/genetics
© 2014 Macmillan Publishers Limited. All rights reserved
PERSPECTIVES
Csaba Pál, Balázs Papp and György Pósfai are at
the Synthetic and Systems Biology Unit,
Institute of Biochemistry, Biological Research
Centre of the Hungarian Academy of Sciences,
Szeged, H-6726, Hungary.
Correspondence to C.P.
e-mail: [email protected]
doi:10.1038/nrg3746
Published online 28 May 2014
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Acknowledgements
The authors thank the anonymous reviewers for suggestions
on the manuscript. C.P. and B.P. thank the Wellcome Trust
and the Lendulet Programme of the Hungarian Academy of
Sciences for supporting this work; G.P. thanks the Hungarian
Research Council (OTKA) for supporting this work. B. Kintses,
A. Nyerges and B. Csorgo gave comments on an earlier
version of the manuscript.
Competing interests statement
The authors declare no competing interests.
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