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Critically assess recent progress in the refactoring of bacterial gene

clusters for synthetic biology applications

Abstract

Genes in the same biosynthetic pathway are often found clustered together

in regions of prokaryotic DNA known as biosynthetic gene clusters (BGCs).

These can either be active or silent (cryptic). Genetic refactoring, a technique

developed over the last decade, has been used for both types of BGC.

Refactoring is the process of removing non-essential sequences and re-

organising only the essential genes and regions of a gene cluster, placing

those genes in a defined and inducible regulatory circuit and then

transplanting the cluster into another organism. It has been used on active

clusters to improve the efficiency of biosynthesis of secondary metabolites for

industry, biotechnology and pharmaceuticals. It has also led to a wealth of

research into developing a standardised approach to refactoring silent

clusters to become inducible and active in order to discover and characterise

novel metabolites. Genetic refactoring can be thought of as the paradigm of

synthetic biology, incorporating new ideas and technology from software

design/programming for BGC discovery and refactoring design, to DNA

synthesis and manipulation for the construction of refactored clusters.

Despite limitations, such as the number of host organisms in which the

technique is possible and gaps in the knowledge of gene regulation in many

systems under study, gene cluster refactoring is emerging as a technique of

great potential. Research into systems level characterisation and

standardisation of biological parts, and integration of these into databases,

e.g. promoter libraries, is paving the way for refactoring to become a

standard biotechnological tool.

Word Count: 244

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What is prokaryotic gene cluster refactoring?

Genetic refactoring in bacteria is a relatively new technique, even by the

standards of synthetic biology, the current poster child for the biosciences.

Less of a technique and more a conceptual philosophy which has now

become practical due to technological advances, genetic refactoring is a

direct result of the successes and paradigm shifts of systems biology and

rational bioengineering (1). Refactoring is a term borrowed from software

design, in particular modular programming. Essentially, it is the removal of

the redundant elements of a section of code (in this case DNA) and the

reorganisation of characterised modules (in this case genes and regulatory

elements) into a more logical, coherent and more easily manipulated

structure, whilst retaining the original function (2). A ‘synthetic controller’

sequence encoding the inducible regulatory elements is also designed and

placed in the same construct (3).

Building on previous developments in Synthetic Biology, such as pioneering

work on whole bacteriophage genome synthesis (4), genome refactoring in

viruses was developed in the 2000s (5) and has only really been achieved in

bacterial gene clusters since 2010. Therefore recent progress is taken to

mean post-2010. It could be said that the research and developments of

gene cluster refactoring fall into two categories; firstly the ‘plug-and-play’

methods which are used for the awakening of cryptic gene clusters for the

discovery of novel secondary metabolites (6–8). Secondly, methods which

are used for the re-organisation of already characterised biosynthetic gene

clusters to increase the yield of metabolite production, efficiency of

biocatalysis and the ease of manipulation (3,9–12). Despite the successes in

both of these methods, they also have numerous limitations. Specifically in

the knowledge available regarding the systems under research, the number

of different systems available to work in and the tools necessary to achieve

certain refactoring goals.

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Ultimately however, bacterial gene cluster refactoring is almost too new and

emerging a technique to be subjected to an overly critical analysis, as its full

potential may have yet to be actualised. The pioneering early work discussed

here may represent just the beginning of the ‘“mixed-and-matched”...designer

organism[s]’ as predicted by Fischbach and Voigt (13); the end of the ‘hype

phase’ of synthetic biology and the dawning of the ‘real phase’ (14).

What are the aims and intentions of the technique?

The first example of refactoring the DNA of any organism was the refactoring

of the Bacteriophage T7 genome in 2005 (15). This was an attempt to

redesign an organism’s genome in order to make it simpler, more structured

and easier to manipulate by removing redundant and overlapping

evolutionary relic sequences. The researchers argued that although evolution

may have created organisms of great complexity, efficiency and adaptation,

the naturally random mechanism of mutation, which leads to the

accumulation of functionally redundant genomic elements, means that

natural genomes are not easily decoded or manipulated.

This view is also supported by other research into genomic transcriptional

regulatory networks (GTRNs). Computational modelling of the Escherichia

Coli GTRN, suggests that evolutionarily derived genomes are not necessarily

selected for their robustness against environmental fluctuations (16). They

argue that the deterministic ordinary differential equation (ODE) models of

GTRNs for various organisms can be simplified. They achieved this by

merging multiple regulatory operons into one; and re-wiring the network, by

reducing the number of regulatory interactions, resulting in the same gene

expression profile but with greater long-term robustness. Thus it would

appear that human-mediated, rationally designed genomes may well have

benefits over purely natural, evolutionarily derived ones. This, coupled with

more practical advances in DNA synthesis and ligation technology, such as

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Gibson Isothermal assembly (4) and DNA assembler (17), have enabled

rapid progress in increasing the feasibility of synthesising larger sequences

of DNA into transferable constructs. Progress in the top-down approach to

the characterising of gene networks and regulation in systems biology has

led to a greater abstraction and understanding of the general motifs within

biological systems. These can now be exploited, in a novel ‘bottom-up’

fashion, by rearranging DNA sequences to enhance specific functions of

certain prokaryotes (18).

It is now largely recognised that gene clusters are the genetic building blocks

for evolution in prokaryotes (13) and therefore often contain most, if not all of

the genes necessary for specific functions such as biosynthetic pathways.

This means that they can often be functionally substituted between

organisms, in a process which mirrors horizontal gene transfer (19). This

does not however mean that these clusters will continue to operate in an

optimized and efficient manner due to the various interactions with the host

cells regulatory networks and machinery. Thus, one of the necessary aims of

gene cluster refactoring was to overcome the intrinsic cis-regulatory elements

of the clusters by altering the codon usage, but retain the amino acid

sequence (6). This relies on codon degeneracy. Another way of avoiding

cross talk with the host cell’s regulatory networks is by using orthogonal

regulatory circuits in the refactored cluster. For example, engineering the

cluster to rely on ‘controller’ encoded T7 or T3 phage polymerases rather

than the host cell’s (3,6). This line of research stresses the notion of ‘plug-

and-play’ synthetic biology and attempts to devise a formalised strategy to

gene cluster refactoring in the hope that a standard protocol will be created

that can be applied to any gene cluster.

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What are the achievements of the technique so far?

As mentioned, there are two main areas of interest in gene cluster refactoring

research. Firstly the aim to increase bacterial biosynthetic efficiency and

secondly the awakening of silent (cryptic) biosynthetic gene clusters to

discover novel and potentially useful secondary metabolites e.g. antibiotics

(20). The successes of this first area have various applications in

biotechnology and highlight broadly applicable issues which may apply to any

gene cluster undergoing refactoring. One study involved the refactoring of the

NAD+ regenerating gene cluster in E. Coli, in order to generate high (83.4%)

molar yield butyric acid from glucose (11). This is used as a feedstock in

Figure 1. An example of a very general conceptual protocol for gene cluster

refactoring, providing the step by step instructions from basic sequence analysis

to synthesis of the refactored cluster. Diagram taken from ‘The Science and

Applications of Synthetic and Systems Biology Workshop Summary’ (2)

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chemical synthesis and biofuel production. An achievement of this example

was the realisation that, when refactoring to increase biosynthesis of a

particular product, it is not enough to only optimise the pathway of interest.

Other pathways competing for the same chemical precursor must also be

disrupted in order to prevent flux away from the desired end product.

Naturally this need for disruption must be weighed against the necessity of

competing pathways for the survival and growth of the bacteria. If they are

unnecessary for survival, then these pathways can be disrupted by gene

deletions, as in this example. Two genes were deleted as they encoded

proteins which convert the butyrate precursor, acetyl-CoA and its derivatives

into non-essential acetoacetate and acetate. These and other similar

changes enabled near stoichiometric redox balancing of the butyrate

pathway (11).

One of the first examples of gene cluster refactoring involved the nitrogen

fixation cluster from Klebsiella oxytoca (3) which fixes atmospheric N2 into

NH3 (ammonia). An important achievement in this study was the decoupling

of native regulation of the nitrogenase by NH3 which would normally act as a

repressor in a negative feedback loop. Although the wild type strain showed

no nitrogenase activity in the presence of 17mM NH3, the refactored strain

maintained activity (3).

Other successes include the increased efficiency of the biosynthesis of fine

metabolites such as pharmaceutical agents. A noteworthy example is the

refactoring of the polyketide A-74528 (an antiviral compound) biosynthetic

cluster (10). Researchers were able, by gene deletion, to successfully

repress the synthesis of other unwanted products (fredericamycin) forming

from the same cluster to yield 3 mg/L of A-74528 in Streptomyces coelicolor

compared to ‘minute’ quantities in the WT organism (10). Gene cluster

refactoring has also been shown to have potential uses for biosensor

applications and bioremediation of toxic waste products from industry, e.g.

caffeine (12).

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The successes in the second area, awakening cryptic clusters, are more

novel, and therefore dominate the literature. Research remains challenging

as most cryptic metabolite pathways are evolutionary designed to be tightly

regulated and are only activated under very specific conditions due to their

high energetic cost. Awakening silent gene clusters is a significant advance,

enabling discovery of previously unknown or difficult to express secondary

metabolites under unknown regulation (7). Previous methods have been

based on trial and error growth medium optimization experiments and were

extremely laborious. New ‘plug-and-play’ methods rely on refactoring to

remove the internal regulation of the cluster. Each gene of interest is inserted

into an operon with each gene under the control of particular characterised

and insulated promoters, under inducible activation by known and available

substances (6). It is important to stoichiometrically balance the expression

levels of genes within the biosynthetic pathway and consequently libraries of

promoters, and RBS (Ribosome Binding Sites), with determined expression

efficiencies are being built for current and future reference (7).

An important aspect of this research is the discovery of silent biosynthetic

clusters in sequenced genomes. These must be identified and characterised

before they can be refactored. Various bioinformatic techniques exist for

identifying novel clusters by genome mining, including the software

antiSMASH (21), and there now exist numerous general protocols describing

the entire process of cryptic secondary metabolite discovery and production

(22).

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Significant secondary metabolites discovered using this method include 12

clusters encoding pathways for the biosynthesis of several diverse

tryptophan dimers (9); a new antibiotic compound (taromycin A) (20); and the

compound spectinabilin (7).

A significant limitation to the early progress of synthetic DNA biology was the

difficulty in assembling fragments large enough to contain gene clusters (23).

Advances in DNA ligation technology coupled with computational advances

in DNA sequence design have contributed immensely towards the feasibility

of gene cluster refactoring. Techniques such as transformation-associated

Figure 2. Semi-schematic diagram of the discovery and characterisation

process of novel BGCs and their product secondary metabolites. Diagram

taken from ‘Genome-based bioprospecting of microbes for new therapeutics’

(22).

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recombination (TAR) cloning in yeast now allow complete capture of whole

gene clusters of at least 67kb (20). Previously, using the laborious

cosmid/fosmid approach, fragments of only around 30-40kb would have been

captured and would have to be stitched together (20). Another in vivo, yeast

based method which allows significant manipulation of the sequence and

pathway under study is ‘DNA Assembler’ (17).

Computational tools for aiding synthetic biology and cluster refactoring are

focused on two aspects. The first, on developing standardised biological

parts databases such as the BioBrick parts of the MIT Registry of Standard

Biological Parts1. Also, the promotion and development of the use of

standardised coding languages such as the System Biology Markup

Language (SBML) (24). This is the area where computer science and

synthetic biology meet and inform each other, and many new programs and

languages designed to allow more modular programming are being

developed. These include programming languages such as Kera (25), in

which the user can add new functions, therefore enabling an ever-evolving

rule library. The second focus of computational synthetic biology is more

practical and is based around programmes which enable easier DNA

sequence assembly for refactored clusters such as Gene Designer 2.0 (26).

What are the current limitations of the technique?

Despite the successes in prokaryotic gene cluster refactoring, there remain

considerable drawbacks in the knowledge informing it and the technology

available. One such issue is that the technique is seriously limited to only a

few known model prokaryotic organisms and the transfer of clusters can only

occur between similar organisms with a characterised set of promoters (7).

Currently there is not enough sequence and regulatory information available

1 http://parts.igem.org

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for the technique to be used in many bacteria with more potentially useful

secondary metabolites including photosynthetic bacteria (26).

One of the major limitations is in the understanding of the regulatory

mechanisms of certain genes. When refactoring a cluster, the intrinsic gene

regulation is intended to be removed; however there is the potential for other

unknown regulatory mechanisms to remain unchanged. The approach taken

by most researchers thus far to ensure the potential effects of these unknown

elements are limited, is the use of strong promoters upstream of the gene

(2,7,11,20). The hope being that even if uncharacterised upstream or

downstream elements remain in the sequence, the strength of the inserted

promoter will prevail with negligible interference (7). This is a heavy handed

approach relying on the force of strong promoters to ensure high gene

expression and does little in the way of modelling natural regulatory subtlety.

This appears successful for studies where high levels of production of an

output are required, however may not always be appropriate for the intended

purpose. There is also a risk that by refactoring highly evolved systems the

efficiency of biosynthesis may be reduced. This was the case in the

refactored nitrogen fixation cluster as the nitrogenase activity in the

refactored organism was reduced to 7.4% of the wild-type and its growth rate

slowed 3.5-fold (3).

Although refactoring has been able to activate cryptic clusters to discover

novel metabolites, more research is needed to increase the efficiency of

desired metabolites over shunt metabolites. This will require more pathway

optimisation and stoichiometric flux balancing (10). Another issue when

transplanting refactored clusters into different host cells is the potential lack

of necessary biosynthetic precursor molecules produced by the host. This

would mean that genes encoding precursor synthesising enzymes as well as

secondary metabolite synthesising enzymes would need to be included in the

refactored cluster, adding to its size and complexity (7). It may also be the

case that not all functionally relevant genes are present in the cluster. If for

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example, a species-specific protein such as Glutathione S-Transferase

(GST) or a chaperone is necessary for some function as in the case of the

caffeine operon (12). However, if the necessary co-protein is known and

sequenced, it may be possible to engineer into the cluster construct. Another

practical limitation surrounds the fact that Saccharomyces cerevisiae based

in vivo recombination methods are currently the most popular for the

synthesis of refactored clusters. This, however, may not be viable in the

refactoring of clusters which contain domain-repetitive proteins involved in

PKS (polyketide synthase) and NRPS (nonribosomal peptide-synthetase)

pathways. These may suffer from deletions through yeast homologous

recombination events (6).

In addition to these practical issues, an important conceptual limitation in the

synthetic biology design principle is the integration of noise and non-linear

dynamics, now prevalent in descriptions of some gene circuits. Whilst

conventional engineering aims to reduce noise, some biological systems are

shown to intrinsically depend on noise for their function (27) and this could

pose challenges to the notion of deterministic genomic engineering.

Stochastic in silico simulations of designed/refactored gene circuits/clusters

could prove more important as the pathways undergoing manipulation

increase in complexity (28).

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Future aims and potential for gene cluster refactoring

The successes of gene cluster refactoring in prokaryotes thus far, are a small

but significant step in an area of research which will undoubtedly increase

and yield rewards exponentially in the coming decade. It is apparent that

genetic refactoring is a holistic and cross-disciplinary technique and as such,

the integration of research from computer science, molecular biology and

genetics is paramount. The future aims for the technique already recognised,

include the hope to increase the diversity of gene clusters and natural

products derived from them by improving genome mining (6). The

Figure 3. An example of the abstraction hierarchy of biological components and

their construction into translation/transcription circuits for in silico stochastic

simulations. The example shown here is of the Elowitz-Leibler Repressilator.

Symbols are taken from the MIT Registry of Standard Biological Parts. Diagram

taken from the article ‘Computational design tools for synthetic biology’ (24).

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development of algorithms which assess the diversity of gene clusters and

their derived organisms multi-dimensionally, to determine which most

strongly complement those already known, could rapidly expand BGC

discovery. There is also an emphasis upon the importance of the merging of

different bioinformatic tools into one program which would allow greater ease

of co-ordination between genome analysis and gene cluster/circuit design

(26). A current example of this approach is the software TinkerCell (29).

It has also been suggested that in order to bypass current limitations in

rationally designing refactored BGCs, particularly PKS pathways, it may be

necessary to use more classical genetic engineering approaches in tangent.

In order to create a high throughput approach to novel metabolite discovery

and creation, more random variant generating techniques relying on

evolutionary recombination of complex BGCs, such as directed evolution

may be required (30). It may yet be necessary to rely on natural biological

phenomena to evolve designed clusters to their optimal state.

Finally, the progress of gene cluster refactoring in prokaryotes must be

weighed against the practical safety considerations of their use. The issues

revolve around horizontal gene cluster transfer from engineered organisms to

wild type organisms in the wild. This could be problematic if, for example, the

cluster transferred contained a highly efficient antibiotic synthesis pathway.

Large quantities of potentially novel antibiotics could be released, enabling

potential pathogens to evolve mechanisms of resistance faster due to

repeated exposure. Numerous existing bio-safety methods include toxin-

antitoxin pairs to ensure engineered clusters only work in intended hosts and

‘DNA watermarks’ to trace engineered DNA (31). More recent methods under

development include, as mentioned, orthogonality, whereby the codon usage

in a refactored cluster is altered so as to be unreadable by natural organisms;

and evolved ribosomes capable of binding to non-natural binding sites which

natural ribosomes cannot (31). These methods decrease the likelihood of

synthetic-to-natural organism genetic transfer, however, are still vulnerable to

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the ability of natural organisms to evolve a mechanism to exploit synthetic

systems. A promising and important development in biosafety already

applied is that of forced organism auxotrophy. In the biosensor system for

caffeine (12), the host E.coli were designed to be xanthine (the caffeine

degradation product and guanine precursor) auxotrophs. If the essential

compound is naturally scarce, then the organism’s chance of survival outside

its intended setting is reduced.

Despite these limitations, the future potentials of refactoring for synthetic

biology applications are considerable. These range from the use of

synthetically refactored bacteria for live vaccines (32) and spider silk

production (33), to the refactoring of yeast and other eukaryotic systems,

even the refactoring of entire chromosomes and genomes (34).

Word Count: 2,951

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