Rational design for over-production of desirable microbial metabolites by precision engineering

                                         ORIGINAL PAPER

Rational design for over-production of desirable microbial

metabolites by precision engineering

Hong Gao • Xianlong Zhou • Zhongxuan Gou •

Ying Zhuo • Chengzhang Fu • Mei Liu •

Fuhang Song • Elizabeth Ashforth • Lixin Zhang

Received: 6 December 2009 / Accepted: 1 April 2010

 Springer Science+Business Media B.V. 2010

Abstract Microbes represent a valuable source of

commercially significant natural products that have

improved our quality of life. Precision engineering

can be used to precisely identify and specifically

modify genes responsible for production of natural

products and improve this production or modify the

genes creating products that would not otherwise be

produced. There have been several success stories

concerning the manipulation of regulatory genes,

pathways, and genomes to increase the productivity

of industrial microbes. This review will focus on the

strategies and integrated approaches for precisely

deciphering regulatory genes by various modern

techniques. The applications of precision engineering

in rational strain improvement also shed light on the

biology of natural microbial systems.

Keywords Precision engineering 

Regulatory gene  Strain improvement

Abbreviations

pre-NDA pre-New drug application

MFA Metabolic flux analysis

SAM S-adenosylmethionine

CoA Coenzyme A

1,3-PD 1,3-Propanediol

ALDH Aldehyde dehydrogenase

DCA Dicarboxylic acid

TCA Tricarboxylic acid

HMG-CoA 3-hydroxy-3-methyl-glutarylcoenzyme

A

ADS Amorphadiene synthase

tHMGR HMG-CoA reductase

CEF control effective flux

PCA Principal component analysis

Introduction

Microbes are used in biotechnology industries to

produce a wide variety of diverse compounds, which

are important in chemical, food, pharmaceutical and

health care fields (Davies 2009; Newman and Cragg

2007). In many cases, production of compounds

directly by microbial fermentation is much more

economical than using synthetic chemistry, e.g.,

steroids, beta-lactams, and erythromycin. Indeed,

the potential to commercialize a compound without

chemical modification distinguishes natural products

H. Gao, X. Zhou, and Z. Gou contributed equally to this work.

H. Gao  X. Zhou  Z. Gou  Y. Zhuo 

C. Fu  M. Liu  F. Song  E. Ashforth  L. Zhang (&)

Institute of Microbiology, Chinese Academy of Sciences

(CAS), Beijing, People’s Republic of China

e-mail: lzhang03@gmail.com

Y. Zhuo  C. Fu

Graduate University of Chinese Academy of Sciences,

Beijing, People’s Republic of China

123

Antonie van Leeuwenhoek

DOI 10.1007/s10482-010-9442-4

from all other sources of chemically diverse compounds

and fuel efforts to discover such new

compounds.

Nature has been continually carrying out its own

version of combinatorial chemistry (Verdine 1996)

for over three billion years in which bacteria have

inhabited the earth (Holland 1997). During that time,

there has been an evolutionary process going on in

which producers of secondary metabolites evolved in

response to needs and challenges of their local

environment. If the metabolites were useful to the

organism, the biosynthetic genes were retained and

genetic modifications further improved the process.

Combinatorial chemistry practiced by nature is much

more sophisticated than that in the laboratory,

yielding exotic structures rich in stereochemistry,

concatenated rings, and reactive functional groups

(Verdine 1996). As a result, an amazing variety and

number of products have been found in nature.

160,000 natural products have been identified (Ryan

2000), a value growing by 10,000 per year. About

100,000 secondary metabolites of molecular weight

less than 2500 have been characterized, half by

microbes and the other half by plants (Roessner and

Scott 1996).

Our growing understanding of natural products has

revealed their importance in the survival, competition

and communication between microorganisms. Driven

by profitability, microbial production requires the

development of lower operational costs with concurrently

higher yields (Lee et al. 2005). For microbiologists

this is translated into research on manipulating

and improving microbial strains in order to enhance

their metabolic capabilities, commonly referred to as

strain improvement.

Traditionally, improved strains have been developed

through random mutagenesis followed by

screening for mutants exhibiting enhanced properties

of interest (Parekh et al. 2000). This empirical

approach has a long history of success and generated

a variety of microbes capable of over-producing

metabolites (Elander 2003; Hermann 2003). However,

recent developments in the precision engineering

have enabled us to optimize an existing biotech

process and hence to improve the desirable cell

properties by a completely new approach. The

concept of precision engineering originates from the

field of mechanical engineering. The basic idea is that

machine tools obey cause and effect relationships that

are within our ability to understand and control

(Schellekens et al. 1998). In the field of biology, the

basic idea of precision engineering is to understand

and control the cause-phenotype relationships. Precision

engineering can be achieved by, but is not

limited to, genetic modification and classical tools of

metabolic engineering. Different from the conventional

metabolism-oriented engineering strategy, such

a strategy focuses primarily on association analysis of

the cause-phenotype relationships of biology systems

(Patnaik 2008) and thus precisely engineering the

cause to improve the corresponding phenotype of

interest. Therefore, this strategy aims not only to

improve microbial phenotype, but also to further

investigate the mechanisms underpinning the desired

phenotype.

Precision engineering could be divided two subdivisions:

forward and reverse precision engineering

(Fig. 1). Specifically, forward precision engineering

is a route of association analysis from micro- to

macro-scale. Study from gene scale (DNA), to

transcriptional scale (RNA), translational scale (protein),

metabolic scale (metabolite), finally to phenotypic

scale (physiology). After perturbation (such as

modification of a gene), the gene is tracked and

monitored along the route, thus to explore the

association of the gene and certain phenotype. For

reverse precision engineering, the route is in reverse

direction, in other words, from macro- to micro-scale.

That is, after perturbation (such as alteration of a

phenotype), alterations in metabolic, translational,

transcriptional and gene scales are identified (for

example, by comparative omics analysis).

Many excellent examples illustrate the powerful

utility of this novel approach, some of which are

briefly described below (Bailey 1998).

Extension of substrate range

In many biotech processes, the industrial strains often

have a narrow substrate spectrum which restricts the

production of useful compounds. Therefore, it is

deemed necessary to broaden their substrate range.

This can be achieved by inserting the necessary

pathway (or enzyme) for utilization of the substrate

of interest. Furthermore, it is important to ensure that

the substrate can be directly assimilated and metabolized

by the host organism at a reasonable rate. Many

examples of substrate range extension are described in

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the literature with successful industrial microorganisms

created after the desired substrate could be

utilized (Domingues et al. 1999a, b; Vincent et al.

1999; Ostergaard et al. 2000; Meijnen et al. 2008).

Manipulation of pathways

Precision engineering offers immense possibilities for

manipulating pathways leading to better performance

of industrial microorganisms. There may be three

different options for manipulating pathways in a

certain organism: introduction of completely new

pathways (Hopwood et al. 1985; Omura et al. 1986;

Govender et al. 2008); extension of existing partial

pathways to reduce formation of by-products (Anderson

et al. 1985; Isogai et al. 1991; Hols et al. 1999);

and deletion of competing pathways in a given

microorganism (Lee et al. 2006; Zhang et al. 2006;

Romero et al. 2007; Shaw et al. 2008); Zhang et al.

2006; Romero et al. 2007; Shaw et al. 2008).

Improvement of productivity

Economic factors have been prompting researchers to

put added emphasis on improving productivity. The

cost competitiveness of a process depends on yield

and producing rate, especially for the production of

low-value-added products. Various approaches have

been suggested for increasing the yield and producing

rate, such as increasing the amount of ‘‘rate-limiting’’

enzymes, cofactor replenishment, and increase of

precursor supply (Stambuk et al. 2006; Xiang et al.

2009). There have been many examples of the

application of precision engineering to improve the

yield and producing rate of different processes, and

several recent reviews exist on the developments in

this area (Nielsen 1998; Aristidou and Penttila 2000).

Improvements of cellular properties

In the improvement of microorganisms, it may be of use

to engineer the cellular physiology, such as to improve

bacterial stress resistance (Fu et al. 2006), improve their

glucose tolerance (Alper et al. 2006), engineer their

morphology (especially for filamentous microorganisms,

whose morphology has a significant impact on the

overall process performance) (Nielsen 2001), or

increase flocculation (Watari et al. 1994). This may

involve expression of heterologous genes, disruption of

genes or over-expression of homologous genes.

The above examples show the approaches to

obtain optimized microorganisms. However, precision

engineering of naturally producing microorganisms

currently has limited applications. The lack of

knowledge of regulation systems in cells and the

operation in a ‘‘black box’’ system, where unknown

change may have taken place between the initial and

production strains, limits our engineering efficiency.

In the cell system, which element is the most

important one to control the phenotype that we are

interested in? This question calls for a new strategy

for improving strain, and as a result, precision

engineering has been developed.

In recent years there has been rapid development

in the field of precision engineering. With such

extensive coverage of this research field, it is difficult

to make a comprehensive review. Therefore, this

review will illustrate the principles of precision

engineering with selected examples, which can show

the power of the technology.

Driving forces of precision engineering

The most important driving force in the development

of precision engineering have been human beings

Fig. 1 Forward and reverse precision engineering: the flow

and the technologies involved. For traditional strain improvement,

the mechanism is unknown, and the process is long,

while for precision engineering, the process could be shortened

based on the understand and control of the cause-phenotype

relationships. Forward precision engineering is a route of

association analysis from micro- to macro-scale, and for

reverse precision engineering, the route is in reverse direction

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with their high demands for a better quality of life

(such as energy, medical, and environment) and from

the technological breakthroughs in genome sequencing

which offer unprecedented opportunities. DNA

sequencing has become faster and cheaper, and

sequencing the entire genome of a microbial organism

can now be accomplished in a few months. As a

result, important public sequence databases are

doubling in size every 18 months (Stahler et al.

2006), and more and more genome sequences of

production strains are becoming available (Fig. 2 and

Table 1).

From 1995 to 2007, the number of completely

sequenced genomes increased exponentially (Fig. 3).

Genome sequences provide access to genes and

control elements important for strain improvement

and with more genome sequences being made

available, we may be closer to the fundamental

nature of strain improvement.

The second development that drives precision

engineering is continuing advancement in tools of

molecular biology, making information of all scales

in precision engineering could be accessed. Transcriptional

profiling by DNA microarrays, proteome

profiling by two-dimensional electrophoresis, and

metabolite profiling by HPLC, in fact all methods that

enable us to view a cell at the scales of transcription,

protein and metabolite, have succeeded in dramatically

improving strains, and can potentially be used to

more accurately identify key genetic targets and

pathways for improving strains. These methods have

become increasingly popular as researchers become

familiar with and apply these techniques (Fig. 4).

Precision engineering is an ideal framework for the

integration of these data sets and all existing modern

technologies.

Strategies for strain improvement by precision

engineering

Forward precision engineering

In 1991, Bailey discussed the emerging of metabolic

engineering. In the paper, metabolic engineering was

defined as ‘‘the improvement of cellular activities by

manipulations of enzymatic, transport, and regulatory

functions of the cell with the use of recombinant

DNA technology’’ (Bailey 1991). As one of the

examples of metabolic engineering, metabolic flux

analysis (MFA) was used to classify metabolic

branch point. In a similar vein Stephanopoulos and

Vallino (1991) commented that through analysis of

different mutants, it is possible to obtain information

about the regulation of the different fluxes. ‘‘Forward

precision engineering’’ is an advancement of metabolic

engineering, and developments in new techniques,

such as genetic manipulation methods, have

made it possible to rapidly introduce precise genetic

changes and make the corresponding protein. Subsequently

the metabolite and phenotype of the target

strain can be changed.

There are many applications of forward precision

engineering, which involve a variety of technologies.

The applications fall into the following fields:

Finding regulators and global transcriptional factors

One application is to improve the yield and

productivity of natural products synthesized by

microorganisms. We have improved production of

erythromycin A with the use of forward precision

engineering (Wang et al. 2007). Based on the fact

that Kim et al. (2003) and Okamoto et al. (2003)

found that the level of intracellular S-adenosylmethionine

(SAM) plays important roles as a factor in

antibiotic production in Streptomyces sp., we integrated

an SAM synthetase gene from Streptomyces

spectabilis along with vector DNA into the

chromosome of an erythromycin A overproducer,

Saccharopolyspora erythraea E2. The production of

SAM of the resultant recombinant strain E1 was

elevated and titer of erythromycin was increased

from 920 IU/ml by E2 to approximately 2000 IU/ml

by E1. Also erythromycin B, the main impurity

component in erythromycin, decreased. Though the

Fig. 2 Genomes sequenced: production/model/pathogen strains regulation of genes leading the change in the SAM

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pathways did not change, the morphology of E1 was

affected. The expression of the heterologous SAM

synthetase gene in Saccharopolyspora erythraea

inhibited sporulation, other groups have reported

similar results in B. subtilis (Ochi and Freese 1982)

and in Streptomyces lividans (Kim et al. 2003). Our

example is different from traditional precision

engineering used to improve strains. S-adenosyl-Lmethionine

synthetase catalyzes the biosynthesis of

SAM from ATP and L-methionine, and so the gene

is not responsible for the synthesis of the product or

by-products directly. However, it was selected as a

potential key regulator in erythromycin production,

and changes in phenotype were successful.

Recently, global perturbations in gene expression

has been accomplished by engineering cellular transcription

and translation machinery (Alper et al.

2006) by a tool termed global transcription machinery

engineering (gTME). This method was applied to

improve ethanol tolerance, lycopene overproduction,

and simultaneous phenotypes of tolerance to ethanol

and the detergent SDS. In the case of lycopene

overproduction (Alper and Stephanopoulos 2007), the

rpoD mutants were screened based on increased

lycopene content. The results showed that a single

round of selection using gTME is more effective than

rounds of single-gene knockout or overexpression

modifications linked with a search strategy, and the

lycopene production was improved to 7.7 mg/l.

Table 1 Complete genomes sequence published of production strains in recent years

Strain Size of genome (Mb) Year Journal

Saccharomyces cerevisiae 12.1 1997 Nature (Cherry et al. 1997)

Bacillus subtilis 4.2 1997 Nature (Kunst et al. 1997)

Clostridium acetobutylicum 3.9 2001 J. Bacteriol. (Nolling et al. 2001)

Bifidobacterium longum 2.3 2002 PNAS (Schell et al. 2002)

Pseudomonas putida 6.2 2002 Appl. Environ. Microbiol. (Nelson et al. 2002)

Lactobacillus plantarum 3.3 2003 PNAS (Kleerebezem et al. 2003)

Corynebacterium glutamicum 3.3 2003 J. Biotechnol. (Kalinowski et al. 2003)

Streptomyces avermitilis 9.0 2003 Nature Biotechnol. (Ikeda et al. 2003)

Lactococcus lactis 2.5 2004 PNAS (Blatny et al. 2004)

Mannheimia succiniciproducens 2.3 2004 Nature Biotechnol. (Hong et al. 2004)

Streptococcus thermophilus 1.8 2004 Nature Biotechnol. (Bolotin et al. 2004)

Thermus thermophilus 2.1 2004 Nature Biotechnol. (Henne et al. 2004)

Zymomonas mobilis 2.1 2004 Nature Biotechnol. (Seo et al. 2005)

Aspergillus oryzae 37 2005 Nature (Machida et al. 2005)

Gluconobacter oxydans 2.7 2005 Nature Biotechnol. (Prust et al. 2005)

Lactobacillus sakei 1.9 2005 Nature Biotechnol. (Chaillou et al. 2005)

Ralstonia eutropha 7.1 2006 Nature Biotechnol. (Pohlmann et al. 2006)

Aspergillus niger 33.9 2007 Nature Biotechnol. (Pel et al. 2007)

Pichia stipitis 15.4 2007 Nature Biotechnol. (Jeffries et al. 2007)

Saccharopolyspora erythraea 8.2 2007 Nature Biotechnol. (Oliynyk et al. 2007)

Kocuria rhizophila 2.7 2008 J. Bacteriol. (Takarada et al. 2008)

Streptomyces griseus 8.5 2008 J. Bacteriol. (Ohnishi et al. 2008)

Bacillus cereus 5.2 2009 J. Bacteriol. (Xiong et al. 2009)

Fig. 3 Completely sequenced genomes from 1995 to 2009.

Data from www.genomesonline.org

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Cofactor engineering

Forward precision engineering also focuses on engineering

the transportation systems and improving

supply of cofactor. An excellent example is the work

of Lopez de Felipe et al. (1998). Lopez de Felipe and

coworkers constructed NADH oxidase-overproducing

Lactococcus lactis strains. The increase of NADH

oxidase activity provoked a shift from homolactic to

mixed-acid fermentation during aerobic glucose

catabolism. This study demonstrated that forward

precision engineering on the level of oxidation of the

key cofactor NADH can change L. lactis from a

homolactic bacterium to a highly acetoin- or diacetylproducing

bacterium.

Except for the cofactor NAD/NADH, coenzyme A

(CoA) and its derivative acyl-CoA are also important

cofactor, which are intermediates in numerous biosynthetic

pathways of natural products. Vadali et al.

(2004) studied the time profiles of intracellular CoA

and acetyl-CoA levels in fermentation of E. coli.

They found that the combined effect of precursor

pantothenic acid supplementation and pantothenate

kinase overexpression has a significant effect on

increasing the intracellular CoA/acetyl-CoA levels,

and the increase led to metabolic flux redistribution

toward acetate production pathway.

Blocking branches for bioproducts

Zhang et al. (2006) inhibited competing pathway to

increase production of 1,3-propanediol (1,3-PD) by

forward precision engineering. Production of 1,3-PD

from glycerol by Klebsiella pneumoniae is restrained

by ethanol formation (Menzel et al. 1997). The first

step in ethanol formation from acetyl-CoA is catalyzed

by aldehyde dehydrogenase (ALDH), an

enzyme that competes with 1,3-PD oxidoreductase

for the cofactor NADH (Zeng and Biebl 2002). So the

aldA gene encoding ALDH was inactivated, and

ethanol formation was almost abolished in order to

redirect NADH into 1,3-PD production pathway. The

specific 1,3-PD-producing capability (1,3-PD produced

per gram of cells) of the mutant strain was

2-fold that of the parent strain due to a lower growth

yield of the mutant. It is worth noting that in this

work, physiological change also was observed, and

the cell growth was retarded (Zhang et al. 2006).

Thermoanaerobacterium saccharolyticum is a

thermophilic anaerobic bacterium that hydrolyzes

xylan and ferments the majority of biomass-derived

sugars to ethanol at high yield. In addition to ethanol,

organic acids are also produced. Knockout of genes

involved in organic acid formation (acetate kinase,

phosphate acetyltransferase, and L-lactate dehydrogenase)

resulted in a strain able to produce ethanol as

the only detectable organic product (Shaw et al.

2008). Using the engineered strain (ALK2) in simultaneous

hydrolysis and fermentation experiments at

50C allows a 2.5-fold reduction in cellulase loading

Fig. 4 Profiling methods published in recent years

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compared with using Saccharomyces cerevisiae at

37C, which is significant in light of the dominant

impact of the cost of hydrolysis. The maximum

ethanol titer produced by strain ALK2, 37 g/l, is the

highest reported thus far for a thermophilic anaerobe.

Redistribution of metabolic flux

Dicarboxylic acids (DCAs) can be obtained by

oxidizing alkanes by Candida tropicalis (Blandin

et al. 2000), but beta-oxidation would degrade fatty

acids into acetyl-CoA and limit DCA yield. The

acetyl-CoA resulting from beta-oxidation would be

transported into tricarboxylic acid (TCA) cycle by

carnitine acetyl transferase (CAT) to generate energy

supplying to cells growth and synthesis of DCAs. In

our work, metabolic flux was redistributed. Acetyl-

CoA transportation between beta-oxidation and TCA

cycle was inhibited by inhibiting CAT enzyme

activity, thereby the flux of acetyl-CoA distributed

in TCA cycle was reduced by 21% (Fig. 5), and

consequently the DCA production was increased

(Cao et al. 2006). This could be regarded as another

example of forward precision engineering, in which

the transportation system was selected to be engineered

to redistribute metabolic fluxes, and the

changes from the genes to the pathways to phenotype

were observed.

System biology, directed evolution of enzymes

and pathways have provided tools for forward

precision engineering. Many literatures have demonstrated

the efficacy and efficiency of these approaches

in producing both natural products in a wide variety

of fermentative hosts. The ability to draw phenotype

to genotype correlations is critical for the success of

these targets.

Reverse precision engineering

The strategy termed ‘‘reverse precision engineering’’

is the route to an improved strain by first identifying

the desired phenotype (Fig. 4). It involves identifying

mutations between the different phenotypes, defining

mutations beneficial for production, and transferring

them to the chosen organisms to achieve the goal.

Almost all the modern biological technologies

involve reverse precision engineering in order to

obtain a global view of cell. Several successful

examples of reverse precision engineering have been

reported (Gonzalez et al. 2003; Han et al. 2003;

Lange et al. 2003; Lee et al. 2003; Wahlbom et al.

2003; Kromer et al. 2004; Bro et al. 2005) and show

the development of this technique.

Ohnishi et al. (2002) reported a novel methodology

to generate a new L-lysine-producing mutant.

Mutations between an L-lysine-hyperproducing strain

Corynebacterium glutamicum B-6 (constructed by an

iterative procedure of random mutation) and the wild

type were identified by comparative genomic analysis,

and V59A mutation in the homoserine dehydrogenase

gene (hom), a T311I mutation in the

aspartokinase gene (lysC), and P458S mutation in

the pyruvate carboxylase gene (pyc), was determined

to be relevant to lysine production. Lysine production

of the wild-type strain was almost zero. Introduction

of the hom and lysC mutations into the wild-type

strain by allelic replacement resulted in accumulation

of 8 g and 55 g of L-lysine/l, respectively, and the

two mutations exerted a synergistic effect on

production (75 g/l) upon their coexistence in the

wild-type genome. Further introduction of the pyc

mutation resulted in an additional contribution and

accumulation of 80 g/l. The key aspect of this

approach is that beneficial mutations could be

targeted with more ease.

Mogensen et al. (2006) investigated carbon catabolite

repression by comparing the transcriptome data

of a creA (an important regulatory protein controlling

carbon repression) in a deleted mutant strain (Aspergillus

nidulans) with a reference strain grown either

with glucose or ethanol as the sole carbon source. The

authors found evidence that more than 25% of the

genes that are repressed by growth on glucose are not

all de-repressed during growth on ethanol, indicating

that the carbon catabolite repression is not a simple

on/off switch but a more complex system, which not

only dependent on the presence or absence of CreA

but also on the carbon source. The information is

valuable for gaining further insight into the mechanism

of carbon repression in microbes and the role of

CreA in phenotype of carbon catabolite repression.

Changes in phenotype resulting from differences

in pathways can also caused by changes in environmental

conditions. Usaite and coworkers examined

the impact of ammonium, L-alanine, and L-glutamine

on the physiology and transcriptome of the yeast

S. cerevisiae and linked the physiological data to the

transcriptome data using an enzyme subnetwork

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analysis (Usaite et al. 2006). Then the transcript

profiles were clustered. The consensus cluster analysis

together with promoter and metabolic pathway

analysis provided an efficient method to correlate

transcriptional change to physiology.

Fluxomes have also been combined with transcriptomes

data to reveal the key genes involved in

changing phenotype. Cakir and co-workers found that

change in control effective flux (CEFs) under different

environmental conditions was correlated with the

corresponding changes in the transcriptome (Cakir

et al. 2007). CEF analysis was applied to investigate

the degree of transcriptional regulation of fluxed in

the metabolism of Saccharomyces cerevisiae. The

CEF analysis indicated that changes in carbon source

are associated with a high degree of hierarchical

regulation of metabolic fluxes in the central carbon

metabolism as the change in fluxes are correlating

directly with the change in transcript levels of genes

encoding their corresponding enzymes (Cakir et al.

2007). The study showed that the major reason for

lack of correlation reported before was due to

neglecting the flexibility of the network, and provided

a new tool for reverse precision engineering to use

the fluxome to identify key genes.

A new metabolic model of Aspergillus niger was

constructed, which was integrated with bibliome

(Bibliome is the totality of biological text corpus,

also termed as literaturome and textome. By approximate

analogy to genome, metabolome, proteome,

and transcriptome, this -ome would properly refer to

the literature of a specified or contextually implied

field), genome, metabolome and reactome (Andersen

et al. 2008). The metabolic network was based on the

reports of 371 articles and comprised of 1190

biochemically unique reactions and 871 ORFs. The

research showed a potential trend of reverse precision

engineering while providing a comprehensive knowledge

base for the metabolism to be acquired.

There have been many successful examples of

forward and reverse precision engineering, however,

experience clearly shows that a detailed quantitative

knowledge of the cell is required for the rational

design of superior production strains. Any one

technology alone will not reveal the complexity in

the biological cell because the interactions exist at the

level of gene, transcription, protein, metabolite, and

fluxes. Any of them is not sufficient, and other

methods are necessary to ensure that the gene or

pathway targets are met. Therefore, new strategies

combining existing methods have been developed for

more effective strain improvement.

Strategies combining forward and reverse

precision engineering

Recently forward and reverse precision engineering

have been integrated to improve industrial organisms

in a much shorter time span than was previously

thought possible (Fig. 4). Park et al. (2007) reported a

Fig. 5 The metabolic pathway of alkane in C. tropicalis. The

pathway involves enzymes cytochrome P450 (P450) responsible

for a,x-oxidation and production of DCA, and CAT

responsible for transporting the acetyl-CoA (produced through

b-oxidation of the fatty acids) into the TCA cycle in the

mitochondrion. In the engineered strain, the CAT activity was

reduced and DCA13 production was increased. The data are

from Cao et al. (2006)

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successful example of combined precision engineering

strategy. The strategy included: metabolic engineering

of E. coli for L-valine production and

generation of a valine production strain and control

strain; transcriptome analysis and comparison of

transcriptome profiles of the valine production strain

and control strain; enhanced production of valine by

engineering the targeted genes identified in transcriptome

profiling; and improvement of strains based on

in silico gene knockout studies. By this combined

strategy a strain with the highest yield compared to

that of the industrial strain was obtained, and the

engineered strain could be further improved based on

the clearly defined modification.

Another interesting paper describes a full-scale

application in this field (Askenazi et al. 2003).

Askenazi and colleagues combined transcriptional

and metabolomic profiles to develop an Aspergillus

strain overproducing lovastatin, a cholesterol-lowering

drug. Improved lovastatin production was initiated

by creating a set of strains producing different

level of lovastatin by expressing the genes thought to

be involved in lovastatin synthesis, or known to

broadly affect secondary metabolite production in the

parental strain (forward precision engineering). These

strains were characterized by metabolomic and

transcriptional profiling followed by an association

analysis to link gene expression with metabolite

production. From this process the potential key

parameters affecting the production of lovastatin

were identified. Thus, the target genes were identified

and manipulated to improve lovastatin production by

over 50% (reverse precision engineering). Although

it’s difficult to differentiate genes whose altered

expression level is the result or cause of the

phenotype of interest, such an integrated precision

engineering strategy is suitable for all industrially

useful strains for which genome data is limited.

The strategies combining forward and reverse

precision engineering are still in development. For

enabling rapid engineering of complex phenotypes in

microbes, newer tools for dissecting the genotypephenotype

correlation are needed. In the above

example of lovastatin, Pearson product-moment correlation

coefficients were calculated, and hierarchical

clustering and principal component analysis (PCA)

were applied to analyse the association of the

resulting transcriptional and metabolic data sets while

reducing the complexity of profiling data sets. In the

engineering of Saccharomyces cerevisiae to study

glucose repression, PCA was also used to identify

genes having a significantly altered expression relative

to the control strain (Westergaard et al. 2007).

These analytical tools combine with precision engineering

approaches, and provided with a new powerful

platform for strain ration design (Patnaik 2008).

Conclusion

Precision engineering, especially reverse precision

engineering, still requires further development before

its full potential can be fully realized. It’s use is

limited largely by the global knowledge of the cell,

the translation of the functions and characteristics of

the biological systems. Precision engineering requires

the integration of various high-throughput technologies

in order to rationally design microbes for the

efficient production of commercially valuable metabolites.

As the information base continues to expand,

the application of precision engineering is expected

to grow rapidly.

The examples given clearly demonstrated the

power of applying precision engineering strategies

to improve strains, especially for industrial organisms.

Precision engineering includes several differentiating

features that extend the potential of classical

strain improvement and traditional metabolic engineering

methods. It presents a trend towards more

holistic approaches in metabolic engineering focusing

on targeting regulators other than those over-expressing

individual structural genes. The development of

precision engineering will be driven by advances in

functional genomics, analytical techniques for measurement

of transcriptional profiles using DNA chips,

proteomic profiles using 2D gels, and metabolite

profiling. Precision Engineering is a good framework

for integrating all existing advanced technologies to

improve strains. Its impact on industrial biotechnology

will be far reaching in the future.

Acknowledgments We thank Prof. Arnold Demain for critical

reading of the manuscript and helpful discussions. This work

was supported in part by grants from National Natural Science

Foundation of China (No. 30700015), National 863 project

(2006AA09Z402 and 2007AA09Z443), and Key Project of

International Cooperation (2007DFB31620). National Key

Technology R&D Program 2007BAI26B02, the National

Science & Technology Pillar Program (No. 200703295000-

02), Important National Science&Technology Specific Projects

Antonie van Leeuwenhoek

123

(No. 2008ZX09401-05), and Science and Technology Planning

Project of Guangdong Province, China (No. 2006A50103001).

L.Z. was an awardee for Hundred Talents Program.

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