Cell Systems
Review
Cell Factory Engineering
Anne Mathilde Davy,1 Helene Faustrup Kildegaard,2 and Mikael Rørdam Andersen1,*
1Department of Biotechnology and Biomedicine
2The Novo Nordisk Foundation Center for Biosustainability
Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
*Correspondence: mr@bio.dtu.dk
http://dx.doi.org/10.1016/j.cels.2017.02.010
Rational approaches to modifying cells to make molecules of interest are of substantial economic and
scientific interest. Most of these efforts aim at the production of native metabolites, expression of heterologous
biosynthetic pathways, or protein expression. Reviews of these topics have largely focused on
individual strategies or cell types, but collectively they fall under the broad umbrella of a growing field known
as cell factory engineering. Here we condense >130 reviews and key studies in the art into a meta-review of
cell factory engineering. We identified 33 generic strategies in the field, all applicable to multiple types of cells
and products, and proven successful in multiple major cell types. These apply to three major categories: production
of native metabolites and/or bioactives, heterologous expression of biosynthetic pathways, and
protein expression. This meta-review provides general strategy guides for the broad range of applications
of rational engineering of cell factories.
Introduction
Cells engineered for the enhanced production of native compounds,
or production of heterologous products is an established
and economically important discipline. Serving as the
basis of all product-oriented industrial biotechnology, the economic
footprint of these cell factories ranges in the hundreds
of billions of USD/year on the global markets: pharmaceutical
proteins have been estimated at 140 billion USD in 2013 (Walsh,
2014); industrial enzymes in the range of 1.8 billion USD in 2009
(Waegeman and Soetaert, 2011); bio-derived non-protein pharmaceuticals
100 billion USD (Chemier et al., 2009); and bulk
biochemicals (excluding biofuels) 58 billion USD (Nieuwenhuizen
and Lyon, 2011). For comparison, the petrochemical industry
is >3 trillion USD/year (2015), so there is still a large market to
expand into.
While industrial biotechnology has a long history, it was not until
the arrival of genetic engineering that it became possible to
modify the DNA of the cell factories to improve production
(Figure 1), a process that hitherto had been based on clonal selection.
Such developments gave rise to the discipline cellular
engineering (Nerem, 1991), which covers both basic and applied
cell research. The same year, Bailey defined metabolic engineering
as a rational and directed process of engineering metabolism,
rather than a cycle of trial and error (Bailey, 1991). Since
then, the field of engineering cell factories has expanded in
outlook and scope to include several ‘‘flavors’’ of cellular engineering
specific to industrial biotechnology (Figure 1). Two
examples are (1) inverse metabolic engineering (Bailey et al.,
2002), in which one starts with the desired phenotype and works
toward that goal by directed genetic or environmental manipulation,
and (2) systems metabolic engineering as coined by the
group of Sang-Yup Lee (Lee et al., 2007; Lee and Kim, 2015)
as a term for large-scale holistic metabolic engineering. However,
in many applications, the engineering efforts are not limited
to the metabolic network of the cell, and therefore ‘‘metabolic
engineering’’ does not fully encompass all activities. This is
particularly true for the large sector of expression of protein,
native and heterologous, which covers the range from bulk enzymes
to formulated pharmaceutical proteins. Here, engineering
targets can be within cellular machinery such as the protein
secretion pathway. To include all of these activities, this meta-review
defines cell factory engineering, encompassing all rational
approaches to improve a cell factory.
The objective of this meta-review is specifically not to present
a comprehensive list of examples within the individual strategies,
nor is it to present direct strategies for target identification, such
as modeling in tandem with predictive algorithms (Ranganathan
et al., 2010; Burgard et al., 2003; Pharkya and Maranas, 2006).
For this, specialized reviews of high quality and information content
already exist (see, e.g., the excellent and recent work of the
group of Sang-Yup Lee; Lee and Kim, 2015). In this text, we provide
a meta-review summarizing several years of cell engineering
efforts, in essence an applicable list of strategies generally applicable
across species and products suitable for the experienced
scientist. In this, we have focused on strategies applied reproducibly
across multiple cell factories, and chosen the most
applied microbial cell factories from the entire Tree of life, spanning
bacteria, yeast, filamentous fungi, and mammalian cells (in
particular Chinese hamster ovary [CHO] cells), as well as some
higher fungi. See also Box 1 for an overview of cell factory engineering
methods in other fields. This meta-review provides
representative examples of applications of the strategies for
illustration.
Meta-review Overview
The analysis here draws on a long list of reviews supplemented
by primary literature to provide an overview of cell factory engineering.
Table 1 lists the reviews cited in this text and annotations
on which types of strategies and organisms these reviews
are most relevant for.
It is the reductionist argument of this meta-review that nearly
all cell factory engineering efforts can be classified in one of
the following three categories or as combinations of them: (1)
optimization of the production of a metabolite in the native
262 Cell Systems 4, March 22, 2017 ª 2017 Elsevier Inc.
host; (2) production of a non-native metabolite by expression of a
heterologous biosynthetic pathway; and (3) expression of a heterologous
protein. Here, we condense the strategies of the reviews
in Table 1 into these three generic categories, examine
each in detail, and provide guidance on choosing and applying
individual strategies.
Production of Native Metabolites
Native metabolites are here compounds naturally produced by
the cell factory, either intracellularly or (preferably) a secreted
compound. Examples are amino and nucleic acids, antibiotics,
vitamins, enzymes, bioactive compounds, and proteins produced
from anabolic pathways of cells (see details for protein
products further below). Common for these are that they cannot
be synthetically produced or for which it is not economical to do
so (Stephanopoulos and Vallino, 1991). This has been examined
for specific cells or products in a multitude of excellent reviews
(see, e.g., Bailey et al., 2002; Pickens et al., 2011; Stephanopoulos
and Vallino, 1991; Keasling, 2008, 2012; Hwang et al., 2014;
Wu et al., 2014; Weber et al., 2015; Kiel et al., 2010; Xiao and
Zhong, 2016). Here, we provide an overview of general strategies
to increase the formation of native metabolites (Figure 2).
The strategies one would apply to this problem can be
reduced to ten types (Figure 2, 1A–1J).
1A1-2. Pathway overexpression: Using this strategy, one
would typically overexpress one or more enzymes in the
biosynthetic pathway. It is a common strategy and is often
achieved by overexpressing the native genes (1A1). As an
alternative to normal overexpression, enzymes could be engineered
to have higher activity. In either case, it can be advantageous
to identify enzymatic steps with particular control of
the flux to the product, such as irreversible reactions, or the
first steps in the pathway. Some steps in the pathway (often
the latter) may have very little control over the flux, so multiple
targets should be engineered and/or metabolic control analysis
(Nielsen, 1998) should be employed. It has also been
seen that heterologous expression of ortholog enzymes
from related species (1A2) can have a larger effect than the
native enzymes. The reason for this remains speculative,
but one hypothesis could be a lower regulatory effect on
the heterologous proteins. One example of the latter is
enhanced citrate production in Aspergillus niger by heterologous
expression of tricarboxylic acid cycle enzymes from
Saccharomyces cerevisiae and Rhizopus oryzae (de Jongh
and Nielsen, 2008), or improved ganoderic acid accumulation
in Ganoderma lucidum (Xu et al., 2012).
1B1-2. Transporter engineering: Accumulation of the product
in the cell can decrease the carbon flux by affecting
enzyme kinetics, thus decreasing production rates and
yields. Furthermore, accumulation of product can trigger
feedback inhibition, which will severely limit the flux through
the pathway. In some cases, the product may even be toxic.
Overexpressing product efflux pumps can thus be an efficient
way of increasing the flux (1B1) (Dunlop et al., 2011; Wu et al.,
2014; Lee et al., 2012; Lee and Kim, 2015). One example
is the improved production of biofuels in Escherichia coli
by systems engineering of 43 efflux pumps (Dunlop et al.,
2011). This strategy both increased production and lowered
product toxicity. Alternatively, or in combination with 1B1,
gene knockouts of uptake transporters specific for the product
(1B2) can also be effective (Lee et al., 2012).
1C. De-branching: Branching or competing pathways can
decrease the overall flux toward the product (Pickens
et al., 2011; Lee et al., 2012; Pfleger et al., 2015). If these
pathways are not lethal, deleting the first branching step
may improve product formation. With essential pathways,
decreasing the activity by knockdown or, e.g., tunable
promoters can be an alternative option. This is a common
strategy; one comprehensive example includes the
knockout of L-lysine, L-methionine, and L-glycine biosynthesis
for improved isoleucine production in E. coli (Park
et al., 2012).
1D. Product degradation: Any non-essential reactions that
convert the product to unwanted metabolites should be
deleted, as these may degrade the product and decrease
yields and titers. Such an example is the work of Lee et al.
(2007), where threonine dehydrogenase was deleted in
E. coli to increase the production of L-threonine.
1E1-3. Co-factor engineering: In some cases, it has been
shown that a major limitation is the availability of co-factors
(NADH/NAD+
, NADPH2, NADP+
, acetyl-CoA, etc.) (Lee and
Kim, 2015; Ghosh et al., 2011; Lee et al., 2012; Pfleger
et al., 2015; van Rossum et al., 2016). In these cases, one
must make more co-factors available by engineering other
pathways. Ideally, the deletion of a non-essential enzyme,
which catabolizes large amounts of the co-factor, is preferred
(1E1). In cases where this is not possible, an alternative might
be replacing such an enzyme with a native or heterologous
enzyme with the same function, but specific for another cofactor
(1E2). An example of this is substitution of a native
NADPH-dependent glutamate dehydrogenase with an overexpressed
NADH-dependent glutamate dehydrogenase to
enhance sesquiterpene production in S. cerevisiae (Asadollahi
et al., 2009). A third option is the insertion (Yamauchi
Figure 1. Ontology of Different Types of Cellular Engineering
Covered in This Review
Cell Systems 4, March 22, 2017 263
Cell Systems
Review
et al., 2014) or overexpression (Cui et al., 2014) of an
E. coli transhydrogenase for interconversion of NADH and
NAPDH (1E3).
1F. Removal of feedback inhibition: In many cases, especially
with products that are a part of standard growth metabolism
(e.g., amino acids), strong feedback inhibition exists
to tightly regulate the concentrations of the product. When
one wishes to produce such compounds in large amounts,
it can be necessary to disable feedback inhibition. Often
this is achieved by random or targeted mutagenesis of enzymes
in the pathway known to be feedback inhibited
(Lee and Kim, 2015). In some cases, analogs of the product,
which bind tightly/near irreversibly to the regulated enzymes,
can be used to screen for feedback-deregulated
mutants. This has been used, e.g., for L-threonine (Lee
et al., 2003), and L-tryptophan and L-serine (Rodrigues
et al., 2013), both in E. coli. This strategy was also efficient
for engineering acid production in A. niger (de Jongh and
Nielsen, 2008) and for production of fatty acids in E. coli
(Pfleger et al., 2015).
1G. By-product elimination: Several species produce varying
amounts of by-products. Often these by-products, while not
directly linked to the metabolic pathway of the product,
compete with the product for available carbon and/or co-factors
(Lee et al., 2012). If possible without making lethal deletions,
the enzymatic activities producing such compounds
should be deleted or reduced. Numerous successful examples
of this strategy can be found, for instance removal of
glycerol biosynthesis in S. cerevisiae for increased ethanol
production (Wang et al., 2013).
1H. Precursor/substrate enrichment: It will often be advantageous
to increase the availability of the substrate for the
product biosynthesis (Lee and Kim, 2015; Pickens et al.,
2011; Lee et al., 2012). This can be achieved by a multitude
of strategies, essentially by considering the substrate as an
intermediate product, and applying one or more of strategies
1A–1J to increase substrate formation. When considering
substrates, one should also remember to take co-substrates
such as acetyl-CoA in account (see, e.g., a recent review of
acetyl-CoA engineering in S. cerevisiae; Nielsen, 2014). Other
carbon donors can also become limiting, e.g., malonyl-CoA
and glucose-1-phosphate in the production of an anti-cancer
compound in Streptomyces argillaceus (Zabala et al., 2013).
1I. De-regulation of carbon catabolism: In some cases, the
pathway of interest may be subject to general metabolic
regulation of the cell, e.g., general regulators of carbon catabolism
or nitrogen source-induced regulation. Examples of this
is de-regulation of galactose metabolism in S. cerevisiae
by deletion of negative regulators, leading to de-repression
and increased galactose utilization (Ostergaard et al., 2000)
or disruption of a global regulator in Pichia guilliermondii to
trigger aerobic glucose catabolism for ethanol production
(Qi et al., 2014).
1J. Signal transduction engineering: In some cases, the production
of specific metabolites is not regulated by carbon or
nitrogen sources (1I), but may be subject to signals from, e.g.,
micronutrients, or from other steps in the pathway. In these
cases, engineering signal transduction can be a strong strategy
(Kiel et al., 2010).
Choosing a Strategy for Producing Native Metabolites
Generally, there is a logical order in which to apply strategies 1A–
1J. The strategies can be sorted into three categories, which we
suggest to apply in progression.
Step 1: Direct Optimization of the Pathway in Any Way Possible.
The main goal of this step is to ensure that neither enzymes nor
intermediates of the pathway are limiting production. If this is not
achieved, the other strategies may not be effective. This can be
addressed by the following actions in roughly this order:
i. Overexpression of the biosynthetic pathway using the
strategies of 1A. This ensures that the concentrations
of the enzymes are not limiting.
ii. Enrichment for the substrates (1H) and for the co-factors
(1E), thus ensuring that the required metabolites,
precursors, and co-factors do not become limiting.
iii. Ensuring that the product is removed from the cell by
transporter engineering (1B) if possible. Accumulation
of the product can seriously decrease product formation
as enzyme kinetics are dependent on concentrations
of the product. Furthermore, product accumulation
can in some cases lead to feedback inhibition of
the entire pathway.
iv. If feedback inhibition is known for the pathway, this
should be engineered out if possible, or removed by
mutagenesis, screening and reverse genetics (1F).Again,
this may not be a problem if actions i–iii are limiting.
Step 2: Remove Competing Activities. Once the pathway itself
is optimized, the next steps is to ensure that no other pathways
are impairing the product formation, either directly by sharing metabolites
or co-factors, or by using carbon which could be converted
to product. The three main strategies here are as follows:
i. De-branching (1C). Any pathway that shares intermediates
or precursors with the pathway of interest should
be deleted if possible.
ii. Product degradation (1D). A particular case of 1C is
pathways converting/degrading the product of interest.
These should also be deleted if possible.
iii. Removal of by-products (1G). While by-products are
not often directly associated with the product pathway,
by-products will use carbon, co-factors, and energy
which could be converted into product.
Step 3: Application of Global Regulation Engineering. This
does not seem to be common strategies, as it will often be highly
effective to perform the actions above. However, should this be
in place, engineering carbon repression (1I) or similar signal
transduction pathways (1J) can be a final approach.
Clearly, the actions above can, and should be, combined for
increased effects. Prime examples of this are large-scale rational
design of metabolic pathways, which has been applied to great
effect in several systems, in particular in bacterial hosts (Lee
et al., 2007; Rodrigues et al., 2013; Becker et al., 2013) and yeast
(Wu et al., 2014; Lee et al., 2012).
Heterologous Expression of Biosynthetic Pathways
When trying to produce an interesting compound, one of the
most important decisions is the choice of production in the native
264 Cell Systems 4, March 22, 2017
Cell Systems
Review
host, and optimize this host, or transfer of the pathway to another
well-known host. If the original host can be adapted to an industrial
fermentation process, and there are no health-related risks
in doing so (e.g., production of toxic by-products), this can be
a preferred strategy (as was the case e.g., for penicillin). However,
in many modern cases, the potential of using an industrially
preferred cell factory and related platform processes out-weighs
the difficulty of transferring the pathway. In some cases, transplanting
a pathway also removes metabolic inhibition found in
the original host (Martin et al., 2003).
In this section, we examine how one may need to adapt the cell
factory in order to accommodate production of a heterologous
product. In general, several excellent specialized reviews exist
within this area, and for additional details on specific cases for
particular groups of compounds, we recommend the following
studies: Pickens et al. (2011), Pfeifer and Khosla (2001), Lee
et al. (2012), and Xiao and Zhong (2016). Here, we give an overview
of common and general problems regarding heterologous
expression of pathways.
Innate differences between the native host and the cell factory
of choice are major challenges in expressing a pathway in a new
host. In general, the compatibility of the enzymes and metabolites
with the new host should be considered. The more complicated
the pathway, the larger the advantage of choosing a more
closely related host. Major types of challenges are shown in
Figure 3.
These challenges can be condensed into points 2A–2F below.
Note that these cover both eukaryotic and prokaryotic hosts and
donors in combination, meaning that some of these are specific
to certain types of cells (e.g., intracellular compartmentalization
is seldom a problem in prokaryotes).
2A. Compartmentalization or steric proximity: In heterologous
expression, a common pitfall is not making sure that
the pathway is expressed in the same compartment as the
substrate metabolite. If the heterologous enzymes do not
contain targeting signals, in a eukaryotic host they will be expressed
in the cytosol. In the case where the substrate is in
another compartment, targeting sequences or gene fusions
can be applied to direct the heterologous enzyme(s) to the
correct compartment (Siddiqui et al., 2012). As an alternative,
synthetic scaffolds have been made to bring biosynthetic enzymes
together with great effect in both E. coli (Dueber et al.,
2009) and S. cerevisiae (Wang and Yu, 2012).
2B1-2. Co-factor availability: Any overexpressed pathway will
present a significant drain on available co-factors (van Rossum
et al., 2016). It is advantageous to ensure that these
are present in sufficient amounts in the host (2B1), as shown,
e.g., in Streptomyces coelicolor (Borodina et al., 2008). This
may be specific to the compartment (2B2). Alternatively,
transhydrogenases may be engineered as described in 1E1-2.
2C1-2. Substrate and co-substrate availability: In addition
to co-factors, one must also ensure that the host produces
all substrates and co-substrates/precursors required for the
pathway (2C1). It may also be the case that the host produces
similar compounds, which may be competing for the substrate
or precursors. In these cases, it can be advantageous
to delete the competing pathways (Baltz, 2016). It has been
demonstrated in, e.g., E. coli (Rodrigues et al., 2013, 2014)
and Corynebacterium glutamicum (Becker et al., 2013), that
high availability of the substrate in the heterologous host improves
productivity. If all (co-)substrates are not available or
present in low amounts, it is necessary to insert or overexpress
biosynthetic genes for these as well (2C2), Examples
of this are seen for, e.g., amino acids or oxaloacetate (Kind
et al., 2010; Rodrigues et al., 2013) or adipic acid (Yu
et al., 2014).
2D1-2. Product efflux pumps: When adding biosynthesis of a
new compound to a cell, specific transporters for that compound
may not exist. Accumulation of the product in the
cell will decrease the flux through the biosynthetic pathway
(Lee et al., 2012) and may also have toxic effects on the cell
(Pfeifer and Khosla, 2001). Passive transport or unspecific
transporters may be available, but if this is not the case a specific
transporter must be added (2D1,) as seen for, e.g., flavonoids
(Wu et al., 2014) or cadaverine (Qian et al., 2011).
Should the pathway be compartmentalized, this also needs
to be accounted for, possibly by expressing an organellespecific
transporter (2D2), e.g., with mitochondrial products
(Chen et al., 2015).
2E. Biosynthesis of functional groups: For a number of proteins,
all functional groups are not encoded by the gene,
Box 1. Research Applications of Cell Factory Engineering
The focus of the current review is on cell factory engineering for biotechnological applications. However, there are many other applications
of cell factory engineering in life sciences and medicine.
Besides industrial applications, a main application of cell factory engineering is to study the biological function of genes and proteins
in basic research. For this, genome engineering and synthetic biology tools can be applied to regulate and remove current
gene function or introduce new function, followed by analyzing the effect on cellular functions including biochemical reactions,
regulatory networks, or cell phenotypes (Hsu et al., 2014; Bashor et al., 2010). An example is engineering of genes involved in
glycosylation to study their function in generating certain glycoforms that can be applied to achieve homogenized glycoforms
on recombinant proteins for comparative studies of their biological effect (Yang et al., 2015).
Cell factory engineering is also often applied in generating reagents for research. Examples include expression of antibodies to
obtain reagents for genetics studies (Barnstable et al., 1978), hormones to obtain reagents for immunoassays (Ribela et al.,
1996), and purified proteins for structural analysis by crystallographers and nuclear magnetic resonance spectroscopists (Edwards
et al., 2000). In addition, the products from cell factory engineering can be applied in screening for drug activity or as a
potential drug target for medical applications (Trosset and Carbonell, 2015). This also includes engineering of natural probiotics
to produce valuable compounds for enhancement of their benefit to the host (Behnsen et al., 2013).
Cell Systems 4, March 22, 2017 265
Cell Systems
Review
but require separate biosynthesis. One example is heme
groups, found in multiple types of enzymes requiring oxygen
as a co-factor. Heme groups are not found in all prokaryotes
(Cavallaro et al., 2008), and may be limiting in some fungal
systems (Franken et al., 2011). Another functional group is
Fe-S clusters, which have several different biosynthetic pathways
specific to the type of host organism. Fe-S clusters are
synthesized in the cytoplasm of bacteria and in the mitochondrion
of eukaryotic microbes, from where they are transported
into the cytosol. In order for the heterologous pathway
Table 1. Overview of Reviews Covered in This Text
Reference Species Strategies Products
Walsh (2014) bacteria, yeasts, fungi,
mammalian cells
protein expression biopharma *
Waegeman and Soetaert (2011) E. coli protein expression enzymes and biopharma *
Chemier et al. (2009) E. coli, S. cerevisiae native and heterologous pathways bioactives, biofuels **
Lee and Kim (2015) bacteria, yeasts native and heterologous pathways any ***
Pickens et al. (2011) bacteria, fungi heterologous pathways bioactives ***
Hwang et al. (2014) Streptomyces sp. native pathways bioactives **
Wu et al. (2014) E. coli, S. cerevisiae heterologous pathways flavonoids **
Anyaogu and Mortensen (2015) fungi heterologous pathways bioactives *
Xiao and Zhong (2016) basidiomycetes heterologous pathways terpenoids *
Nielsen (1998) bacteria, yeasts, fungi,
mammalian cells
all any **
van Rossum et al. (2016) S. cerevisiae native and heterologous pathways acetyl-CoA-derived
compounds
**
Lee et al. (2012) E. coli native and heterologous pathways small molecules ***
Nielsen (2014) S. cerevisiae native and heterologous pathways acetyl-CoA-derived
compounds
*
Pfleger et al. (2015) E. coli, S. cerevisiae native and heterologous pathways oleochemicals **
Pfeifer and Khosla (2001) S. coelicolor, E. coli,
yeasts, fungi
native and heterologous pathways polyketides **
Siddiqui et al. (2012) yeasts heterologous pathways bioactives ***
Franken et al. (2011) fungi heterologous pathways heme *
Baltz (2016) actinomycetes heterologous pathways bioactives **
Bekiesch et al. (2016) actinomycetes heterologous pathways bioactives *
Fisher et al. (2014) bacteria, yeasts, fungi heterologous pathways any **
Mattanovich et al. (2012) yeasts protein expression biopharma, enzymes **
Hossler (2012); Hossler et al. (2009) mammalian cells protein expression biopharma **
Andersen et al. (2011) mammalian cells protein expression biopharma **
Damasceno et al. (2012) P. pastoris protein expression proteins ***
Celik and Calık (2011) yeasts protein expression proteins ***
Ward (2011) fungi protein expression proteins ***
Wurm (2004) mammalian cells protein expression biopharma *
Berkmen (2012) E. coli protein expression proteins with
disulfide bonds
*
de Marco (2009) E. coli protein expression proteins with
disulfide bonds
*
Schro¨ der (2008) eukaryotes protein expression biopharma **
Fleissner and Dersch (2010) Aspergillus protein expression enzymes **
Lubertozzi and Keasling (2009) Aspergillus native and heterologous pathways,
protein expression
small molecules, proteins *
Vogl et al. (2013) P. pastoris protein expression biopharma *
Rosano and Ceccarelli (2014) E. coli protein expression proteins **
Liu et al. (2013) Gram-positives protein expression proteins **
Gasser et al. (2008) bacteria, yeasts, fungi protein expression proteins **
Asterisks on the right denote the general relevance of the text for cell factory engineering.
266 Cell Systems 4, March 22, 2017
Cell Systems
Review
to be functional, it may be required to express the native
biosynthesis pathway heterologously (2E). A specific type of
Fe-S proteins (ferredoxins) mediates electron transfer. Cases
exist where the expression of specific ferredoxins from the
native host were necessary for optimal expression of the
pathway (Molna´ r et al., 2006).
2F. Transcription engineering: With many secondary metabolites,
several genes are required to act in concert to form
the product. If only one or a few genes are active, the product
may be absent or different from the expected product. For
many of these pathways, one regulatory protein exists, which
transcriptionally activates the entire pathway. If one uses
native promoters to express the genes, it can be advantageous
to overexpress the regulatory protein (if it can be identified),
and thereby induce the entire set of genes (Pickens
et al., 2011; Baltz, 2016; Bekiesch et al., 2016). Examples of
this include the transplant of the geodin gene cluster from
Aspergillus terreus into Aspergillus nidulans and substitution
of the native promoter for the transcription factor for a strong
constitutive promoter, thus allowing heterologous expression
of geodin (Nielsen et al., 2013).
Choosing a Strategy for Heterologous Pathway
Expression
For heterologous pathways, the strategy is a combination
of the issues encountered in the expression of native pathways,
and issues arriving from interaction with the new
host. Roughly, the considerations can be sorted into two major
steps.
Step 1: Compatibility of the Pathway to the Host. The actions
listed in this step are interesting in that they may not be needed,
dependent on the interaction with the host. Appropriate host selection
can thus be used already in the design phase to remove
or minimize the problems (for a few reviews on host selection,
see, e.g., Fisher et al., 2014; Lee and Kim, 2015; Bekiesch
et al., 2016). However, if these are not considered, no other engineering
strategies may be effective. The three main things to
consider are thus:
i. Compartmentalization (2A). Spatial co-localization of
the inserted enzymes as well as availability of co-factors
and precursors in the compartment(s) of choice.
ii. Functional group biosynthesis (2E). Ensuring functionality
of all enzymes.
iii. Substrates, co-substrates (2C), and co-factors (2B).
Ensuring that all required precursors are available in
the host.
Step 2: Optimization of the Pathway. Once it is ensured that the
pathway is functional in the host, one can apply strategies to increase
flux through the pathway. Here the following five steps
should be investigated, sorted in order of perceived importance.
Figure 2. Engineering Strategies for Optimizing the Production of Native Metabolites
Metabolites are denoted: S, substrate; S2, alternative substrate; X1-9, pathway intermediates; P, product of interest; BP, by-product. Generic engineering
strategies are marked in blue and annotated as 1A–1I (described in the main text).
Cell Systems 4, March 22, 2017 267
Cell Systems
Review
i. Application of transcription engineering where possible
(2B). Increased transcription of all enzymatic steps is an
efficient way to increase enzyme levels.
ii. Pathway overexpression strategies (1A1) and removal
of feedback inhibition (1F) are equally applicable to
heterologous pathways.
iii. Removal of competing activities as described in 1C and
1D. This is particularly interesting when producing a
compound, where the host produces several similar
compounds competing for the precursors, e.g., within
microbial bioactive compounds (Pickens et al., 2011).
iv. Improving the product efflux by transporter engineering
(2D and 1B).
v. Removal of by-products (1G) can possibly be considered
last, as the strategies above are more direct toward
improving the pathway. However, by-products
removal has been seen to have importance here
(Wu et al., 2014; Pickens et al., 2011).
In summary, the overview above provides a strategy guide for
heterologous pathway expression encompassing many different
reviews and studies. However, it is important to note that this
does not cover host-specific or donor-specific problems. In
these cases, we direct the reader to Table 1 to find suggestions
for additional species-specific engineering challenges.
Protein Expression
The expression of proteins, both homologous and heterologous,
is presently done in a wide variety of hosts from E. coli and Bacillis
subtilis, over yeasts, e.g., Klyuveromyces lactis, Pichia pastoris,
and S. cerevisiae, through filamentous fungi such as
A. niger, to cells derived from multicellular organisms such as
mammals and insects. The variety of proteins of commercial interest
is great, ranging from bulk enzymes to complex biopharmaceuticals
(Association of Manufacturers and Formulators
of Enzyme Products, 2009; Walsh, 2014).
Due to the diverse properties of proteins, it is currently not
possible to use one platform organism for expression of all proteins.
The scientist must thus choose the cell factory based on
the properties and applications of the desired protein. The advantages
and disadvantages of applying different cell factories
are discussed in several excellent reviews specialized to particular
expression systems (see Table 1). In particular we recommend
the review of Waegeman and Soetaert (2011) for a very
clear comparison of expression systems in addition to a thorough
overview of E. coli expression.
Despite the variety of employed systems, there are generic
strategies applicable to high-yield expression of proteins. Not
all of the strategies presented here are applicable in every
host, but we focus on strategies, which are applicable in several
hosts. For this reason, the present review does not discuss strategies
related to the accumulation of protein in inclusion bodies,
a feature encountered in some bacterial hosts such as E. coli for
some proteins with particular folding. For this, we again direct
the reader to specialized overviews (de Marco, 2009; Waegeman
and Soetaert, 2011).
Overall, the successful high-yield process for production of a
given protein requires high transcription and translation of the
Figure 3. Engineering Strategies for Heterologous Expression of Biosynthetic Pathways in a Eukaryotic Host Cell
Metabolites are denoted: S, Substrate; X1-6, pathway intermediates; P, product of interest; CS, co-substrate; E1-10, enzymes; FG, functional group. The inserted
heterologous pathway is marked in green. Generic engineering strategies are boxed in blue and annotated as 2A–2F (described in the main text).
268 Cell Systems 4, March 22, 2017
Cell Systems
Review
gene, successful targeting of the protein to the secretion
pathway (if secretion is desired), correct folding and limited
induction of secretion stress, the desired post-translational
modifications, efficient secretion, and limited or no degradation
of the product in the medium. In general, the major strategies
for engineering increased protein expression can be found in
Figure 4, and are summarized in points 3A–3F below.
3A1-2. Promoter Engineering: Nearly all systems aim at
ensuring maximal availability of recombinant mRNA so that
this is not a bottle neck for protein expression. The major
strategy employed is addition of a highly expressed constitutive
promoter (3A1). A selection of these are known for most
hosts such as the native GAPDH promoter in yeasts (Mattanovich
et al., 2012), the heterologous gdhA promoter in
Aspergillus species (Fleissner and Dersch, 2010), or viral promoters
in mammalian hosts (Wurm, 2004). It is also a common
strategy to develop synthetic promoters (Dehli et al.,
2012; Vogl et al., 2013; Fleissner and Dersch, 2010). Alternatively,
one can employ strong inducible promoters (3A2) to
have a biphasic process (Waegeman and Soetaert, 2011;
Fleissner and Dersch, 2010), for instance methanol-inducible
gene expression in the methylotrophic yeast P. pastoris (Mattanovich
et al., 2012; Damasceno et al., 2012). Reviews with
particularly good overviews of promoters for specific systems
are available (Celik and Calık, 2011; Fleissner and Dersch,
2010). A complementary strategy to the use of strong promoters
is the expression from high-copy plasmids (Rosano
and Ceccarelli, 2014) or multigene insertions (Westwood
et al., 2010; Wurm, 2004; Damasceno et al., 2012). Other transcriptional
elements such as enhancers, transcription factor
binding, and chromosomal elements should be considered
dependent on expression systems (Liu et al., 2013; Fleissner
and Dersch, 2010; Westwood et al., 2010).
3B1-2. Gene Fusion for Enhanced Secretion: For proteins with
no inherent secretion signal, the gene sequence requires
engineering to facilitate secretion of the protein. The predominant
way is the addition of a signal peptide/secretion leader
signal (3B1). This can also be applied to substitute the original
signal peptide for improved secretion in the host. For the
major hosts, efficient signal peptides are known from native
secreted proteins, e.g., alpha-mating factor or acid phosphatase
in yeasts (Mattanovich et al., 2012; Damasceno et al.,
2012) or leader sequences from secreted proteins in Aspergillus
(Fleissner and Dersch, 2010) or bacteria (Liu et al.,
2013). In some combinations of host and protein, this may
not be sufficient; in which case, the gene of interest is fused
with the sequence for a carrier protein (3B2), which then has
the effect of ushering the protein out of the cell. One example
is the production of animal proteins in Aspergillus species,
where a successful strategy for bovine chymosin production
was fusion with the glucoamylase gene. This approach has
since then become a preferred method (Ward et al., 1990;
Ward, 2011; Fleissner and Dersch, 2010).
3C. Stability of Heterologous Gene Transcripts: Most eukaryotic
genes contain introns. In many cases, their removal
from the transcript is necessary to generate a functional
gene product due to differences in (or absence of) splicing
Figure 4. Overview of Generic Engineering Strategies for Expression of Proteins in a Eukaryotic Host Cell
The inserted gene and its derived mRNA and polypeptide are marked in green. Generic engineering strategies are marked in blue and annotated as 3A–3J
(described in the main text).
Cell Systems 4, March 22, 2017 269
Cell Systems
Review
machinery between species (Hamann and Lange, 2006; Innis
et al., 1985). In higher eukaryotic systems a single intron early
in the transcript or in the promoter can, however, successfully
enhance stability of mRNA and increase the final product titer
(Borkovich et al., 2004). In many cases, codon optimization of
heterologous transcripts are often needed due to incompatibility
between the host and the protein codons, e.g., use of
rare codons or difference in stop codons. In general, the
half-life of a heterologous transcript might be different from
related transcripts of the host. In a bacterial system, the
importance of terminators and 30
UTR regions to transcript
stability has been well established (Cambray et al., 2013;
Pfleger et al., 2006; Curran et al., 2013). Often changing natural
or adding new structures, e.g., hairpin structures, to the
ends of transcripts, have been shown in bacteria to accumulate
mRNA and increase product formation (Hienonen et al.,
2007). In yeast and fungal systems, recent studies show
that changing a terminator can effectively optimize the
transcript stability and increase the product titer (Curran
et al., 2013).
3D1-2. Improved Translocation to the Endoplasmic Reticulum:
The induced pressure on the secretion machinery creates
numerous rate-limiting steps. The first is already at the
entrance of the secretion pathway through translocation
(3D1). A successful approach for several systems is overexpression
of signal peptidases cleaving the signal peptide by
entrance to the endoplasmic reticulum (ER) (Meta et al.,
2009; van Dijl et al., 1991; Ailor et al., 1999). Insufficient
amount of proteolytic cleavage enzymes may also be limiting
for secreted proteins with precursor domains (3D2). An
example is for therapeutic protein produced in CHO cells,
where overexpression of the cleaving enzyme PACE, increase
the secretion capability for several different proteins
(Sathyamurthy et al., 2012).
3E1-2. Protein Secretion Stress Engineering: It is generally
found that overexpression of proteins induces protein secretion
stress to some degree, which decreases productivity and
overall cell fitness (Lubertozzi and Keasling, 2009; Gasser
et al., 2008; Schro¨ der, 2008). One generally applied strategy
is the overexpression of chaperones (3E1). This strategy has
been proven to be successful in several studies in a multitude
of systems: E. coli (Waegeman and Soetaert, 2011; Gasser
et al., 2008; Rosano and Ceccarelli, 2014), other bacteria
(Gasser et al., 2008), yeasts (Mattanovich et al., 2012; Gasser
et al., 2008), fungi (Ward, 2011; Fleissner and Dersch, 2010),
and CHO cells (Ailor and Betenbaugh, 1998; Josse´ et al.,
2012; Pybus et al., 2013). It has also been broadly successful
in regulating global activators of the ER or the unfolded
protein response (3E2), in bacteria (Gasser et al., 2008),
S. cerevisiae (Valkonen et al., 2003b; Mattanovich et al.,
2012; Calfon et al., 2002), in A. niger var. awamori (Valkonen
et al., 2003a; Carvalho et al., 2012; Fleissner and Dersch,
2010), and in mammalian cells (Ohya et al., 2008; Tigges
and Fussenegger, 2006; Ku et al., 2008).
3F. Engineering the Post-translational Modification Machinery:
In some cases, the bottlenecks are in the formation of
disulfide bridges (Schro¨ der, 2008). This has been a problem
in E. coli in particular (de Marco, 2009), but proteins involved
in disulfide bridge formation have been seen to be limiting in
many cases, as seen by the positive effect of protein disulfide
isomerase in many other organisms, such as several yeasts,
Aspergillus (Gasser et al., 2008; Fleissner and Dersch, 2010),
and CHO (Borth et al., 2005; Davis et al., 2000; Mohan
et al., 2007).
3G. Improved Vesicle Trafficking: Another rate-limiting step is
the vesicle trafficking between ER-Golgi and Golgi-membrane.
Overexpression of SNAREs and their key regulators
can stimulate vesicular trafficking in yeast and enhance heterologous
protein secretion (Hou et al., 2012; Ruohonen et al.,
1997). Vacuolar protein sorting is complex, illustrated by
disruption of the vacuolar protein sorting receptor, Vsp10p,
which has a positive impact on secreted protein in both filamentous
fungi and yeast (Yoon et al., 2010; Idiris et al., 2010).
3H. Protein Glycosylation Engineering: This discipline does
not directly aim to improve production rate or titer of the product,
but instead addresses protein quality, in the form of protein
glycosylation. This has two branches, one where it is
sought to optimize the native protein glycosylation, and one
where the host organism does not have the required protein
glycosylation features, and these are engineered into the
cell factory (Mattanovich et al., 2012; Hossler, 2012; Hossler
et al., 2009; Andersen et al., 2011; Vogl et al., 2013). E. coli,
like most prokaryotes, does not have native protein glycosylation,
but genes from other prokaryotes with protein glycosylation
have successfully been engineered into the host (Waegeman
and Soetaert, 2011). Protein glycosylation has also
been engineered in filamentous fungi (Ward, 2011). A very
ambitious example is the expression of major parts of the
human glycosylation pathway in P. pastoris (Li et al., 2006;
Hamilton et al., 2006; Choi et al., 2003; Damasceno et al.,
2012), a technology later acquired by Merck (Walsh, 2010).
3I. Protease Deletions: The deletion of extracellular proteases
has been pursued in many systems with significant effects
(Ward, 2011; Fleissner and Dersch, 2010). Examples include
the deletion of all 25 known proteases in E. coli (Meerman and
Georgiou, 1994), S. cerevisiae (Tyo et al., 2014), and the deletion
of five proteases in A. oryzae (Jin et al., 2007). Another
strategy, with effects in several Aspergillus species, has
been the identification and deletion of a global regulator of
protease expression, PrtT. Deletion of this gene eliminates
nearly all protease activity (Punt et al., 2008; Fleissner and
Dersch, 2010).
3J. By-product Removal: A final general strategy, in all species,
is the removal of by-products with negative effect on protein
production. Examples include removal of acetate biosynthesis
in E. coli (Waegeman and Soetaert, 2011), oxalic acid
production in Aspergilli (Li et al., 2013), or lactate production
in CHO cells (Kim and Lee, 2007). All of these have been shown
to improve product formation, growth characteristics or both.
Choosing a Strategy for Protein Expression
Contrary to the strategies for production of smaller compounds,
where the yield and titer of the product is the primary optimization
criterion, it is more difficult to define a generalized order of
engineering strategies for protein products. The main reason
is, that for some proteins, in particular pharmaceuticals, quality
is more important than quantity. In some cases, quantity even
has a detrimental effect on quality, as it may elicit stress
270 Cell Systems 4, March 22, 2017
Cell Systems
Review
responses in the cell which degrade the product (Wurm, 2004;
Damasceno et al., 2012; Hossler, 2012; Hossler et al., 2009).
Therefore, we propose two strategies, one for optimizing titers
(e.g., for enzymes and bulk products), and one for products
focused on quality (i.e., pharmaceuticals).
Strategy A: Optimal Expression of the Heterologous Gene.
Here, multiple initiatives can be used separately, sequentially,
or in parallel, to find the strategy that is the most efficient. The
following six actions are thus applicable only in the cases where
that factor is limiting. In general, actions 3A–3C in particular are
relatively consistently applied in successful studies.
i. Selection and engineering of optimal promoters (3A) are
vital for high levels of transcript, so this does not
become a limiting factor.
ii. Engineering of the heterologous gene in regard to codon
compatibility and optimality and removal or adaptation
of introns (3C) are also found in nearly all studies.
iii. Selection and/or engineering of the secretion signal (3B)
is required to ensure secretion of the product, and
appropriate trafficking of the peptide chain to the ER.
This can affect the production by severalfold.
iv. Protein secretion stress reduction (3E), in particular
regarding the formation of disulfide bridges, generally
increases product formation.
v. As is seen for small molecules (1D), removal of product
degradation improves productivity. For proteins, this is
solved by protease deletions (3I).
vi. Finally, it has been shown that engineering vesicle trafficking
(3G) and translocation to the ER (3D) increases
productivity. However, this is in only few cases, possibly
due to the complexity of engineering these processes. It
thus becomes difficult to evaluate how applicable this is
in general.
Strategy B: Protein Quality. For optimization of protein quality,
the strategy depends on which quality criterion is suboptimal in
the production process, and a first step should thus be the determination
of this. Here, analytical biochemistry will be the primary
tool, and thus not within the scope of this meta-review. Once it
has been established, one can apply one or more of the following
three engineering types:
d Protein glycosylation engineering (3H) is generally very
attractive for glycosylated biopharmaceuticals (Walsh,
2014; Ratner, 2014).
d Engineering disulfide bridge formation (3F) and protein
folding(3E)ingeneralcanhelpremoveerroneouslyfolded
protein and decrease protein folding-associated stress.
d Protease deletions (3I) are just as important for maintaining
protein quality as quantity.
In addition to the strategies of this section, one can also
consider adding strategies of the previous sections where
appropriate. In particular, by-product removal (3J) has been
demonstrated to be efficient.
Conclusions
Considering the breadth and depth of the strategies discussed
above, it is clear that the field of cell factory engineering as a whole
has come a long way. Through tens of thousands of studies, a
multitude of individual challenges have been solved across a
broad range of expression systems and diverse types of compounds.
New and interesting avenues are being opened, such
as expansion of the substrate range of E. coli turning it into a synthetic
methylotroph (M€uller et al., 2015), or achieving the biosynthesis
of caffeine and other methylxanthines in yeast from plant
biosynthetic genes (McKeague et al., 2016), or achieving biobased
nylon through large-scale engineering in C. glutamicum
(Kind et al., 2014). We are also now seeing engineering of a central
metabolism for increased protein production (Nocon et al., 2014).
It seems like there is no obvious limit to the possibilities in sight.
Furthermore, increasingly advanced work is being published,
opening the field up into the applications of synthetic biology.
This impacts small parts of the cell factory engineering, such
as improvements in synthetic promoters (Vogl et al., 2013) and,
at larger scale, the building of artificial pathways rather than using
‘‘simple’’ heterologous expression. Examples here include
the biosynthesis of gastrodin (Bai et al., 2016) and the impressive
feat of the Smolke Lab to biosynthesize opioids in S. cerevisiae
(Thodey et al., 2014; Galanie et al., 2015).
Another interesting development is the use of engineered consortia
of species for achieving particular activities and synergies
from using multiple species. A recent example employs bacterial
consortia for desulfurization of oil-based fuels (Martı´nez et al.,
2016), thus improving the quality.
Even so, there are still significant problems that need to be addressed.
Despite the extensive size of the toolbox of strategies
outlined above, it is still difficult to know a priori which modifications
are required for a specific combination of product and cell
factory. This is one of the main reasons why the development
time for new cell factories remains the largest bottleneck for
new bioproducts.
In order to move the field forward, these challenges must be
addressed in multiple ways. Currently we see a next major
step is to predict how cells change dynamically over the course
of cultivation. At the moment, the most successful modeling of
biological systems has been for steady state; which is often
not representative of the conditions in a bioreactor in a long
production process. Another brick in the wall will be the new
conceptual frameworks (e.g., systems biology or system metabolic
engineering), which are moving toward holistic design of
cell factories and biological networks (Nocon et al., 2014).
To achieve these goals, the importance of efficient genetic engineering
and genome-editing tools cannot be overstated. Every
time genetic engineering technologies have improved, so has
the sophistication of cell factory engineering. Synthetic biology
and genome-editing technologies such as CRISPR will accelerate
cell factory engineering as we know it (Jakociunas et al.,
2016), and they also promise to facilitate more-rapid tests of
new theories, permutations of solutions, and generally cell engineering
at a systems level.
In tandem, dynamic modeling, holistic design, synthetic
biology, and genome editing hold great promises for rational
design of biological systems.
AUTHOR CONTRIBUTIONS
All authors were a part of the literature analysis and wrote the manuscript.
Cell Systems 4, March 22, 2017 271
Cell Systems
Review
ACKNOWLEDGMENTS
The authors are indebted to past and present students on the Cell Factory Engineering
course at the Technical University of Denmark for giving inspiration
and motivation for this text. Furthermore, sage advice and valuable input from
several colleagues in writing this text is gratefully acknowledged. In particular,
the input from Ana Ley was highly appreciated. H.F. Kildegaard thanks the
Novo Nordisk Foundation for generous funding. M.R. Andersen is partially
funded by the Novo Nordisk Foundation (grant NNF13OC0004831), and the
Villum Foundation (grant VKR0234037).
REFERENCES
Ailor, E., and Betenbaugh, M.J. (1998). Overexpression of a cytosolic chaperone
to improve solubility and secretion of a recombinant IgG protein in insect
cells. Biotechnol. Bioeng. 58, 196–203.
Ailor, E., Pathmanathan, J., Jongbloed, J.D., and Betenbaugh, M.J. (1999).
A bacterial signal peptidase enhances processing of a recombinant single
chain antibody fragment in insect cells. Biochem. Biophys. Res. Commun.
255, 444–450.
Andersen, M.R., Nam, J.H., and Sharfstein, S.T. (2011). Protein glycosylation:
analysis, characterization, and engineering. In Encyclopedia of Industrial
Biotechnology, Bioprocess, Bioseparation, and Cell Technology, M.C. Flickinger,
ed. (John Wiley), pp. 1–50.
Anyaogu, D.C., and Mortensen, U.H. (2015). Heterologous production of
fungal secondary metabolites in Aspergilli. Front. Microbiol. 6, 77.
Asadollahi, M.A., Maury, J., Patil, K.R., Schalk, M., Clark, A., and Nielsen, J.
(2009). Enhancing sesquiterpene production in Saccharomyces cerevisiae
through in silico driven metabolic engineering. Metab. Eng. 11, 328–334.
Association of Manufacturers and Formulators of Enzyme Products, 2009. List
of commercial enzymes. http://www.amfep.org/content/list-enzymes.
Bai, Y., Yin, H., Bi, H., Zhuang, Y., Liu, T., and Ma, Y. (2016). De novo biosynthesis
of gastrodin in Escherichia coli. Metab. Eng. 35, 138–147.
Bailey, J.E. (1991). Toward a science of metabolic engineering. Science 252,
1668–1675.
Bailey, J.E., Sburlati, A., Hatzimanikatis, V., Lee, K., Renner, W.A., and Tsai,
P.S. (2002). Inverse metabolic engineering: a strategy for directed genetic engineering
of useful phenotypes. Biotechnol. Bioeng. 79, 568–579.
Baltz, R.H. (2016). Genetic manipulation of secondary metabolite biosynthesis
for improved production in Streptomyces and other actinomycetes. J. Ind.
Microbiol. Biotechnol. 43, 343–370.
Barnstable, C.J., Bodmer, W.F., Brown, G., Galfre, G., Milstein, C., Williams,
A.F., and Ziegler, A. (1978). Production of monoclonal antibodies to group A
erythrocytes, HLA and other human cell surface antigen-new tools for genetic
analysis. Cell 14, 9–18.
Bashor, C.J., Horwitz, A.A., Peisajovich, S.G., Lim, W.A., et al. (2010). Rewiring
cells: synthetic biology as a tool to interrogate the organizational principles of
living systems. Annu. Rev. Biophys. 39, 515–537.
Becker, J., Sch€afer, R., Kohlstedt, M., Harder, B.J., Borchert, N.S., Sto¨ veken,
N., Bremer, E., and Wittmann, C. (2013). Systems metabolic engineering of
Corynebacterium glutamicum for production of the chemical chaperone ectoine.
Microb. Cell Fact. 12, 110.
Behnsen, J., Deriu, E., Sassone-Corsi, M., and Raffatellu, M. (2013). Probiotics:
properties, examples, and specific applications. Cold Spring Harb. Perspect.
Med. 3, a010074.
Bekiesch, P., Basitta, P., and Apel, A.K. (2016). Challenges in the heterologous
production of antibiotics in Streptomyces. Arch. Pharm. (Weinheim) 349,
594–601.
Berkmen, M. (2012). Production of disulfide-bonded proteins in Escherichia
coli. Protein Expr. Purif. 82, 240–251.
Borkovich, K.A., Alex, L.A., Yarden, O., Freitag, M., Turner, G.E., Read, N.D.,
Seiler, S., Bell-Pedersen, D., Paietta, J., Plesofsky, N., et al. (2004). Lessons
from the genome sequence of Neurospora crassa: tracing the path from
genomic blueprint to multicellular organism. Microbiol. Mol. Biol. Rev.
68, 1–108.
Borodina, I., Siebring, J., Zhang, J., Smith, C.P., van Keulen, G., Dijkhuizen, L.,
and Nielsen, J. (2008). Antibiotic overproduction in Streptomyces coelicolor
A3(2) mediated by phosphofructokinase deletion. J. Biol. Chem. 283,
25186–25199.
Borth, N., Mattanovich, D., Kunert, R., and Katinger, H. (2005). Effect of
increased expression of protein disulfide isomerase and heavy chain binding
protein on antibody secretion in a recombinant CHO cell line. Biotechnol.
Prog. 21, 106–111.
Burgard, A.P., Pharkya, P., and Maranas, C.D. (2003). Optknock: a bilevel programming
framework for identifying gene knockout strategies for microbial
strain optimization. Biotechnol. Bioeng. 84, 647–657.
Calfon, M., Zeng, H., Urano, F., Till, J.H., Hubbard, S.R., Harding, H.P., Clark,
S.G., and Ron, D. (2002). IRE1 couples endoplasmic reticulum load to secretory
capacity by processing the XBP-1 mRNA. Nature 415, 92–96.
Cambray, G., Guimaraes, J.C., Mutalik, V.K., Lam, C., Mai, Q.A., Thimmaiah,
T., Carothers, J.M., Arkin, A.P., and Endy, D. (2013). Measurement and
modeling of intrinsic transcription terminators. Nucleic Acids Res. 41,
5139–5148.
Carvalho, N.D., Jørgensen, T.R., Arentshorst, M., Nitsche, B.M., van den
Hondel, C.A., Archer, D.B., and Ram, A.F. (2012). Genome-wide expression
analysis upon constitutive activation of the HacA bZIP transcription factor in
Aspergillus niger reveals a coordinated cellular response to counteract ER
stress. BMC Genomics 13, 350.
Cavallaro, G., Decaria, L., and Rosato, A. (2008). Genome-based analysis of
heme biosynthesis and uptake in prokaryotic systems. J. Proteome Res. 7,
4946–4954.
Celik, E., and Calık, P. (2011). Production of recombinant proteins by yeast
cells. Biotechnol. Adv. 30, 1108–1118.
Chemier, J.A., Fowler, Z.L., Koffas, M.A., and Leonard, E. (2009). Trends in microbial
synthesis of natural products and biofuels. Adv. Enzymol. Relat. Areas
Mol. Biol. 76, 151–217.
Chen, X., Dong, X., Wang, Y., Zhao, Z., and Liu, L. (2015). Mitochondrial engineering
of the TCA cycle for fumarate production. Metab. Eng. 31, 62.
Choi, B.-K., Bobrowicz, P., Davidson, R.C., Hamilton, S.R., Kung, D.H., Li, H.,
Miele, R.G., Nett, J.H., Wildt, S., and Gerngross, T.U. (2003). Use of combinatorial
genetic libraries to humanize N-linked glycosylation in the yeast Pichia
pastoris. Proc. Natl. Acad. Sci. USA 100, 5022–5027.
Cui, Y.-Y., Ling, C., Zhang, Y.Y., Huang, J., and Liu, J.Z. (2014). Production of
shikimic acid from Escherichia coli through chemically inducible chromosomal
evolution and cofactor metabolic engineering. Microb. Cell Fact. 13, 21.
Curran, K.A., Karim, A.S., Gupta, A., and Alper, H.S. (2013). Use of expressionenhancing
terminators in Saccharomyces cerevisiae to increase mRNA
half-life and improve gene expression control for metabolic engineering applications.
Metab. Eng. 19, 88–97.
Damasceno, L.M., Huang, C.-J.J., and Batt, C.A. (2012). Protein secretion
in Pichia pastoris and advances in protein production. Appl. Microbiol. Biotechnol.
93, 31–39.
Davis, R., Schooley, K., Rasmussen, B., Thomas, J., and Reddy, P. (2000).
Effect of PDI overexpression on recombinant protein secretion in CHO cells.
Biotechnol. Prog. 16, 736–743.
de Marco, A. (2009). Strategies for successful recombinant expression of disulfide
bond-dependent proteins in Escherichia coli. Microb. Cell Fact. 8, 26.
Dehli, T., Solem, C., and Jensen, P.R. (2012). Tunable promoters in synthetic
and systems biology. Subcell Biochem. 64, 181–201.
Dueber, J.E., Wu, G.C., Malmirchegini, G.R., Moon, T.S., Petzold, C.J., Ullal,
A.V., Prather, K.L., and Keasling, J.D. (2009). Synthetic protein scaffolds provide
modular control over metabolic flux. Nat. Biotechnol. 27, 753–759.
Dunlop, M.J., Dossani, Z.Y., Szmidt, H.L., Chu, H.C., Lee, T.S., Keasling, J.D.,
Hadi, M.Z., and Mukhopadhyay, A. (2011). Engineering microbial biofuel tolerance
and export using efflux pumps. Mol. Syst. Biol. 7, 487.
Edwards, A.M., Arrowsmith, C.H., Christendat, D., Dharamsi, A., Friesen, J.D.,
Greenblatt, J.F., and Vedadi, M. (2000). Protein production: feeding the crystallographers
and NMR spectroscopists. Nat. Struct. Biol. 7, 970–972.
272 Cell Systems 4, March 22, 2017
Cell Systems
Review
Fisher, A.K., Freedman, B.G., Bevan, D.R., and Senger, R.S. (2014). A review
of metabolic and enzymatic engineering strategies for designing and optimizing
performance of microbial cell factories. Comput. Struct. Biotechnol.
J. 11, 91–99.
Fleissner, A., and Dersch, P. (2010). Expression and export: recombinant protein
production systems for Aspergillus. Appl. Microbiol. Biotechnol. 87,
1255–1270.
Franken, A.C.W., Lokman, B.C., Ram, A.F., Punt, P.J., van den Hondel, C.A.,
and de Weert, S. (2011). Heme biosynthesis and its regulation: towards understanding
and improvement of heme biosynthesis in filamentous fungi. Appl.
Microbiol. Biotechnol. 91, 447–460.
Galanie, S., Thodey, K., Trenchard, I.J., Filsinger Interrante, M., and Smolke,
C.D. (2015). Complete biosynthesis of opioids in yeast. Science 349,
1095–1100.
Gasser, B., Saloheimo, M., Rinas, U., Dragosits, M., Rodrı´guez-Carmona, E.,
Baumann, K., Giuliani, M., Parrilli, E., Branduardi, P., Lang, C., et al. (2008).
Protein folding and conformational stress in microbial cells producing recombinant
proteins: a host comparative overview. Microb. Cell Fact. 7, 11.
Ghosh, A., Zhao, H., and Price, N.D. (2011). Genome-scale consequences of
cofactor balancing in engineered pentose utilization pathways in Saccharomyces
cerevisiae. PLoS One 6, e27316.
Hamann, T., and Lange, L. (2006). Discovery, cloning and heterologous
expression of secreted potato proteins reveal erroneous pre-mRNA splicing
in Aspergillus oryzae. J. Biotechnol. 126, 265–276.
Hamilton, S.R., Davidson, R.C., Sethuraman, N., Nett, J.H., Jiang, Y., Rios, S.,
Bobrowicz, P., Stadheim, T.A., Li, H., Choi, B.K., et al. (2006). Humanization of
yeast to produce complex terminally sialylated glycoproteins. Science 313,
1441–1443.
Hienonen, E., Romantschuk, M., Fenel, F., and Taira, S. (2007). Transcript
stabilization by mRNA sequences from hrpA of Pseudomonas syringae.
J. Biotechnol. 128, 258–267.
Hossler, P. (2012). Protein glycosylation control in mammalian cell culture:
past precedents and contemporary prospects. Adv. Biochem. Eng. Biotechnol.
127, 187–219.
Hossler, P., Khattak, S.F., and Li, Z.J. (2009). Optimal and consistent protein
glycosylation in mammalian cell culture. Glycobiology 19, 936–949.
Hou, J., Tyo, K., Liu, Z., Petranovic, D., and Nielsen, J. (2012). Engineering of
vesicle trafficking improves heterologous protein secretion in Saccharomyces
cerevisiae. Metab. Eng. 14, 120–127.
Hsu, P.D., Lander, E.S., and Zhang, F. (2014). Development and applications
of CRISPR-Cas9 for genome engineering. Cell 157, 1262–1278.
Hwang, K.-S., Kim, H.U., Charusanti, P., Palsson, B.Ø., and Lee, S.Y. (2014).
Systems biology and biotechnology of Streptomyces species for the production
of secondary metabolites. Biotechnol. Adv. 32, 255–268.
Idiris, A., Tohda, H., Sasaki, M., Okada, K., Kumagai, H., Giga-Hama, Y., and
Takegawa, K. (2010). Enhanced protein secretion from multiprotease-deficient
fission yeast by modification of its vacuolar protein sorting pathway. Appl. Microbiol.
Biotechnol. 85, 667–677.
Innis, M.A., Holland, M.J., McCabe, P.C., Cole, G.E., Wittman, V.P., Tal, R.,
Watt, K.W., Gelfand, D.H., Holland, J.P., and Meade, J.H. (1985). Expression,
glycosylation, and secretion of an Aspergillus glucoamylase by Saccharomyces
cerevisiae. Science 228, 21–26.
Jakociunas, T., Jensen, M.K., and Keasling, J.D. (2016). CRISPR/Cas9 advances
engineering of microbial cell factories. Metab. Eng. 34, 44–59.
Jin, F.J., Watanabe, T., Juvvadi, P.R., Maruyama, J., Arioka, M., and Kitamoto,
K. (2007). Double disruption of the proteinase genes, tppA and pepE, increases
the production level of human lysozyme by Aspergillus oryzae. Appl.
Microbiol. Biotechnol. 76, 1059–1068.
de Jongh, W.A., and Nielsen, J. (2008). Enhanced citrate production through
gene insertion in Aspergillus niger. Metab. Eng. 10, 87–96.
Josse´ , L., Smales, C.M., and Tuite, M.F. (2012). Engineering the chaperone
network of CHO cells for optimal recombinant protein production and authenticity.
Methods Mol. Biol. 824, 595–608.
Keasling, J.D. (2008). Synthetic biology for synthetic chemistry. ACS Chem.
Biol. 3, 64–76.
Keasling, J.D. (2012). Synthetic biology and the development of tools for metabolic
engineering. Metab. Eng. 14, 189–195.
Kiel, C., Yus, E., and Serrano, L. (2010). Engineering signal transduction pathways.
Cell 140, 33–47.
Kim, S.H., and Lee, G.M. (2007). Down-regulation of lactate dehydrogenase-A
by siRNAs for reduced lactic acid formation of Chinese hamster ovary cells
producing thrombopoietin. Appl. Microbiol. Biotechnol. 74, 152–159.
Kind, S., Jeong, W.K., Schro¨ der, H., and Wittmann, C. (2010). Systems-wide
metabolic pathway engineering in Corynebacterium glutamicum for bio-based
production of diaminopentane. Metab. Eng. 12, 341–351.
Kind, S., Neubauer, S., Becker, J., Yamamoto, M., Vo¨ lkert, M., Abendroth, Gv,
Zelder, O., and Wittmann, C. (2014). From zero to hero – production of biobased
nylon from renewable resources using engineered Corynebacterium
glutamicum. Metab. Eng. 25, 113–123.
Ku, S.C.Y., Ng, D.T., Yap, M.G., and Chao, S.H. (2008). Effects of overexpression
of X-box binding protein 1 on recombinant protein production in Chinese
hamster ovary and NS0 myeloma cells. Biotechnol. Bioeng. 99, 155–164.
Lee, S.Y., and Kim, H.U. (2015). Systems strategies for developing industrial
microbial strains. Nat. Biotechnol. 33, 1061–1072.
Lee, J.H., Lee, D.E., Lee, B.U., and Kim, H.S. (2003). Global analyses of transcriptomes
and proteomes of a parent strain and an L-threonine-overproducing
mutant strain. J. Bacteriol. 185, 5442–5451.
Lee, K.H., Park, J.H., Kim, T.Y., Kim, H.U., and Lee, S.Y. (2007). Systems metabolic
engineering of Escherichia coli for L-threonine production. Mol. Syst.
Biol. 3, 149.
Lee, J.W., Na, D., Park, J.M., Lee, J., Choi, S., and Lee, S.Y. (2012). Systems
metabolic engineering of microorganisms for natural and non-natural chemicals.
Nat. Chem. Biol. 8, 536–546.
Li, H., Sethuraman, N., Stadheim, T.A., Zha, D., Prinz, B., Ballew, N., Bobrowicz,
P., Choi, B.K., Cook, W.J., Cukan, M., et al. (2006). Optimization of
humanized IgGs in glycoengineered Pichia pastoris. Nat. Biotechnol. 24,
210–215.
Li, A., Pfelzer, N., Zuijderwijk, R., Brickwedde, A., van Zeijl, C., and Punt, P.
(2013). Reduced by-product formation and modified oxygen availability
improve itaconic acid production in Aspergillus niger. Appl. Microbiol. Biotechnol.
97, 3901–3911.
Liu, L., Yang, H., Shin, H.D., Li, J., Du, G., and Chen, J. (2013). Recent advances
in recombinant protein expression by Corynebacterium, Brevibacterium,
and Streptomyces: from transcription and translation regulation to
secretion pathway selection. Appl. Microbiol. Biotechnol. 97, 9597–9608.
Lubertozzi, D., and Keasling, J.D. (2009). Developing Aspergillus as a host for
heterologous expression. Biotechnol. Adv. 27, 53–75.
Martin, V.J.J., Pitera, D.J., Withers, S.T., Newman, J.D., and Keasling, J.D.
(2003). Engineering a mevalonate pathway in Escherichia coli for production
of terpenoids. Nat. Biotechnol. 21, 796–802.
Martı´nez, I., Mohamed Mel, S., Rozas, D., Garcı´a, J.L., and Dı´az, E. (2016). Engineering
synthetic bacterial consortia for enhanced desulfurization and revalorization
of oil sulfur compounds. Metab. Eng. 35, 46–54.
Mattanovich, D., Branduardi, P., Dato, L., Gasser, B., Sauer, M., and Porro, D.
(2012). Recombinant protein production in yeasts. Methods Mol. Biol. 824,
329–358.
McKeague, M., Wang, Y.H., Cravens, A., Win, M.N., and Smolke, C.D. (2016).
Engineering a microbial platform for de novo biosynthesis of diverse methylxanthines.
Metab. Eng. 38, 191–203.
Meerman, H.J., and Georgiou, G. (1994). Construction and characterization of
a set of E. coli strains deficient in all known loci affecting the proteolytic stability
of secreted recombinant proteins. Biotechnology (N Y) 12, 1107–1110.
Meta, A., Nakatake, H., Imamura, T., Nozaki, C., and Sugimura, K. (2009).
High-yield production and characterization of biologically active recombinant
aprotinin expressed in Saccharomyces cerevisiae. Protein Expr. Purif.
66, 22–27.
Cell Systems 4, March 22, 2017 273
Cell Systems
Review
Mohan, C., Park, S.H., Chung, J.Y., and Lee, G.M. (2007). Effect of doxycycline-regulated
protein disulfide isomerase expression on the specific productivity
of recombinant CHO cells: thrombopoietin and antibody. Biotechnol.
Bioeng. 98, 611–615.
Molna´ r, I., Jungmann, V., Stege, J., Trefzer, A., and Pachlatko, J.P. (2006).
Biocatalytic conversion of avermectin into 400
-oxo-avermectin: discovery,
characterization, heterologous expression and specificity improvement of
the cytochrome P450 enzyme. Biochem. Soc. Trans. 34, 1236–1240.
M€uller, J.E.N., Meyer, F., Litsanov, B., Kiefer, P., Potthoff, E., Heux, S., Quax,
W.J., Wendisch, V.F., Brautaset, T., Portais, J.C., et al. (2015). Engineering
Escherichia coli for methanol conversion. Metab. Eng. 28, 190–201.
Nerem, R.M. (1991). Cellular engineering. Ann. Biomed. Eng. 19, 529–545.
Nielsen, J. (1998). Metabolic engineering: techniques for analysis of targets for
genetic manipulations. Biotechnol. Bioeng. 58, 125–132.
Nielsen, J. (2014). Synthetic biology for engineering acetyl coenzyme a metabolism
in yeast. mBio 5, e02153.
Nielsen, M.T., Nielsen, J.B., Anyaogu, D.C., Holm, D.K., Nielsen, K.F., Larsen,
T.O., and Mortensen, U.H. (2013). Heterologous reconstitution of the intact
geodin gene cluster in Aspergillus nidulans through a simple and versatile
PCR based approach. PLoS One 8, e72871.
Nieuwenhuizen, P.J., and Lyon, D. (2011). Anticipating opportunities in industrial
biotechnology: sizing the market and growth scenarios. J. Commer. Biotechnol.
17, 159–164.
Nocon, J., Steiger, M.G., Pfeffer, M., Sohn, S.B., Kim, T.Y., Maurer, M.,
Rußmayer, H., Pfl€ugl, S., Ask, M., Haberhauer-Troyer, C., et al. (2014). Model
based engineering of Pichia pastoris central metabolism enhances recombinant
protein production. Metab. Eng. 24, 129–138.
Ohya, T., Hayashi, T., Kiyama, E., Nishii, H., Miki, H., Kobayashi, K., Honda, K.,
Omasa, T., and Ohtake, H. (2008). Improved production of recombinant human
antithrombin III in Chinese hamster ovary cells by {ATF}4 overexpression.
Biotechnol. Bioeng. 100, 317–324.
Ostergaard, S., Olsson, L., Johnston, M., and Nielsen, J. (2000). Increasing
galactose consumption by Saccharomyces cerevisiae through metabolic engineering
of the GAL gene regulatory network. Nat. Biotechnol. 18, 1283–1286.
Park, J.H., Oh, J.E., Lee, K.H., Kim, J.Y., and Lee, S.Y. (2012). Rational design
of Escherichia coli for L-isoleucine production. ACS Synth. Biol. 1, 532–540.
Pfeifer, B.A., and Khosla, C. (2001). Biosynthesis of polyketides in heterologous
hosts. Microbiol. Mol. Biol. Rev. 65, 106–118.
Pfleger, B.F., Pitera, D.J., Smolke, C.D., and Keasling, J.D. (2006). Combinatorial
engineering of intergenic regions in operons tunes expression of multiple
genes. Nat. Biotechnol. 24, 1027–1032.
Pfleger, B.F., Gossing, M., and Nielsen, J. (2015). Metabolic engineering strategies
for microbial synthesis of oleochemicals. Metab. Eng. 29, 1–11.
Pharkya, P., and Maranas, C.D. (2006). An optimization framework for identifying
reaction activation/inhibition or elimination candidates for overproduction
in microbial systems. Metab. Eng. 8, 1–13.
Pickens, L.B., Tang, Y., and Chooi, Y.H. (2011). Metabolic engineering for the
production of natural products. Annu. Rev. Chem. Biomol. Eng. 2, 211–236.
Punt, P.J., Schuren, F.H., Lehmbeck, J., Christensen, T., Hjort, C., and van den
Hondel, C.A. (2008). Characterization of the Aspergillus niger prtT, a unique
regulator of extracellular protease encoding genes. Fungal Genet. Biol. 45,
1591–1599.
Pybus, L.P., Dean, G., West, N.R., Smith, A., Daramola, O., Field, R., Wilkinson,
S.J., and James, D.C. (2013). Model-directed engineering of ‘‘difficultto-express’’
monoclonal antibody production by Chinese hamster ovary cells.
Biotechnol. Bioeng. 111, 372–385.
Qi, K., Zhong, J.J., and Xia, X.X. (2014). Triggering respirofermentative metabolism
in the crabtree-negative yeast Pichia guilliermondii by disrupting the
CAT8 gene. Appl. Environ. Microbiol. 80, 3879–3887.
Qian, Z.G., Xia, X.X., and Lee, S.Y. (2011). Metabolic engineering of Escherichia
coli for the production of cadaverine: a five carbon diamine. Biotechnol.
Bioeng. 108, 93–103.
Ranganathan, S., Suthers, P.F., and Maranas, C.D. (2010). OptForce: an optimization
procedure for identifying all genetic manipulations leading to targeted
overproductions. PLoS Comput. Biol. 6, e1000744.
Ratner, M. (2014). Genentech’s glyco-engineered antibody to succeed Rituxan.
Nat. Biotechnol. 32, 6–7.
Ribela, M.T., Bianco, A.C., and Bartolini, P. (1996). The use of recombinant human
thyrotropin produced by Chinese hamster ovary cells for the preparation
of immunoassay reagents. J. Clin. Endocrinol. Metab. 81, 249–256.
Rodrigues, A.L., Trachtmann, N., Becker, J., Lohanatha, A.F., Blotenberg, J.,
Bolten, C.J., Korneli, C., de Souza Lima, A.O., Porto, L.M., Sprenger, G.A.,
et al. (2013). Systems metabolic engineering of Escherichia coli for production
of the antitumor drugs violacein and deoxyviolacein. Metab. Eng. 20, 29–41.
Rodrigues, A.L., Becker, J., de Souza Lima, A.O., Porto, L.M., and Wittmann,
C. (2014). Systems metabolic engineering of Escherichia coli for gram scale
production of the antitumor drug deoxyviolacein from glycerol. Biotechnol.
Bioeng. 111, 2280–2289.
Rosano, G.L., and Ceccarelli, E.A. (2014). Recombinant protein expression in
Escherichia coli: advances and challenges. Front. Microbiol. 5, 172.
Ruohonen, L., Toikkanen, J., Tieaho, V., Outola, M., Soderlund, H., and Keranen,
S. (1997). Enhancement of protein secretion in Saccharomyces cerevisiae
by overproduction of Sso protein, a late-acting component of the secretory
machinery. Yeast 13, 337–351.
Sathyamurthy, M., Lee, J.S., Park, J.H., Kim, Y.J., Jeong, J.Y., Jang, J.W., and
Lee, G.M. (2012). Overexpression of PACEsol improves BMP-7 processing in
recombinant CHO cells. J. Biotechnol. 164, 336–339.
Schro¨ der, M. (2008). Engineering eukaryotic protein factories. Biotechnol. Lett.
30, 187–196.
Siddiqui, M.S., Thodey, K., Trenchard, I., and Smolke, C.D. (2012). Advancing
secondary metabolite biosynthesis in yeast with synthetic biology tools. FEMS
Yeast Res. 12, 144–170.
Stephanopoulos, G., and Vallino, J.J. (1991). Network rigidity and metabolic
engineering in metabolite overproduction. Science 252, 1675–1681.
Thodey, K., Galanie, S., and Smolke, C.D. (2014). A microbial biomanufacturing
platform for natural and semisynthetic opioids. Nat. Chem.
Biol. 10, 1–10.
Tigges, M., and Fussenegger, M. (2006). Xbp1-based engineering of secretory
capacity enhances the productivity of Chinese hamster ovary cells. Metab.
Eng. 8, 264–272.
Trosset, J.Y., and Carbonell, P. (2015). Synthetic biology for pharmaceutical
drug discovery. Drug Des. Dev. Ther. 9, 6285–6302.
Tyo, K.E., Liu, Z., Magnusson, Y., Petranovic, D., and Nielsen, J. (2014). Impact
of protein uptake and degradation on recombinant protein secretion in yeast.
Appl. Microbiol. Biotechnol. 98, 7149–7159.
Valkonen, M., Ward, M., Wang, H., Penttil€a, M., and Saloheimo, M. (2003a).
Improvement of foreign-protein production in Aspergillus niger var. awamori
by constitutive induction of the unfolded-protein response. Appl. Environ. Microbiol.
69, 6979–6986.
Valkonen, M., Penttil€a, M., and Saloheimo, M. (2003b). Effects of inactivation
and constitutive expression of the unfolded- protein response pathway on protein
production in the yeast Saccharomyces cerevisiae. Appl. Environ. Microbiol.
69, 2065–2072.
van Dijl, J.M., de Jong, A., Smith, H., Bron, S., and Venema, G. (1991). Signal
peptidase I overproduction results in increased efficiencies of export and
maturation of hybrid secretory proteins in Escherichia coli. Mol. Gen. Genet.
227, 40–48.
van Rossum, H.M., Kozak, B.U., Pronk, J.T., and van Maris, A.J. (2016).
Engineering cytosolic acetyl-coenzyme A supply in Saccharomyces cerevisiae:
pathway stoichiometry, free-energy conservation and redox-cofactor
balancing. Metab. Eng. 36, 99–115.
Vogl, T., Hartner, F.S., and Glieder, A. (2013). New opportunities by synthetic
biology for biopharmaceutical production in Pichia pastoris. Curr. Opin. Biotechnol.
24, 1094–1101.
274 Cell Systems 4, March 22, 2017
Cell Systems
Review
Waegeman, H., and Soetaert, W. (2011). Increasing recombinant protein production
in Escherichia coli through metabolic and genetic engineering. J. Ind.
Microbiol. Biotechnol. 38, 1891–1910.
Walsh, G. (2010). Biopharmaceutical benchmarks 2010. Nat. Biotechnol. 28,
917–924.
Walsh, G. (2014). Biopharmaceutical benchmarks 2014. Nat. Biotechnol. 32,
992–1000.
Wang, Y., and Yu, O. (2012). Synthetic scaffolds increased resveratrol biosynthesis
in engineered yeast cells. J. Biotechnol. 157, 258–260.
Wang, J., Liu, W., Ding, W., Zhang, G., and Liu, J. (2013). Increasing ethanol
titer and yield in a gpd1D gpd2D strain by simultaneous overexpression
of GLT1 and STL1 in Saccharomyces cerevisiae. Biotechnol. Lett. 35,
1859–1864.
Ward, O.P. (2011). Production of recombinant proteins by filamentous fungi.
Biotechnol. Adv. 30, 1119–1139.
Ward, M., Wilson, L.J., Kodama, K.H., Rey, M.W., and Berka, R.M. (1990).
Improved production of chymosin in Aspergillus by expression as a glucoamylase-chymosin
fusion. Biotechnology (N Y) 8, 435–440.
Weber, T., Blin, K., Duddela, S., Krug, D., Kim, H.U., Bruccoleri, R., Lee, S.Y.,
Fischbach, M.A., M€uller, R., Wohlleben, W., et al. (2015). antiSMASH 3.0-a
comprehensive resource for the genome mining of biosynthetic gene clusters.
Nucleic Acids Res. 43, W237–W243.
Westwood, A.D., Rowe, D.A., and Clarke, H.R.G. (2010). Improved recombinant
protein yield using a codon deoptimized DHFR selectable marker in a
CHEF1 expression plasmid. Biotechnol. Prog. 26, 1558–1566.
Wu, J., Du, G., Zhou, J., and Chen, J. (2014). Systems metabolic engineering of
microorganisms to achieve large-scale production of flavonoid scaffolds.
J. Biotechnol. 188, 72–80.
Wurm, F.M. (2004). Production of recombinant protein therapeutics in cultivated
mammalian cells. Nat. Biotechnol. 22, 1393–1398.
Xiao, H., and Zhong, J.J. (2016). Production of useful terpenoids by higher-fungus
cell factory and synthetic biology approaches. Trends Biotechnol. 34,
242–255.
Xu, J.W., Xu, Y.N., and Zhong, J.J. (2012). Enhancement of ganoderic acid
accumulation by overexpression of an n-terminally truncated 3-hydroxy-3methylglutaryl
coenzyme a reductase gene in the basidiomycete Ganoderma
lucidum. Appl. Environ. Microbiol. 78, 7968–7976.
Yamauchi, Y., Hirasawa, T., Nishii, M., Furusawa, C., and Shimizu, H. (2014).
Enhanced acetic acid and succinic acid production under microaerobic conditions
by Corynebacterium glutamicum harboring Escherichia coli transhydrogenase
gene pntAB. J. Gen. Appl. Microbiol. 60, 112–118.
Yang, Z., Wang, S., Halim, A., Schulz, M.A., Frodin, M., Rahman, S.H., VesterChristensen,
M.B., Behrens, C., Kristensen, C., Vakhrushev, S.Y., et al. (2015).
Engineered CHO cells for production of diverse, homogeneous glycoproteins.
Nat. Biotechnol. 33, 842–844.
Yoon, J., Aishan, T., Maruyama, J., and Kitamoto, K. (2010). Enhanced production
and secretion of heterologous proteins by the filamentous fungus
Aspergillus oryzae via disruption of vacuolar protein sorting receptor gene
Aovps10. Appl. Environ. Microbiol. 76, 5718–5727.
Yu, J.L., Xia, X.X., Zhong, J.J., and Qian, Z.G. (2014). Direct biosynthesis of
adipic acid from a synthetic pathway in recombinant Escherichia coli. Biotechnol.
Bioeng. 111, 2580–2586.
Zabala, D., Bran˜ a, A.F., Flo´ rez, A.B., Salas, J.A., and Me´ ndez, C. (2013). Engineering
precursor metabolite pools for increasing production of antitumor mithramycins
in Streptomyces argillaceus. Metab. Eng. 20, 187–197.
Cell Systems 4, March 22, 2017 275
Cell Systems
Review