Industrial biotechnology

1. Industrial Biotechnology
Kalyani Rajalingham
Strain engineering
In adaptive evolution, a selection pressure is applied on a naturally diverse
population until the desired phenotype is obtained (Kim et al., 2013, Table 1).
Two methods in particular are used with this technique – batch, and continuous
culture. In continuous culture, replacement of an initially diverse parent
population with a variant occurs with time. Whereas in batch cultures,
transfer of a portion of existing culture into a new medium results in the
transfer of variants with higher fitness.
In genome shuffling, following protoplast fusion, homologous recombination
results in the exchange of multiple genes (Kim et al., 2013, Table 1). Following
digestion of cell walls, protoplasts are fused using fusogens. The advantage of
this technique is that the genome of multiple parents can be shuffled
permitting quick evolution.
In gTME, diversity is achieved at the transcriptome machinery level;
modification of transcriptome regulating proteins results in diversity. In
this case, the genes of interest are left untouched, however, the sigma factor
(σ70
) and the RNA Pol II transcription factor 2 (TFIID) are typically altered
using error prone PCR for instance (Kim et al., 2013, Table 1) (Kim et al., 2013).
This particular technique can simultaneously alter multiple genes when
sequential modification is troublesome.
In MAGE, the Redα/Redβ recombinase system, and single-stranded DNA
oligonucleotides are used to induce point mutations, insertions, and deletions
at particular locations (Kim et al., 2013, Table 1).
All methods aim to produce genetically diverse strains – creation of genetic
diversity, and tweaking of strains. In all cases, the goal of strain
improvement is to increase the metabolic capacity of the initial strain to suit
a particular function, and environment. The purpose of creating diversity is
to increase yield or improve cellular function via modifications to
biochemical pathways.
Table 1: Methods used for strain improvement.
Method Principle Common
Adaptive evolution Variant cell  selection
Natural Genetic
diversity/variation
Strain tweaking, and
improvement
gTME Transcriptome regulating Generating genetic

2. proteins are altered diversity of multiple
genes simultaneously
using the
transcriptional
machinery
Genome Shuffling Cell wall digestion 
protoplast fusion using
fusogens  homologous
recombination
Generation of novel
strains
MAGE Redα/Redβ recombinase
system, and single-stranded
DNA oligonucleotides 
point mutations, insertions,
and deletions
Diversity
Adaptive evolution is a feasible and fast process. It is great for tweaking, has
a short generation time, and does not require complex manipulations (Steensels
et al., 2014). However, if the selection criteria are not suited to the industrial
set-up, the resulting strain would be “crippled”, or not as effective under
industrial settings (Steensels et al., 2014). Further, most stains are screened to
tolerate/resist one stress, however, under industrial settings, strains might
have to endure multiple stresses. Further, some hosts (favoured over others)
might not have the capacity to metabolize certain molecules (ex: S. cerevisiae
cannot metabolize xylose); engineering may be required in these cases.
Genome shuffling, a fast, and easy process, can be used to gather genes from a
variety of strains that typically do not qualify for sexual hybridization
(Steensels et al., 2014). This technique can be used to raise the ploidy which can
at times increase productivity as well, and uses the entire genome to generate
diversity (Steensels et al., 2014). Hybrid success varies, and is dependent on the
proximity of the parents. However, at times, aneuploidy or dissociation results
because of instability. Further, the hybrid’s genotype cannot be easily
predicted (Steensels et al., 2014).
MAGE uses oligos which are inexpensive, and does not require large constructs
(Kim et al., 2013). Further, in MAGE, a single base or a codon can be altered, or
one can have small/large deletions/insertions; genes are targeted. The
frequency of generated mutant is quite high, and as such can be isolated
without selection. Multiplexing is also possible with MAGE. Efficiency is based
on size of modification, and therefore variable. However, it is both targeted,
and more complex than say adaptive evolution. The number of genes that have to
be modified simultaneously requires luck but the modification itself does not
leave behind “junk” such as selection markers, and restriction sites. However,
there might at times be additional unplanned changes/mutations (Court, 2015).

3. gTME alters multiple genes at any one time, however, it is a bit more complex
(labour intensive). gTME requires a mutant library to be created which is a bit
more involved than the remaining methods. In conjunction with a microarray
experiment, it can reveal phenotype-genotype correlation (Kim et al., 2013).
Table 2: Strengths and weaknesses of strain improvement techniques.
Method Strength Weakness
Adaptive evolution 1- Short generation
time, non-complex
manipulation,
feasible, fast,
great for tweaking
(Steensels et al.,
2014)
2- Selection criteria
has to be like the
industrial
conditions (else
“crippled” strains).
3- Typically, strains
are screened to
resist one stress,
but in the
industrial setting,
multiple stresses
might be present.
4- Some hosts do not
have the capacity
to hydrolyze
certain molecules
(ex: S. cerevisiae
and xylose).
5- Modification of a
few genes
(Steensels et al.,
2014)
gTME 1- Affects multiple
genes at any one
time
2- With microarray 
genotype-
phenotype
correlation
1- Requires that
mutants be created
2- Labour intensive
Genome Shuffling 1- Can be used to
combine genes of
strains that
usually don’t
qualify for sexual
hybridization
2- Synergy of genes
3- Uses genome to
improve diversity
1- Variable success
rate
2- Can result in
aneuploidy or
dissociation
3- Hybrid’s genotype
hard to predict
4- Sometimes thought
of as GMOs

4. 4- Increase in ploidy
5- Fast, easy
(Steensels et al.,
2014)
5- Modification of a
few genes
(Steensels et al.,
2014)
MAGE 1- Inexpensive oligos
2- No use of large
constructs
3- Can alter a single
base or codon
4- Multiplexing
5- No selection for
mutations
6- Efficient, not
expensive,
automated
7- Alters without
“junk” such as
selection markers,
restriction sites
1- Efficiency is
based on size
2- Only performed on
a few selected
hosts (not
applicable to all
microorganisms)
3- Knowledge of
genetic code
required
Algae-based biofuels
One must consider the yield per acreage, the cost of production, and fuel
characteristics. Currently, there is an ongoing food versus fuel debate. In
other words, with hunger still part of the society, the use of food to make fuel
is deemed unwarranted. Further, given the advances made in the field of
biotechnology, the use of alternative sources such as microalgae has become a
possibility. The problem with microalgae is the production cost, and energy
requirements associated with it (Wijffels and Barbosa, 2010).
Microalgae belong to the non-food biomass category, and does not require
arable land for growth (as such does not compete with food crops); land plants
would require approximately 0.4 billion m3
while microalgae would require 9.25
million ha (Wijffels and Barbosa, 2010). Microalgae can result in the
production of biodiesel, other fuel products (jet fuel, gasoline), ethanol, and
long-chain hydrocarbons (Chisti, 2008, Wijffels and Barbosa, 2010). Microalgae
have a higher lipid content (>80%), does not require large amounts of water,
and does require CO2 (carbon sink), can grow on seawater, and in deserts (with
salt aquifiers), and has a market price of €250/kg (the market price of
oleaginous crops is 0.50 €/kg) (Wijffels and Barbosa, 2010, Chisti, 2008).
However, the input energy (input fossil fuels) was found to be greater than
output energy by 56% (with a yield of 15 Mg ha-1
year-1
) (Reijnders, 2008).
Currently, scientists induce stress in order to acquire oil. However, scientists
believe that with more time, microalgae can be modified in such a way to

5. produce a cost effective oil supply; they believe that industrialization should
commence when the techniques have been perfected. For instance, stress-induced
production of oil decreases growth rates of algae. Scientists believe that with
time, they can initiate production of oil without applying stress (and
therefore decreasing growth) (Wijffels and Barbosa, 2010). At the present time,
there is only one strain that can be modified at multiple loci; only a few
strains are currently available, and many that have not been explored
(Wijffels and Barbosa, 2010).
The process must also be modified to reduce cost of production (10 fold), and
increase production itself (3 fold) (Wijffels and Barbosa, 2010). The
photobioreactors (material lifetime, water usage, cleaning, energy
requirements) requires modifications as well. Thus, it was advised that 10-15
years of research is required before microalgae fuel can be brought to the
market (Wijffels and Barbosa, 2010).
Table 3: Strengths, and weaknesses of microalgae beats.
Method Strength Weakness
Microalgae beats Oil rich (80% of dry weight) –
agricultural crops have an oil
content of 5% of total mass
Relative to the costs, the
yield is not sufficiently
high
Large areas (0.53 billion m3
)
required for growth of crops for
target yield production.
Microalgae does not require
large areas (123 m3
).
Input Energy – Output
Energy < 0 (negative)
when using microalgae.
(Reijnders, 2008)
Short doubling time, inexpensive
growth medium, residues can be
used as animal feed, carbon
neutral, photoautotrophic
Input Energy – Output
Energy > 0 for land
crops (positive yield)
(Reijnders, 2008)
Genetic modification can create
novel better performing strains
(Chisti, 2008)
Antibiotics
Derivatives can be produced by a process called combinatorial biosynthesis. In
this case, the required genes are subdivided into modules defined by catalytic
domains. Variation is generated by excising and either replacing, or deleting
a module (Nguyen et al., 2006). Alternatively, domains contained within modules

6. can be altered as well to generate diversity. Each module is responsible for
the incorporation of a particular amino acid. Typically, multiple modules are
deleted via lambda-Red-mediated recombination. The process begins with the
introduction of a plasmid (carrier of a cassette) present in a microorganism
such as E. coli into a strain of interest via conjugation. Recombination
between cassette and DNA results in the modification of the target modules
(Nguyen et al., 2006).
Non-ribosomal peptide synthesis is similar to polyketide synthesis. In this
particular set of genes, there are 6 domains of interest: the A, the PCP, C, E,
Cy, and Te domains. By altering a domain, one can generate a new molecule. The
A domain is responsible for the choosing of the amino acid which it
transferred to the PCP domain; the C domain is responsible for the
transesterification of the amino acid. The Cy domain is responsible for
cyclization; the Te domain is required for peptide release. Once the strain is
created, the effectiveness of the compound is determined. The MIC (minimum
inhibitory concentration) of the compound is also measured.
Nguyen et al., (2006) stated that the performance of daptomycin analogs were
not superior to daptomycin, and a few were found to be equal to daptomycin. As
such, one might expect to generate multiple compounds but only one or two will
most likely perform similar to the parent compound.
One can generate novel, and effective molecules with similar properties via
combinatorial biosynthesis. The number of possible molecules is limited by the
number of modules. The NRPS system, unlike the normal peptide synthesis, has
more than 300 amino acids that can be utilized. In this case, assuming 12 amino
acids, there are 12300
possible combinations, and therefore potential compounds.

7. However, structures are based on existing compounds, and are as such limited.
One can vary the module number, the primer, the extender unit, degree of
reduction, and the stereochemistry. However, length-wise, the NRP is between 2-
48 residues. However, limitations are many; chemistry is a limitation (certain
reactions can take place only after another reaction), and downstream modules
accept only a few upstream products.
Each module can take up any of the 300 proteinogenic amino acids. Specificity
of modules have to be redesigned by excision, or replacement to permit
inclusion of another amino acid. Specificity of tailoring enzymes are also
reduced. However, not all mutations are well tolerated, and this field still
remains to be explored. If modules were independent of each other,
combinatorial biosynthesis would be simple. However, the dependency of a given
module is yet unknown.
Alternatively, low yield, problems in expression (genes may not be accepted by
all hosts), large size, and modifications after synthesis of compound can be
problematic. Further, as the analogs differ from the parent compound, the
function, and potency of the compound is likely to decrease. Typically, quality
of analog is less superior than the actual compound.
NRPS biosynthetic pathway construction can generate one strain, and therefore
one compound. Each analog derivative, and therefore strain, must be generated
via modifications made manually, and as such, exploration of potential
compounds would be tedious. The biosynthetic pathway can be moved into another
host, however, yield is likely to be low.
Typically, quality of an analog is less superior than the actual compound.
However, due to resistance to actual antibiotics, and the need for novel
compounds, this technique can generate alternative effective compounds.
However, it takes about 7-10 years to before an antibiotic can be put on the
market. The market itself for an antibiotic is small, and use, and resistance
are positively correlated.
References
Chisti, Y. (2008). Biodiesel from microalgae beats bioethanol. Trends In
Biotechnology 26, 126-131.
Court, D. (2015). Background: Recombineering with ssDNA.
Kim, B., Du, J., and Zhao, H. (2013). Strain improvement via evolutionary
engineering, John Wiley & Sons, Inc.

8. Nguyen, K., Ritz, D., Gu, J., Alexander, D., Chu, M., Miao, V., Brian, P., and Baltz,
R. (2006). Combinatorial biosynthesis of novel antibiotics related to
daptomycin. Proceedings Of The National Academy Of Sciences 103, 17462-17467.
Reijnders, L. (2008). Do biofuels from microalgae beat biofuels from terrestrial
plants?. Trends In Biotechnology 26, 349-350.
Steensels, J., Snoek, T., Meersman, E., Nicolino, M., Voordeckers, K., and
Verstrepen, K. (2014). Improving industrial yeast strains: exploiting natural
and artificial diversity. FEMS Microbiology Reviews 38, 947-995.
Wijffels, R., and Barbosa, M. (2010). An Outlook on Microalgal Biofuels. Science
329, 796-799.