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

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).

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- 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-1year-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

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

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.

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.

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.