Novel Methods for Development of Cell Factories and Laboratory Simultations

Biotechnology is providing remarkable societal changes by creation of biological systems with useful purposes. A core aim of metabolic engineering and synthetic biology is to redesign and create biological systems, for example cell factories that produce pharmaceuticals, fuels and chemicals. However,the lack of foundational technologies, methods, and prediction capabilities form a large barrier towards the transformation to a bio-based society, where products of cell factories are replacing fossil fuels, plastics and traditional chemicals derived from oil.

In this thesis we present new laboratory and computational methods that we have developed, aimed at advancing the fields of cell factory development, metabolic engineering and synthetic biology. We also present a new generation of laboratory simulations for training and learning about laboratory techniques along with a quantitative assessment of their effectiveness, benefits and limitations.We first describe a novel method to amplify DNA oligos from DNA microarray chips for large-scale genetic engineering.

The method, termed Microarray-Oligonucleotide Multiplex Automated Genome Engineering (MO-MAGE), allows the modification of thousands of genomic sites simultaneously. We demonstrated the feasibility of the method by inserting T7 promoters upstream of 2587 operons in E. coli, and validating the resulting cell library by deep sequencing.

The method makes large-scale mutagenesis projects possible at a cost between 10x to more than 1000x lower than traditional column-based approaches.The design of oligonucleotides for (MO)-MAGE is a barrier that can be tedious, time-consuming, and is impossible to be manually conducted for larger projects targeting many genomic sites.

To address these challenges, we have developed the MAGE Oligo Design Tool (MODEST), which allow design of MAGE oligos for generating translational gene knockouts, and introducing other coding or non-coding mutations, including amino acid substitutions, insertions, deletions, and point mutations. Another key challenge in cell factory development that we have addressed in this work is the ability to precisely modify the expression level of genes.

We constructed an Escherichiacoli genomic cell library with at least 99.3 % of the possible Shine Dalgarno (SD) sequences. We then comprehensively assessed their differential contribution to protein expression using fluorescence-activated cell sorting and deep DNA sequencing. Based on this data, we developed an algorithm(EMOPEC), which enables efficient modulation of the expression level of any chromosomally encoded gene in E. coli, by changing only a few bases in the SD sequence.

We tested the algorithm by modulating 6 chromosomal genes with 10 different linearly spaced expression levels, and found a good correlation between predicted and measured values (R2=0.64), which was considerably higher than state of the art thermodynamic models (R2= 0.37). State-of-the-art systems for MAGE are limited by a lack of mobility, high off-target mutation rate and growth only below 34°C because of temperature induced genes toxic when expressed for long.

We have developed a novel plasmid-based system that provides high recombination efficiency, is curable,and reduces off target mutations rates to wild type levels when not induced, by transient activating of the mismatch repair system (MMR) by dam over expression. When induced, the mutation frequency was observed to be 93% lower on average than the mutS- system.We applied MODEST, MAGE as well as Adaptive laboratory evolution (ALE) to develop heat resistant strains and study evolution.

Heat resistance is relevant for many production processes and can be a useful trait to introduce in many types of cell factories. We performed ALE to 42 °C of ten parallel populations of Escherichia coli K-12 MG1655. Genome engineering with the techniques described above was used to introduce the novel ALE-acquired alleles in random combinations into the ancestral strain, and competition between these engineered strains was used to identify causative mutations by selection at 42 °C and subsequent deep sequencing of several selected clones.

Interestingly, most of the identified key genes differed significantly from those found in similar temperature adaptation studies, highlighting the sensitivity of genetic evolution to experimental conditions and ancestral genotype. The results of the study provide insight into the adaptation process and yield important lessons for the future implementation of ALE, as well as a novel framework for identifying causative mutations in evolved populations.

Furthermore, the identified mutations can be introduced into cell factories to improve production capabilities. For the development of cell factories based on plasmid systems, the USER (or “uracil excision”) cloning method is a powerful method. Using a fast and simple protocol, USER cloning has proved successful for directional scar-less assembly of multiple DNA fragments into plasmids as well as forsite-directed mutagenesis.

We have developed a web servertool (AMUSER) that automates the design of optimal PCR primers for several distinct USER cloning-based applications by designing optimal PCR primers for the desired genetic changes.Together, the developed methods enable precise, costeffective,large-scale targeted genome engineering useful for avariety of applications in synthetic biology and metabolic engineering.

A key part of the transforming towards a biobased society and development of cell factories, is a skilled workforce to address the challenges. Furthermore, new methods need to be accessible to scientists and students in order to impact future research. Part of this challenge can be addressed by improving education and making these technologies accessible to students.

The second part of the thesis relates to innovative methods for enhancing science education through technology. Traditional teaching methods are dominating science education,but new IT-based approaches provide an opportunity for increasing the skill level of students and motivate young people to pursue studies within the field.

Many practical barriers such as cost, safety and time limit laboratory teaching, making it an especially relevant area for implementing simulations. We show a 76 % increase in learning outcomes by using a gamified laboratory simulation (Labster) compared to traditional teaching and doubled the learning effectiveness when used in combination, suggesting an untapped potential for increasing the skills of science students and graduates.

In another study with medical students we found that simulations increased students’ learning, intrinsic motivation, and selfefficacy and increased the perceived relevance of medical educational activities. The results suggest that simulations can help future generations of doctors transfer new understanding of disease mechanisms gained in virtual laboratory settings into everyday clinical practice.