Tryptophan (Trp) Optimization

Our intention after validation…

if more lab time had been possible…

was to further proof SOAP lab’s usefulness…

By applying it to...

A metabolic engineering problem!

Our initial and more ambitious project involved using BASIC assembly in yeast for the first time to build a high tryptophan yielding Saccharomyces cerevisiae strain. In the past, work on Trp optimisation in yeast was done in the groups of Michael Jensen and J. Keasling, who took a predictive engineering approach using machine learning (Jie Zhang et al., 2019). This would offer a sustainable alternative to chemical synthesis. L-Tryptophan is an essential amino acid widely used in medicine, food and animal feed and has an annual demand above 50000 tons (Liuet at., 2019). From the onset, we wanted to optimise the pathway with help of automation, as we realised that even our reduced design space would require over 200 assemblies, which was further inspiration for SOAP Lab.

1. Jie Zhang, Søren D. Petersen, Tijana Radivojevic, Andrés Ramirez, Andrés Pérez, Eduardo Abeliuk, Benjamín J. Sánchez, Zachary Costello, Yu Chen, Mike Fero, Hector Garcia Martin, Jens Nielsen, Jay D. Keasling & Michael K. Jensen 2019, "Predictive engineering and optimization of tryptophan metabolism in 2 yeast through a combination of mechanistic and machine learning 3 models", BioRxiv.
2. Liu, Bilal, Luo, Zhao & Iqbal 2019, "Metabolic Engineering and Fermentation Process Strategies for L-Tryptophan Production by Escherichia coli", Processes, vol. 7, no. 4, pp. 213.

STEP 1

We had planned to achieve our aim through the simultaneous knockdown of competing pathway enzymes and overexpression of important enzymes in the Trp biosynthesis pathway or the shikimate pathway.

We initially found potential targets through investigating the literature and the biosynthesis pathways and complemented it with flux balance analysis (FBA).

Whilst we were unable to carry out the lab work due to the Covid pandemic, we present this modelling and the experimental design on this page of our Wiki.

Modelling

Head over to our Modelling page to see how we did the modelling for this project!

Next Steps

Sadly, due to Covid-19 all the subsequent steps will have to be performed after the competition

Future work

· · ·

Our software is particularly useful in high-throughput experiments, such as combinatorial approaches that explore a large design space.

The enzymes Trp2 and Aro4 catalyze the initial step of Trp biosynthesis and the first step in aromatic amino acid biosynthesis respectively (Saccharomyces cerevisiae database).

They will be overexpressed in their mutated form to abolish any feedback related inhibition and will be placed downstream 6 different strength promoters, with the aim of finding their optimal expression levels.

Overexpression of these enzymes is supported by (Jie Zhang et at., 2019).

Simultaneously, we will express a single sgRNA targeting either Aro7, Aro8 or CDC19 to achieve its downregulation. Three different sgRNAs will be tested for the three different genes.

The sgRNA will bind a constitutively expressed and genome integrated dCas9 in Rodrigo Ledesma’s S. cerevisiae validated strain.

Upon binding, these will travel to the promoter region of the above-mentioned genes.

Impeding the binding of DNA polymerase and
thus down regulating gene expression.

Aro7 initiates the branch for tyrosine/phenylalanine biosynthesis, a competing pathway for Trp production, and Aro 8 is involved in Trp degradation. CDC19 codes for pyruvate kinase which feeds into the TCA cycle which also diverts flux from Trp production (Saccharomyces cerevisiae database).

Thus, we believe downregulating these genes would lead to an increase in Trp production. This is further supported by flux balance analysis we conducted on
previous work.

The objective was to use SOAP-Lab to build a large combinatorial library of varying CRISPR-dCas9 based knockdowns of Aro8, Aro7, and CDC19 combined with varied strengths of overexpressions of Aro4 K229L and Trp2 S65R,S76L. For the successful implementation of this project it required the design of BASIC parts for yeast including: 7 yeast promoters; a yeast terminator; the two genes to be overexpressed; nine different gRNAs; and finally parts for the development of a yeast backbone.These parts have all been synthesised with thanks to Twist and IDT, and we hope to continue with this project in the near future.

The illustrated construct/s will be integrated into a backbone, which will include an origin of replication, chloramphenicol resistance, Ura 3 homology sites for genomic integration, mScarlett (red fluorescence) and an Ura 3 marker gene. The references for the mutated enzymes shown in the construct are: Graf, R., Mehmann, B., and Braus, G.H. (1993). Analysis of feedback-resistant anthranilate 945 synthases from Saccharomyces cerevisiae. J. Bacteriol. 175, 1061–1068. – trp2; Hartmann, M., Schneider, T.R., Pfeil, A., Heinrich, G., Lipscomb, W.N., and Braus, G.H. (2003). 956 Evolution of feedback-inhibited / barrel isoenzymes by gene duplication and a single mutation. 957 Proc. Natl. Acad. Sci. 100, 862–867.

If the single gRNA experiments had been successful, we would have then proceeded with experiments involving an array of multiple gRNAs. In order for each gRNA in the array to be cleaved, they would be placed in the following manner: HammerHead Ribozyme — gRNA — HDV ribozyme and repeat (He et al., 2017). This would allow to direct multiple gRNA to the promoter sequence of a target gene simultaneously. This is desirable as it would allow to test for a range of knockdowns, hopefully leading to an optimal knockdown percentage of a competing pathway. We thought this would be interesting especially in Trp optimisation, because competing pathways in this case lead to the production of other amino acids, essential for cell survival. As there is a trade off between cell survival and Trp production in the presence of competing pathway knockdown, we intended to find the optimal knockdown percentage between 0 and 100%.

He, Y., Zhang, T., Yang, N., Xu, M., Yan, L., Wang, L., Wang, R. & Zhao, Y. 2017, "Self-cleaving ribozymes enable the production of guide RNAs from unlimited choices of promoters for CRISPR/Cas9 mediated genome editing", Journal of genetics and genomics, vol. 44, no. 9, pp. 469-472.

Screening and gene expression analysis

1. Violacein pathway

To allow for screening, we will insert the violacein pathway as a visual output, which uses Trp as a precursor. An increase of Trp should lead to an increase of violacein production.

2. Liquid Chromatography / Mass Spectrometry (LCMS)

This initial screen would then be confirmed through LC/MS.

3. Real time quantitative PCR (RT-qPCR) and sequencing

Finally, gene up and down regulations will be measured through RT-qPCR and correct plasmid construction will be confirmed through sequencing.