(281f) Resolving Genetic Engineering Signatures in Yeast with the Iseq and Minion on-Site
AIChE Annual Meeting
2019
2019 AIChE Annual Meeting
Food, Pharmaceutical & Bioengineering Division
Metabolic Engineering: Emerging Tools and Technologies
Tuesday, November 12, 2019 - 9:30am to 9:48am
To establish the most accurate workflow, we evaluated four nanopore de novo assemblers and three polishing algorithms at varying genome coverage depths for the lab strain S. cerevisiae CEN.PK113-7D. Our results show that (1) nanopore genome coverage depth must be at least 40X, (2) Flye and Canu are currently the best assemblers due to their combination of structure, completeness, and accuracy, and (3) Illumina data is essential for polishing. Our final pipeline generated a better S. cerevisiae CEN.PK113-7D assembly than the publicly available reference genome.
We then applied this pipeline to 12 engineered S. cerevisiae strains of varying genetic background â including strains from S288C, BY4741, BY4742, CEN.PK, W303a, and brewery lineages. Interestingly, the nanopore assembler Flye was the only software able to resolve both chromosomally-integrated pathways and complete plasmids â and was able to do so even when presented with mixtures of plasmids. The more widely used nanopore assembler Canu was unable to resolve complete plasmids. We then extended the pipeline to resequence nonconventional yeasts. A high-quality genome is key to metabolic engineering, systems and synthetic biology, and connecting genotype to phenotype. Thus, we sequenced Pichia pastoris, Hansenula polymorpha, Yarrowia lipolytica, Kluyveromyces marxianus, Debaryomyces hansenii, and Xanthophyllomyces dendrorhous. The resulting de novo genomes show significant improvement over the respective references â achieving chromosomal resolution, closing large gaps, and revealing previously omitted genes. Thus, the engineered and âground truthâ assemblies we have created represent an advance in the ability to detect signatures of metabolic engineering and support further metabolic engineering of nonconventional yeasts.
Sequencing is becoming ever more prevalent in research workflows across disciplines, including metabolic engineering. We provide here a pipeline that can accurately determine genotype and resolve complete engineering signatures in unknown samples. This technology can be inexpensively implemented on-site in the many distributed locations where organisms are engineered to obtain high-quality de novo genome assemblies. Thus, this pipeline can be widely applied in academic, government, and industry settings to study and monitor engineered organisms without high capital costs and deep coverage depths characteristic of alternative sequencing platforms and algorithms.