(498d) A Workflow for the Systems-Level Analysis, Design, and Engineering of Genomically Recoded Organisms

Genomically recoded organisms are tightly biocontained and biosolated (e.g., virus resistant) and allow the efficient incorporation of multiple non-standard amino acids, making them attractive platform technologies for biotechnological, industrial, and biomedical applications^1–4. In the process of recoding, we substitute a set of selected codons by synonymous ones throughout the entire genome^2,5. Although protein sequences are maintained, we and others have observed considerable fitness reductions upon recoding^2,3,6,7. To design viable genomically recoded organisms, we need computational methods that can connect genome sequences to cellular phenotypes with enhanced resolution than available methods.

Here, we present a workflow for the rational analysis, design, and engineering of genomically recoded organisms. First, we have developed deep learning models connecting sequence-to-function of regulatory genetic features. Second, we have constructed extended genome-scale models for the integrative analysis of metabolism, expression, and regulation. These models can predict cellular fitness from recoded genome sequences and can integrate large and disparate omics data sets. Finally, we have defined optimization-based methods for the computational analysis of recoding mutations. With this workflow, we can predict at a systems-level the combinatorial effect of recoding mutations on fitness, and we identify sets of mutational bottlenecks constraining growth.

We are applying this workflow to identify fitness-decreasing mutations in genomically recoded strains of Escherichia coli. The models developed together with newly obtained RNA-seq data for these strains are helping characterize the metabolic function arising from synthetic genomes. In the future, this workflow will serve to design genomically recoded organisms with minimal growth reduction and will provide a better understanding of genotype-to-phenotype relationships at a cellular level.

References

1. Ostrov, N. et al. Synthetic genomes with altered genetic codes. Curr Opin Sys Biol 24, 32–40 (2020).
2. Lajoie, M. J. et al. Genomically Recoded Organisms Expand Biological Functions. Science 342, 357–360 (2013).
3. Fredens, J. et al. Total synthesis of Escherichia coli with a recoded genome. Nature 569, 514–518 (2019).
4. Robertson Wesley E. et al. Sense codon reassignment enables viral resistance and encoded polymer synthesis. Science 372, 1057–1062 (2021).
5. Isaacs, F. J. et al. Precise Manipulation of Chromosomes in Vivo Enables Genome-Wide Codon Replacement. Science 333, 348 (2011).
6. Ostrov, N. et al. Design, synthesis, and testing toward a 57-codon genome. Science 353, 819–822 (2016).
7. Lajoie, M. J. et al. Probing the Limits of Genetic Recoding in Essential Genes. Science 342, 361 (2013).