Deep scanning lysine metabolism in Escherichia coli

Abstract

Our limited ability to predict genotype–phenotype relationships has called for strategies that allow testing of thousands of hypotheses in parallel. Deep scanning mutagenesis has been successfully implemented to map genotype–phenotype relationships at a single‐protein scale, allowing scientists to elucidate properties that are difficult to predict. However, most phenotypes are dictated by several proteins that are interconnected through complex and robust regulatory and metabolic networks. These sophisticated networks hinder our understanding of the phenotype of interest and limit our capabilities to rewire cellular functions. Here, we leveraged CRISPR‐EnAbled Trackable genome Engineering to attempt a parallel and high‐resolution interrogation of complex networks, deep scanning multiple proteins associated with lysine metabolism in Escherichia coli. We designed over 16,000 mutations to perturb this pathway and mapped their contribution toward resistance to an amino acid analog. By doing so, we identified different routes that can alter pathway function and flux, uncovering mechanisms that would be difficult to rationally design. This approach sets a framework for forward investigation of complex multigenic phenotypes.

Development and Application of CRISPR/Cas in Microbial Biotechnology

“…Lee et al used a CRISPRi system, targeting 4,565 (99.7%) genes to identify a minimal set of genes required for rapid growth of Vibrio natriegens (Lee et al, 2019). Bassalo et al applied CRISPR/Cas9 to perform a parallel and high-resolution interrogation of over 16,000 mutations to identify proteins associated to lysine metabolism in E. coli (Bassalo et al, 2018). While Wang et al built a larger guide RNA library of ∼60,000 members for coding and non-coding targets in E. coli, and applied CRISPRi system to associate genes with phenotypes at the genome level (Wang T. et al, 2018).…”

Mutation bias interacts with composition bias to influence adaptive evolution

“…These facilitate the assignment of phenotypes to a large number of genotypes, and thus allow for the construction of empirical genotype-phenotype landscapes directly from experimental data. Examples include the "splicing-in" of exons [28], the binding preferences and enzymatic activities of macromolecules [29][30][31][32], the gene expression patterns of regulatory circuits [33], and the carbon utilization profiles of metabolic pathways [34]. In nearly all of these examples, the genotype-phenotype landscape is necessarily incomplete, representing only a small fraction of a much larger landscape, which cannot be constructed in its entirety due to the hyper-astronomical size of the corresponding genotype space [18].…”

Integrating CRISPR-Enabled Trackable Genome Engineering and Transcriptomic Analysis of Global Regulators for Antibiotic Resistance Selection and Identification in Escherichia coli

“…Subsequently, these cassettes were amplified and cloned into cells expressing Streptococcus pyogenes Cas9 and bacteriophage lambda Red recombineering proteins to generate the libraries. A major limitation of CRISPR-based high-throughput technologies is that differences in gRNA activity lead to a low overall editing efficiency of ϳ1 to 2% at the library scale (31). Therefore, we plated and scraped 30-to 200-fold more colonies than the theoretical size of each sublibrary in order to ensure the efficient coverage of all mutations (Table S2).…”