Open AccessDeep scanning lysine metabolism in Escherichia coli
Author(s) -
Bassalo Marcelo C,
Garst Andrew D,
Choudhury Alaksh,
Grau William C,
Oh Eun J,
Spindler Eileen,
Lipscomb Tanya,
Gill Ryan T
Publication year - 2018
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.15252/msb.20188371
Subject(s) - biology , phenotype , computational biology , mutagenesis , escherichia coli , genetics , lysine , crispr , function (biology) , forward genetics , systems biology , gene , mutation , amino acid
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.