|Title||Parallel mapping of genotypes to phenotypes contributing to overall biological fitness.|
|Publication Type||Journal Article|
|Year of Publication||2008|
|Authors||Gall, S, Lynch, MD, Sandoval, NR, and Gill, RT|
|Pagination||382 - 393|
Laboratory selection is a powerful approach for engineering new traits in metabolic engineering applications. This approach is limited because determining the genetic basis of improved strains can be difficult using conventional methods. We have recently reported a new method that enables the measurement of fitness for all clones contained within comprehensive genomic libraries, thus enabling the genome-scale mapping of fitness altering genes. Here, we demonstrate a strategy for relating these measurements to the individual phenotypes selected for in a particular environment. We first provide a mathematical framework for decomposing fitness into selectable phenotypes. We then employed this framework to predict that single-batch selections would enrich primarily for library clones with increased growth rate, serial-batch would enrich for a broad collection of clones enhanced via a combination of increased growth rate and/or reduced lag times, and that overlap among selected clones would be minimal. We used the SCalar Analysis of Library Enrichments (SCALEs) method to test these predictions. We mapped all genomic regions for which increased copy number conferred a selective advantage to Escherichia coli when cultured via single- or serial-batch in the presence of 1-naphthol. We identified a surprisingly large collection (163 total) of tolerance regions, including all previously identified solvent tolerance genes in E. coli. We show that the majority of the identified regions were unique to the different selection strategies examined and that such differences were indeed due to differences among enriched clones in growth rate and lag times over the solvent concentrations examined. The combination of a framework for decomposing overall fitness into selectable phenotypes along with a genome-scale method for mapping genes to such phenotypes lays the groundwork for improving the rational design of laboratory selections.
|Short Title||Metabolic Engineering|