Abstract
Successful strain engineering involves perturbing key nodes within the cellular network. How the network’s connectivity affects the phenotype of interest and the ideal nodes to modulate, however, are frequently not readily apparent. To guide the generation of a list of candidate nodes for detailed investigation, designers often examine the behavior of a representative set of strains, such as a library of transposon insertion mutants, in the environment of interest. Here, we first present design principles for creating a maximally informative competitive selection. Then, we describe how to globally quantify the change in distribution of strains within a transposon library in response to a competitive selection by amplifying the DNA adjacent to the transposons and hybridizing it to a microarray. Finally, we detail strategies for analyzing the resulting hybridization data to identify genes and pathways that contribute both negatively and positively to fitness in the desired environment.
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Acknowledgments
We are grateful to Hany Girgis for developing and optimizing many of the techniques described here and to Hani Goodarzi for developing iPAGE. Work in the Tavazoie lab was supported by grants from NSF (CAREER), DARPA (BIOS), NIGMS (P50 GM071508), and the NIH Director’s Pioneer Award (1DP10D 003787-01).
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Hottes, A.K., Tavazoie, S. (2011). Microarray-Based Genetic Footprinting Strategy to Identify Strain Improvement Genes after Competitive Selection of Transposon Libraries. In: Williams, J. (eds) Strain Engineering. Methods in Molecular Biology, vol 765. Humana Press. https://doi.org/10.1007/978-1-61779-197-0_6
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DOI: https://doi.org/10.1007/978-1-61779-197-0_6
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