Exploring the fitness benefits of genome reduction in Escherichia coli by a selection-driven approach

Viktor Vernyik, Ildikó Karcagi, Edit Tímár, István Nagy, Ádám Györkei, Balázs Papp, Zsuzsanna Györfy, György Pósfai

Research output: Article

Abstract

Artificial simplification of bacterial genomes is thought to have the potential to yield cells with reduced complexity, enhanced genetic stability, and improved cellular economy. Of these goals, economical gains, supposedly due to the elimination of superfluous genetic material, and manifested in elevated growth parameters in selected niches, have not yet been convincingly achieved. This failure might stem from limitations of the targeted genome reduction approach that assumes full knowledge of gene functions and interactions, and allows only a limited number of reduction trajectories to interrogate. To explore the potential fitness benefits of genome reduction, we generated successive random deletions in E. coli by a novel, selection-driven, iterative streamlining process. The approach allows the exploration of multiple streamlining trajectories, and growth periods inherent in the procedure ensure selection of the fittest variants of the population. By generating single- and multiple-deletion strains and reconstructing the deletions in the parental genetic background, we showed that favourable deletions can be obtained and accumulated by the procedure. The most reduced multiple-deletion strain, obtained in five deletion cycles (2.5% genome reduction), outcompeted the wild-type, and showed elevated biomass yield. The spectrum of advantageous deletions, however, affecting only a few genomic regions, appears to be limited.

Original languageEnglish
Article number7345
JournalScientific reports
Volume10
Issue number1
DOIs
Publication statusPublished - dec. 1 2020

ASJC Scopus subject areas

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    Vernyik, V., Karcagi, I., Tímár, E., Nagy, I., Györkei, Á., Papp, B., Györfy, Z., & Pósfai, G. (2020). Exploring the fitness benefits of genome reduction in Escherichia coli by a selection-driven approach. Scientific reports, 10(1), [7345]. https://doi.org/10.1038/s41598-020-64074-5