Comparative analysis of gene importance in Escherichia coli across growth conditions

As the ability to synthesize complete genomes becomes increasingly possible, the question of what should compose those sequences is becoming more prevalent. However, identifying genes essential for the survival of an organism is challenging, as gene essentiality is a nuanced concept that heavily depends on context. In this study, we identified growth medium-specific fitness-impacting genes by performing transposon mutagenesis in Escherichia coli BW25113 and sequencing mutant populations at multiple time points in three growth media. Our analysis revealed a core set of 412 core genes with high impact on the fitness across all conditions, along with distinct medium-specific gene sets. By analyzing temporal variations in read counts per gene, we identified additional sets of genes whose inactivation causes an appreciable, albeit lower, impact on fitness. We used these sets to define medium-specific gene modules required to sustain robust growth under each condition. Our study underscores the context-dependent nature of gene essentiality and represents a step toward refining the concept from a universal list to a more nuanced, condition-specific framework, which will be invaluable for future genome design efforts.

As complete genome synthesis becomes more accessible, determining which genes should be included in a synthetic genome to provide robust growth becomes increasingly critical. This study demonstrates that gene contributions to fitness are not binary and depend strongly on environmental conditions. By analyzing E. coli transposon mutants grown in different media over multiple passages using an innovative sliding-window approach, we identified genes generally important for fitness and others that are condition-specific or have a reduced, yet measurable, impact on fitness. Using these gene sets, we formulated medium-specific gene modules that combine essential and fitness-contributing genes to support robust growth in each environment. This improved understanding takes us beyond static gene lists and toward dynamic, context-aware genome design tailored for specific applications.