Unbiased and scalable reduction of diverse bacterial genomes

The genome, a complex and intricate system, contains many components whose functions and regulatory interactions are not fully understood. The goal of genome minimization is to simplify this complexity by eliminating non-essential elements, thereby revealing the core building blocks of cellular life. However, existing minimization methods are typically slow, species-specific, and produce only isolated strains, which limits our understanding of the diverse ways a genome can adapt to extensive DNA removal.

In this study, researchers introduce Stochastic Lineage-based Iterative Minimization (SLIM), a modular and high-throughput platform designed for unbiased genome reduction across a wide range of phylogenetically diverse bacteria. By applying SLIM, the team generated a library of genome-reduced Escherichia coli lineages. They then analyzed these lineages, revealing both universal and lineage-specific transcriptional and translational reprogramming in response to deletions.

This analysis showed that these changes in expression dynamics drive environment-dependent fitness, allowing researchers to identify a single gene deletion in one of the genome-reduced lineages as the cause of a measurable environmental growth defect.

Further, the researchers successfully applied SLIM to phylogenetically distinct bacterial taxa, including Shigella flexneri and Pseudomonas putida, which are from different genera and orders compared to E. coli. These results demonstrate the scalability and generalizability of the SLIM platform, providing a robust and versatile tool for navigating the vast landscape of minimized genomes.

The findings establish a powerful new method for functional discovery and the rational design of synthetic genomic chassis, offering new insights into the fundamental building blocks of cellular life.