Fast-track adaptive laboratory evolution of Cupriavidus necator H16 with divalent metal cations.

Microbial strain improvement through adaptive laboratory evolution (ALE) has been a key strategy in biotechnology for enhancing desired phenotypic traits. In this Biotech Method paper, we present an accelerated ALE (aALE) workflow and its successful implementation in evolving Cupriavidus necator H16 for enhanced tolerance toward elevated glycerol concentrations. The method involves the deliberate induction of genetic diversity through controlled exposure to divalent metal cations, enabling the rapid identification of improved variants. Through this approach, we observed the emergence of robust variants capable of growing in high glycerol concentration environments, demonstrating the efficacy of our aALE workflow. When cultivated in 10% v/v glycerol, the adapted variant Mn-C2-B11, selected through aALE, achieved a final OD value of 56.0 and a dry cell weight of 15.2 g L, compared to the wild type (WT) strain's final OD of 39.1 and dry cell weight of 8.4 g L. At an even higher glycerol concentration of 15% v/v, Mn-C2-B11 reached a final OD of 48.9 and a dry cell weight of 12.7 g L, in contrast to the WT strain's final OD of 9.0 and dry cell weight of 3.1 g L. Higher glycerol consumption by Mn-C2-B11 was also confirmed by high-performance liquid chromatography (HPLC) analysis. This adapted variant consumed 34.5 times more glycerol compared to the WT strain at 10% v/v glycerol. Our method offers several advantages over other reported ALE approaches, including its independence from genetically modified strains, specialized genetic tools, and potentially carcinogenic DNA-modifying agents. By utilizing divalent metal cations as mutagens, we offer a safer, more efficient, and cost-effective alternative for expansion of genetic diversity. With its ability to foster rapid microbial evolution, aALE serves as a valuable addition to the ALE toolbox, holding significant promise for the advancement of microbial strain engineering and bioprocess optimization.