Data-Driven Strain Design Towards Mitigating Biomanufacturing Stresses

Abstract

AbstractMicrobial strains used in large-scale biomanufacturing of melatonin often experience stresses like reactive oxygen species (ROS), SOS response, and acid stress, which can reduce productivity. This study leveraged a data-driven workflow to identify mutations that could improve robustness to these stresses for an industrially important melatonin production strain. This work integrated more than 7000 E. coli adaptive laboratory evolution (ALE) mutations to statistically associate mutated genes to 2 ROS tolerance ALE conditions from 72 available conditions. oxyR, fur, iscR, and ygfZ were significantly associated and hypothesized to contribute to fitness in ROS stress. Across these genes, 259 total mutations were inspected and 10 were chosen for reintroduction based on mutation clustering and transcriptional signals as evidence of fitness impact. Strains engineered with mutations in oxyR, fur, iscR, and ygfZ exhibited increased tolerance to H2O2 and acid stress, and reduced SOS response suggesting improved genetic stability. Additionally, new evidence was generated towards understand the function of ygfZ, a gene of relatively uncertain function. This meta-analysis approach utilized interoperable multi-omics datasets to identify targeted mutations conferring industrially-relevant phenotypes, describing an approach for data-driven strain engineering to optimize microbial cell factories.Visual Abstract