Optimizing rhamnolipid biosynthesis: evaluating predictive methods using Pseudomonas aeruginosa mutants

Rhamnolipids (RLs) are versatile biosurfactants produced by Pseudomonas aeruginosa with significant industrial potential. However, high production costs remain a barrier to large-scale use, necessitating genetic strategies to improve yields. While many genes are reported to influence RL production, studies often rely on qualitative phenotypic assays of questionable reliability. We systematically evaluated 29 P. aeruginosa PA14 mutants using traditional assays (Siegmund–Wagner blue plates, swarming motility) and validated the findings using liquid chromatography–mass spectrometry (LC/MS). We found that traditional phenotypic assays have a high misprediction rate (~35% to 38%), primarily due to confounding factors like variable flagellar function, colony spreading, and growth rates. Specifically, LC/MS quantification revealed that rpoN and pvdQ knockouts significantly increased total RL titers, whereas crc , dksA , and dspI knockouts decreased production. Notably, the increased titers in rpoN and pvdQ mutants were linked to enhanced biomass accumulation rather than higher per-cell biosynthetic rates. These findings highlight the critical necessity of using quantitative analytical methods for accurate strain screening and provide a clarified set of genetic targets for metabolic engineering aimed at optimizing RL production. IMPORTANCE This study addresses a critical methodological flaw in biosurfactant research: the overreliance on qualitative phenotypic assays that too often lead to inaccurate conclusions. By systematically comparing traditional screening methods with liquid chromatography–mass spectrometry quantification across a collection of Pseudomonas aeruginosa mutants, we demonstrate that common assays like swarming motility and blue plates fail to accurately predict rhamnolipid (RL) production in over one-third of cases. These inaccuracies lead to the misidentification of genetic targets and may waste resources in metabolic engineering efforts. Our work provides a reliable framework for strain screening, identifies specific genes that influence RL yields, and clarifies the biological factors—such as flagellar motility and growth dynamics—that bias traditional results. These findings are essential to optimize biosurfactant production and ensure data reproducibility.