Exploring growth-coupled production designs in Synechocystis sp. PCC 6803 to combat phenotypic instability

Industrial-scale cultivation often exposes phenotypic instability, a phenomenon where non-producing mutants outcompete production strains due to the growth burden imposed by product synthesis, leading to declining overall yields over time. Growth-coupled production offers a powerful strategy to overcome this challenge by directly linking cellular fitness to product formation, thereby stabilizing production under long-term evolutionary pressure. Despite advances in genome-scale metabolic modeling, existing growth-coupling algorithms are often computationally intractable and largely restricted to native metabolites. Here, we present the Forbidden Fruits (FF) algorithm, a fast heuristic method for identifying growth-coupled production strategies for non-native metabolites. Applied to L-lactate production in Synechocystis PCC 6803, FF outperformed a conventional deterministic algorithm in both computational efficiency and solution quality. One prominent solution relied on malate–lactate transhydrogenase (MLTH), a poorly characterized enzyme of central carbon metabolism originally described in the genus Veillonella. We identified the MLTH gene in Veillonella parvula as Vpar_1595, experimentally confirmed its activity, and showed through phylogenetic analysis that MLTH forms a distinct evolutionary clade. Expression of MLTH in Synechocystis enabled L-lactate production, and computational analysis indicated growth-coupled production in an intermediate strain, although experimental validation of long-term stability remains outstanding. Finally, we introduce the concept of relief production, extending growth-coupling strategies to metabolites that are otherwise difficult to stabilize. We provide a mathematical framework describing how growth-coupled bottlenecks can be selectively relieved by pathways with favorable thermokinetics, enabling sustained production when the resulting growth benefit outweighs enzymatic costs. Together, these results advance both the computational and conceptual toolkit for engineering stable cyanobacterial production strains.