Cellular responsiveness as a predictive indicator for population collapse and autonomous control in continuous cultures of Pseudomonas putida.

Abstract

How microbial populations respond to repeated environmental transitions determines both their ecological fitness and their utility in biotechnological applications. Using Pseudomonas putida KT2440 equipped with fluorescent biosensors and monitored by automated flow cytometry in the Segregostat platform, we show that exposure to benzoate, a plastic-derived aromatic feedstock, progressively reduces cellular responsiveness, defined as the fraction of cells that successfully activate a gene circuit following an environmental transition. Unlike classical switching costs, which promote phenotypic diversification, benzoate suppresses responsiveness without increasing population entropy, in a concentration-dependent and circuit-independent manner tightly correlated with fitness loss. A resource allocation model incorporating the competing demands of benzoate assimilation, toxicity, and tolerance reveals that this impairment emerges from a three-way competition for limited cellular resources. Above a critical benzoate load, insufficient resources remain available to sustain the adaptive reallocation required for circuit activation. In continuous culture, a non-responsive subpopulation accumulates as a leading indicator of population collapse. Exploiting this signal, we implement a two-stage connected bioreactor system in which benzoate feeding is autonomously regulated based on real-time population structure, enabling complete substrate consumption and stable operation at otherwise destabilizing concentrations. These results establish cellular responsiveness as a quantitative population variable and demonstrate that structure-aware feedback control, acting on population composition rather than bulk physiology, provides a principled route toward autonomous bioprocesses on challenging substrates.

Importance: This work contributes at two levels. First, it positions cellular responsiveness as a quantitative and mechanistically grounded population variable. It also shows that cellular responsiveness can be interpreted based on a resource allocation approach, a principle that could be applied to several other biological systems. Second, it demonstrates that monitoring population composition, rather than bulk physiology, provides new engineering principle toward autonomous bioprocess control on challenging substrates.