Univ Brest, Laboratoire Universitaire de Biodiversité et Ecologie Microbienne, UMT ALTER'iX, Quimper, France.
Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France.
Appl Environ Microbiol. 2019 May 2;85(10). doi: 10.1128/AEM.00322-19. Print 2019 May 15.
Spore-forming bacteria are natural contaminants of food raw materials, and sporulation can occur in many environments from farm to fork. In order to characterize and to predict spore formation over time, we developed a model that describes both the kinetics of growth and the differentiation of vegetative cells into spores. The model is based on a classical growth model and enables description of the kinetics of sporulation with the addition of three parameters specific to sporulation. Two parameters are related to the probability of each vegetative cell to commit to sporulation and to form a spore, and the last one is related to the time needed to form a spore once the cell is committed to sporulation. The goodness of fit of this growth-sporulation model was assessed using growth-sporulation kinetics at various temperatures in laboratory medium or in whey for , , and The model accurately describes the kinetics in these different conditions, with a mean error lower than 0.78 log CFU/ml for the growth and 1.08 log CFU/ml for the sporulation. The biological meaning of the parameters was validated with a derivative strain of 168 which produces green fluorescent protein at the initiation of sporulation. This model provides physiological information on the spore formation and on the temporal abilities of vegetative cells to differentiate into spores and reveals the heterogeneity of spore formation during and after growth. The growth-sporulation model describes the progressive transition from vegetative cells to spores with sporulation parameters describing the sporulation potential of each vegetative cell. Consequently, the model constitutes an interesting tool to assess the sporulation potential of a bacterial population over time with accurate parameters such as the time needed to obtain one resistant spore and the probability of sporulation. Further, this model can be used to assess these data under various environmental conditions in order to better identify the conditions favorable for sporulation regarding the time to obtain the first spore and/or the concentrations of spores which could be reached during a food process.
产芽孢细菌是食品原料的天然污染物，并且在从农场到餐桌的许多环境中都可能发生芽孢形成。为了描述和预测随时间的芽孢形成，我们开发了一种模型，该模型描述了生长的动力学和营养细胞向芽孢的分化。该模型基于经典的生长模型，并通过添加三个特定于芽孢形成的参数来描述芽孢形成的动力学。两个参数与每个营养细胞进行芽孢形成并形成芽孢的概率有关，最后一个参数与细胞进行芽孢形成后形成芽孢所需的时间有关。使用实验室培养基或乳清中不同温度下的生长-芽孢形成动力学来评估该生长-芽孢形成模型的拟合优度， 和 。该模型准确地描述了这些不同条件下的动力学，生长的平均误差低于 0.78 log CFU/ml，芽孢形成的平均误差低于 1.08 log CFU/ml。通过 168 的衍生菌株验证了参数的生物学意义，该菌株在芽孢形成开始时产生绿色荧光蛋白。该模型提供了关于芽孢形成和营养细胞在时间上分化为芽孢的能力的生理学信息，并揭示了生长过程中和生长后的芽孢形成的异质性。生长-芽孢形成模型描述了从营养细胞到芽孢的渐进过渡，芽孢形成参数描述了每个营养细胞的芽孢形成潜力。因此，该模型是一种评估随时间变化的细菌群体的芽孢形成潜力的有趣工具，具有准确的参数，例如获得一个抗性芽孢所需的时间和芽孢形成的概率。此外，该模型可以用于在各种环境条件下评估这些数据，以便更好地确定与获得第一个芽孢的时间和/或在食品加工过程中可能达到的芽孢浓度有关的芽孢形成的有利条件。
