Systems Biology, Amsterdam Institute of Molecular and Life Sciences (AIMMS), VU Amsterdam, Amsterdam, The Netherlands.
Department of Bioengineering, IBSB, Marmara University, Istanbul, Turkey.
Biotechnol Bioeng. 2021 Jan;118(1):223-237. doi: 10.1002/bit.27565. Epub 2020 Sep 28.
In this study, we have investigated the cheese starter culture as a microbial community through a question: can the metabolic behaviour of a co-culture be explained by the characterized individual organism that constituted the co-culture? To address this question, the dairy-origin lactic acid bacteria Lactococcus lactis subsp. cremoris, Lactococcus lactis subsp. lactis, Streptococcus thermophilus and Leuconostoc mesenteroides, commonly used in cheese starter cultures, were grown in pure and four different co-cultures. We used a dynamic metabolic modelling approach based on the integration of the genome-scale metabolic networks of the involved organisms to simulate the co-cultures. The strain-specific kinetic parameters of dynamic models were estimated using the pure culture experiments and they were subsequently applied to co-culture models. Biomass, carbon source, lactic acid and most of the amino acid concentration profiles simulated by the co-culture models fit closely to the experimental results and the co-culture models explained the mechanisms behind the dynamic microbial abundance. We then applied the co-culture models to estimate further information on the co-cultures that could not be obtained by the experimental method used. This includes estimation of the profile of various metabolites in the co-culture medium such as flavour compounds produced and the individual organism level metabolic exchange flux profiles, which revealed the potential metabolic interactions between organisms in the co-cultures.
在这项研究中,我们通过一个问题研究了干酪发酵剂微生物群落:共培养物的代谢行为是否可以用构成共培养物的特征化单个生物来解释?为了解决这个问题,我们分别在纯培养和四种不同的共培养物中培养了常用于干酪发酵剂的乳球菌乳亚种、乳球菌乳亚种、嗜热链球菌和肠膜明串珠菌。我们使用了一种基于整合相关生物的基因组尺度代谢网络的动态代谢建模方法来模拟共培养物。使用纯培养实验估计动态模型的菌株特异性动力学参数,然后将其应用于共培养模型。共培养模型模拟的生物量、碳源、乳酸和大多数氨基酸浓度曲线与实验结果非常吻合,并且共培养模型解释了动态微生物丰度背后的机制。然后,我们将共培养模型应用于估计无法通过使用的实验方法获得的关于共培养物的进一步信息。这包括共培养物培养基中各种代谢物(如产生的风味化合物)的分布估计以及单个生物体水平的代谢交换通量分布,这揭示了共培养物中生物体之间潜在的代谢相互作用。