Dynamic co‐culture metabolic models reveal the fermentation dynamics, metabolic capacities and interplays of cheese starter cultures

Abstract

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 cocultures. We used a dynamic metabolic modelling approach based on the integration of the genome-scale metabolic networks of the involved organisms to simulate the cocultures. 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 coculture 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. K E Y W O R D S co-culture metabolic modelling, genome-scale metabolic network, lactic acid bacteria, starter cultures 1 | INTRODUCTION Milk has been processed by humankind for millennia and cheese is one of the oldest fermented dairy food (Salque et al., 2013). Cheese is traditionally made either by the lactic acid bacteria (LAB) naturally present in milk or by the back-slopping technique, which is adding a small portion of a previous batch of cheese to milk. On the other hand, in industrial cheese production, defined mixtures of purified and characterized LAB, known as starter cultures, are used to standardize the bulk production (Cogan & Hill, 1993; Leroy &

Microbial Interactions within the Cheese Ecosystem and Their Application to Improve Quality and Safety

“…Some microorganisms, such as Arthrobacter, Corynebacterium , yeasts, etc., produce siderophores to help take up these essential trace elements, while siderophore-deficient bacteria such as Brevibacterium and Microbacterium , etc., have molecular systems that help them to “steal” siderophores from their producers [ 84 , 144 ]. Understanding these interactions is essential, for instance, to selecting starter species and strains (or mixtures of strains) with efficient metal acquisition systems [ 145 , 146 ], which will allow them to strive for growth in dairy systems.…”

“…Occasionally, Gram-negative bacteria such as H. alvei , Proteus vulgaris or Psychrobacter celer can also be included, aiming at enhancing the production of volatile sulfur compounds [ 233 , 234 ]. At present, the design of such cultures is mostly empirical, and neither the biotic interactions between the different taxa involved nor the effects of abiotic factors are currently taken into account, which very commonly results in a colonization failure [ 145 , 235 , 236 ]. Scientifically sound, microbiota-based, multi-species starters composed of LAB and non-LAB species, and, if required, of eukaryotic organisms, would provide enzymatic activities that LAB alone do not possess, thus contributing to expanding the textural and flavor patterns of the cheeses produced with them.…”

The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale

“…In some cases, dynamic community metabolic models can be generated for co-cultures using each organism's genome-scale metabolic network. These dynamic simulations are able to predict metabolite concentration profiles for the community as well metabolic exchange flux profiles for individual organisms [55]. Along a similar vein, communities are able to partition metabolic functions among community members like in cases of auxotrophy.…”

Eco-Evolutionary Dynamics in Microbial Communities from Spontaneous Fermented Foods

“…Communities from milk-fermented foods seem like a suitable platform to create models to generate and verify predictions from an eco-evolutionary perspective: those communities contain a few key species of lactic acid bacteria, yeast and viruses with known patterns of metabolism and inter-species interactions. However, this field of using modelling in food microbiology has just recently opened [ 83 , 84 ]. Taken together, observations of different patterns allow us to generate hypotheses and predictions that are testable using experimental communities.…”