A review of methods for the inference and experimental confirmation of microbial association networks in cheese

Abstract

Interactions among microorganisms deeply affect the dynamics of cheese microbial communities and, as a consequence, multiple aspects of cheese quality, from the production of metabolites affecting the taste, aroma and flavour, to body, texture and colour. Understanding and exploiting interactions among beneficial or detrimental microorganisms is therefore key to managing cheese quality. This is true for the simplest systems (fresh cheeses produced from pasteurized milk using defined starters) and the more so for complex, dynamic systems, like surface ripened cheese produced from raw milk, in which a dynamic succession of diverse microorganisms is essential for obtained the desired combination of sensory properties while guaranteeing safety. Positive (commensalism, protocooperation) and negative (competition, amensalism, predation and parasitism) interactions among members of the cheese biota have been reviewed multiple times. However, even if the complex, multidimensional datasets generated by multi-omic approaches to cheese microbiology and biochemistry are ideally suited for the representation of biotic and metabolic interactions as networks, network science concepts and approaches are rarely applied to cheese microbiology. In this review we illustrate concepts relevant to the description of microbial interactions using a network science framework. Then, we briefly review methods used for the inference and analysis of microbial association networks (MAN) and their potential use in the interpretation of the cheese interactome. Finally, since these methods can only be used for mining microbial associations, we review the experimental methods used to confirm the nature of microbial interactions among cheese microbes.

Keywords: Cheese; Data mining; Microbial association networks; Microbial interactions; Microbiota.