Microbial association networks in cheese: a meta-analysis

Abstract

Interactions among starter and non-starter microorganisms (starter bacteria, naturally occurring or intentionally added non-starter bacteria, yeasts and filamentous fungi, spoilage and pathogenic microorganisms and, finally bacteriophages and even arthropods) deeply affect the dynamics of cheese microbial communities and, as a consequence, multiple aspects of cheese quality, from metabolites affecting the taste, aroma and flavor, to body, texture and color. Understanding and exploiting microbial interactions is therefore key to managing cheese quality. This is true for the simplest systems (fresh cheeses produced from pasteurized milk using defined starters composed solely of Lactic Acid Bacteria) 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) among members of the cheese biota have been reviewed multiple times. Although 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 first illustrate concepts relevant to the description of microbial interaction networks using network science concepts. Then, we briefly review methods used for the inference and analysis of microbial association networks and their potential use in the interpretation of the cheese interactome. Since these methods can only be used for mining microbial associations, a review of the experimental methods used to confirm the nature of microbial interactions among cheese microbes. Finally, we demonstrate the potential of microbial association network inference by mining metataxonomic data stored in the public database DairyFMBN, a specialized version of FoodMicrobionet which collates data on 74 metataxonomic studies on dairy products. Microbial association networks were inferred from 34 studies on cheese with up to 4 different methods and the results discussed to evaluate several aspects (choice of method, level of taxonomic resolution for the analysis, network, node and edge properties) which provide insight on the usefulness of this approach as explorative tool in the detection of microbial interactions in cheese.

Metataxonomic Mapping of the Microbial Diversity of Irish and Eastern Mediterranean Cheeses

“…In addition to the detected negative associations, we also found positive associations between L. delbrueckii and Streptococcus . These results are in agreement with Charlet and colleagues, [ 63 ], and Parente and colleagues [ 3 ], indicating that these interactions are critical for their co-administration as starters in cheese-making, to prevent pathogenic or spoilage bacteria growth.…”

“…During cheese ripening, complex and dynamic interactions take place among the members of the microbial community. The specific microbial patterns that are developed as a result of these interactions influence the taste and flavor, and affect the quality of the final product [ 3 ]. The use of pasteurized milk and defined starter cultures simplifies the composition and the dynamics of the microbial communities.…”

Extensive diversity and rapid turnover of phage defense repertoires in cheese-associated bacterial communities

“…To determine the species composition of the metagenomic samples, we used MetaPhlAn (v3.0.7-1) (69). For the 16S rRNA gene amplicon sequencing datasets we used the extensive FoodMicrobionet (8.2020) collection and analysis pipeline (see script section) (70, 71). Overall, 185 species were identified.…”

FoodMicrobionet v4: a large, integrated, open and transparent database for food bacterial communities

“…Use cases and example workflows have been illustrated in a previous work (Parente et al, 2019) and we have tried to demonstrate this approach in a series of proof-of-concept metastudies (Parente et al, 2020, 2021; Zotta et al, 2021). The code for generating graphs and statistical analyses is fully reproducible and reusable (https://data.mendeley.com/datasets/8fwwjpm79y/4; https://github.com/ep142/MAN_in_cheese) and allows to reproduce the figures and tables whenever a new version of FoodMicrobionet is published.…”

“…Use cases and example workflows have been illustrated in a previous work (Parente et al, 2019) and we have tried to demonstrate this approach in a series of proof-of-concept metastudies (Parente et al, 2020(Parente et al, , 2021Zotta et al, 2021). The code for generating graphs and statistical analyses is fully reproducible and reusable 1).…”