2021
DOI: 10.1101/2021.07.21.453196
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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 i… Show more

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Cited by 11 publications
(14 citation statements)
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References 133 publications
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“…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.…”
Section: Discussionsupporting
confidence: 92%
See 1 more Smart Citation
“…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.…”
Section: Discussionsupporting
confidence: 92%
“…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.…”
Section: Introductionmentioning
confidence: 99%
“…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.…”
Section: Methodsmentioning
confidence: 99%
“…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.…”
Section: Resultsmentioning
confidence: 99%
“…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).…”
Section: Methodsmentioning
confidence: 99%