Microbial Small-Talk: Does Quorum Sensing Play a Role in Beer Fermentation?

Britton, Scott J.; Neven, Hedwig; Maskell, Dawn Louise

doi:10.1080/03610470.2020.1843928

Cited by 4 publications

(2 citation statements)

References 126 publications

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“…This flocculation behavior is suggested by these authors to be a protective mechanism mediated by Try-OH, where mutual adhesion leads to the formation of flocs that prevent diffusion and thus provide physical protection for the innermost cells from harmful compounds in the external environment. Since flocculence requires a fairly large microbial population and Flo adhesin expression is only functional at higher population densities, it stands to reason that this particular adaptive feature, flocculation behavior in S. cerevisiae, occurs in cells exposed to Try-OH (Britton et al, 2020).…”

Section: Response Mechanism Of Qsms In S Cerevisiaementioning

confidence: 99%

Quorum sensing: cell-to-cell communication in Saccharomyces cerevisiae

Li,

Pan,

Zhang

et al. 2023

Front. Microbiol.

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Quorum sensing (QS) is one of the most well-studied cell-to-cell communication mechanisms in microorganisms. This intercellular communication process in Saccharomyces cerevisiae began to attract more and more attention for researchers since 2006, and phenylethanol, tryptophol, and tyrosol have been proven to be the main quorum sensing molecules (QSMs) of S. cerevisiae. In this paper, the research history and hotspots of QS in S. cerevisiae are reviewed, in particular, the QS system of S. cerevisiae is introduced from the aspects of regulation mechanism of QSMs synthesis, influencing factors of QSMs production, and response mechanism of QSMs. Finally, the employment of QS in adaptation to stress, fermentation products increasing, and food preservation in S. cerevisiae was reviewed. This review will be useful for investigating the microbial interactions of S. cerevisiae, will be helpful for the fermentation process in which yeast participates, and will provide an important reference for future research on S. cerevisiae QS.

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Section: Response Mechanism Of Qsms In S Cerevisiaementioning

confidence: 99%

Quorum sensing: cell-to-cell communication in Saccharomyces cerevisiae

Li,

Pan,

Zhang

et al. 2023

Front. Microbiol.

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“…Saccharomyces cerevisiae , an industrially relevant budding yeast, can undergo a reversible stress-responsive transition from individual ellipsoidal cells (yeast form) to filaments of thinly elongated cells (filamentous form) in response to nitrogen- or carbon starvation (Gimeno et al 1992 , Mösch and Fink 1997 , Lengeler et al 2000 , Mösch 2000 , Ceccato-Antonini and Sudbery 2004 , Etschmann and Schrader 2006 , Lodolo et al 2008 , Britton et al 2021 , Wauters et al 2021 ). This phenotypic switch allows microorganisms to undergo rapid microevolution to adapt to constantly changing microenvironments (Jain and Hasan 2018 ).…”

Section: Introductionmentioning

confidence: 99%

HYPHAEdelity: a quantitative image analysis tool for assessing peripheral whole colony filamentation

Britton

Rogers

White

et al. 2022

FEMS Yeast Research

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The yeast Saccharomyces cerevisiae, also known as brewer's yeast, can undergo a reversible stress-responsive transition from individual ellipsoidal cells to chains of elongated cells in response to nitrogen- or carbon starvation. Whole colony morphology is frequently used to evaluate phenotypic switching response; however, quantifying two-dimensional top-down images requires each pixel to be characterized as belonging to the colony or background. While feasible for a small number of colonies, this labor-intensive assessment process is impracticable for larger datasets. The software tool HYPHAEdelity has been developed to semi-automate the assessment of two-dimensional whole colony images and quantify the magnitude of peripheral whole colony yeast filamentation using image analysis tools intrinsic to the OpenCV Python library. The software application functions by determining the total area of filamentous growth, referred to as the f-measure, by subtracting the area of the inner colony boundary from the outer-boundary area associated with hyphal projections. The HYPHAEdelity application was validated against automated and manually pixel-counted two-dimensional top-down images of S. cerevisiae colonies exhibiting varying degrees of filamentation. HYPHAEdelity's f-measure results were comparable to areas determined through a manual pixel enumeration method and found to be more accurate than other whole colony filamentation software solutions.

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