Temperature Shapes Ecological Dynamics in Mixed Culture Fermentations Driven by Two Species of the Saccharomyces Genus

Interactions of Saccharomyces cerevisiae and Lactiplantibacillus plantarum Isolated from Light-Flavor Jiupei at Various Fermentation Temperatures

Chinese Baijiu is a famous fermented alcoholic beverage in China. Interactions between key microorganisms, i.e., Saccharomyces cerevisiae and Lactiplantibacillus plantarum, have recently been reported at specific temperatures. However, empirical evidence of their interactions at various temperatures during fermentation is lacking. The results of this study demonstrated that S. cerevisiae significantly suppressed the viability and lactic acid yield of L. plantarum when they were cocultured above 15 °C. On the other hand, L. plantarum had no pronounced effect on the growth and ethanol yield of S. cerevisiae in coculture systems. S. cerevisiae was the main reducing sugar consumer. Inhibition of lactic acid production was also observed when elevated cell density of L. plantarum was introduced into the coculture system. A proteomic analysis indicated that the enzymes involved in glycolysis, lactate dehydrogenase, and proteins related to phosphoribosyl diphosphate, ribosome, and aminoacyl-tRNA biosynthesis in L. plantarum were less abundant in the coculture system. Collectively, our data demonstrated the antagonistic effect of S. cerevisiae on L. plantarum and provided insights for effective process management in light-flavor Baijiu fermentation.

Shelf life estimation of refrigerated vacuum packed beef accounting for uncertainty

This study estimates the shelf life of vacuum packed beef meat (three muscles: striploin (longissimus thoracis et lumborum, LTL), tenderloin (psoas major, PM) and outside chuck (trapezius thoracis, TT)) at refrigeration temperatures (0 °C-10 °C) based on modelling the growth of two relevant groups of spoilage microorganisms: lactic acid bacteria (LAB) and Enterobacteriaceae. The growth models were developed combining a two-step and a one-step approach. The primary modelling was used to identify the parameters affecting the growth kinetics, guiding the definition of secondary growth models. For LAB, the secondary model included the effect of temperature and initial pH on the specific growth rate. On the other hand, the model for Enterobacteriaceae incorporated the effect of temperature on the specific growth rate and the lag phase; as well as the effect of the initial pH on the specific growth rate, the lag phase and the initial microbial count. We did not observe any significant effect of the type of muscle on the growth kinetics. Once the equations were defined, the models were fitted to the complete dataset using a one-step approach. Model validation was carried out by cross-validation, mitigating the impact of an arbitrary division between training and validation sets. The models were used to estimate the shelf life of the product, based on the maximum admissible microbial concentration (7 log CFU/g for LAB, 5 log CFU/g for Enterobacteriaceae). Although LAB was the dominant microbiota, in several cases, both LAB and Enterobacteriaceae reached the critical concentration practically at the same time. Furthermore, in some scenarios, the end of shelf life would be determined by Enterobacteriaceae, pointing at the potential importance of non-dominant microorganisms for product spoilage. These results can aid in the implementation of effective control measures in the meat processing industry.

Endo metabolomic profiling of flor and wine yeasts reveals a positive correlation between intracellular metabolite load and the specific glycolytic flux during wine fermentation

This study explored the intracellular metabolic variations between 17 strains of Saccharomyces cerevisiae belonging to two different genetic populations: flor and wine yeasts, in the context of alcoholic fermentation. These two populations are closely related as they share the same ecological niche but display distinct genetic characteristics. A protocol was developed for intracellular metabolites extraction and 1H-NMR analysis. This methodology allowed us to identify and quantify 21 intracellular metabolites at two different fermentation steps: the exponential and stationary phases. This work provided evidence of significant differences in the abundance of intracellular metabolites, which are strain- and time-dependent, thus revealing complex interactions. Moreover, the differences in abundance appeared to be correlated with life-history traits such as average cell size and specific glycolytic flux, which revealed unsuspected phenotypic correlations between metabolite load and fermentation activity.

Understanding Wine through Yeast Interactions

Wine is a product of microbial activities and microbe–microbe interactions. Yeasts are the principal microorganisms responsible for the evolution and fulfillment of alcoholic fermentation. Several species and strains coexist and interact with their environment and with each other during the fermentation course. Yeast–yeast interactions occur even from the early stages of fermentation, determining yeast community structure and dynamics during the process. Different types of microbial interactions (e.g., mutualism and commensalism or competition and amensalism) may exert positive or negative effects, respectively, on yeast populations. Interactions are intimately linked to yeast metabolic activities that influence the wine analytical profile and shape the wine character. In this context, much attention has been given during the last years to the interactions between Saccharomyces cerevisiae (SC) and non-Saccharomyces (NS) yeast species with respect to their metabolic contribution to wine quality. Yet, there is still a significant lack of knowledge on the interaction mechanisms modulating yeast behavior during mixed culture fermentation, while much less is known about the interactions between the various NS species or between SC and Saccharomyces non-cerevisiae (SNC) yeasts. There is still much to learn about their metabolic footprints and the genetic mechanisms that alter yeast community equilibrium in favor of one species or another. Gaining deeper insights on yeast interactions in the grape–wine ecosystem sets the grounds for understanding the rules underlying the function of the wine microbial system and provides means to better control and improve oenological practices.