Characterization of Yeasts Isolated from Parmigiano Reggiano Cheese Natural Whey Starter: From Spoilage Agents to Potential Cell Factories for Whey Valorization

Whey is the main byproduct of the dairy industry and contains sugars (lactose) and proteins (especially serum proteins and, at lesser extent, residual caseins), which can be valorized by the fermentative action of yeasts. In the present study, we characterized the spoilage yeast population inhabiting natural whey starter (NWS), the undefined starter culture of thermophilic lactic acid bacteria used in Parmigiano Reggiano (PR) cheesemaking, and evaluated thermotolerance, mating type, and the aptitude to produce ethanol and bioactive peptides from whey lactose and proteins, respectively, in a selected pool of strains. PCR-RFLP assay of ribosomal ITS regions and phylogenetic analysis of 26S rDNA D1/D2 domains showed that PR NWS yeast population consists of the well-documented Kluyveromyces marxianus, as well as of other species (Saccharomyces cerevisiae, Wickerhamiella pararugosa, and Torulaspora delbrueckii), with multiple biotypes scored within each species as demonstrated by (GTG)5-based MSP-PCR. Haploid and diploid K. marxianus strains were identified through MAT genotyping, while thermotolerance assay allowed the selection of strains suitable to grow up to 48 °C. In whey fermentation trials, one thermotolerant strain was suitable to release ethanol with a fermentation efficiency of 86.5%, while another candidate was able to produce the highest amounts of both ethanol and bioactive peptides with potentially anti-hypertensive function. The present work demonstrated that PR NWS is a reservoir of ethanol and bioactive peptides producer yeasts, which can be exploited to valorize whey, in agreement with the principles of circularity and sustainability.

Characterization of Yeasts Isolated From Parmigiano Reggiano Cheese Natural Whey Starter: From Spoilage Agents to Potential Cell Factories for Whey Valorization

Whey is the main by-product of the dairy industry and contains sugars (lactose) and proteins (especially serum proteins and, at lesser extent, residual caseins), which can be valorized by the fermentative action of yeasts. In the present study, we characterized the spoilage yeast fraction inhabiting natural whey starter (NWS), the undefined starter culture of thermophilic lactic acid bacteria used in Parmigiano Reggiano (PR) cheesemaking, and evaluated thermotolerance, mating type, and the aptitude to produce ethanol and bioactive peptides from whey lactose and proteins, respectively, in a selected pool of strains. We found that PR NWS yeast population consists of other species (Saccharomyces cerevisiae, Wickerhamiella pararugosa, and Torulaspora delbrueckii) in addition to the well-documented Kluyveromyces marxianus, with multiple biotypes scored within each species. Haploid and diploid K. marxianus strains were identified through MAT genotyping, while thermotolerance assay allowed the selection of strains suitable to grow up to 48 °C. In whey fermentation assay, one thermotolerant strain was suitable to release ethanol with yield of 86.5%, while another candidate was able to produce the highest amounts of both ethanol and bioactive peptides with potentially anti-hypertensive function. The present work demonstrated that PR NWS is a reservoir of ethanol and bioactive peptides producer yeasts, which can be exploited to valorize whey, in agreement with the principles of circularity and sustainability.

The N.C.Yeastract and CommunityYeastract databases to study gene and genomic transcription regulation in non-conventional yeasts

ABSTRACT Responding to the recent interest of the yeast research community in non-Saccharomyces cerevisiae species of biotechnological relevance, the N.C.Yeastract (http://yeastract-plus.org/ncyeastract/) was associated to YEASTRACT + (http://yeastract-plus.org/). The YEASTRACT + portal is a curated repository of known regulatory associations between transcription factors (TFs) and target genes in yeasts. N.C.Yeastract gathers all published regulatory associations and TF-binding sites for Komagataellaphaffii (formerly Pichia pastoris), the oleaginous yeast Yarrowia lipolytica, the lactose fermenting species Kluyveromyces lactis and Kluyveromyces marxianus, and the remarkably weak acid-tolerant food spoilage yeast Zygosaccharomyces bailii. The objective of this review paper is to advertise the update of the existing information since the release of N.C.Yeastract in 2019, and to raise awareness in the community about its potential to help the day-to-day work on these species, exploring all the information available in the global YEASTRACT + portal. Using simple and widely used examples, a guided exploitation is offered for several tools: (i) inference of orthologous genes; (ii) search for putative TF binding sites and (iii) inter-species comparison of transcription regulatory networks and prediction of TF-regulated networks based on documented regulatory associations available in YEASTRACT + for well-studied species. The usage potentialities of the new CommunityYeastract platform by the yeast community are also discussed.

Prediction of Genetic Groups within Brettanomyces bruxellensis through Cell Morphology Using a Deep Learning Tool

Brettanomyces bruxellensis is described as a wine spoilage yeast with many mainly strain-dependent genetic characteristics, bestowing tolerance against environmental stresses and persistence during the winemaking process. Thus, it is essential to discriminate B. bruxellensis isolates at the strain level in order to predict their stress resistance capacities. Few predictive tools are available to reveal intraspecific diversity within B. bruxellensis species; also, they require expertise and can be expensive. In this study, a Random Amplified Polymorphic DNA (RAPD) adapted PCR method was used with three different primers to discriminate 74 different B. bruxellensis isolates. High correlation between the results of this method using the primer OPA-09 and those of a previous microsatellite analysis was obtained, allowing us to cluster the isolates among four genetic groups more quickly and cheaply than microsatellite analysis. To make analysis even faster, we further investigated the correlation suggested in a previous study between genetic groups and cell polymorphism using the analysis of optical microscopy images via deep learning. A Convolutional Neural Network (CNN) was trained to predict the genetic group of B. bruxellensis isolates with 96.6% accuracy. These methods make intraspecific discrimination among B. bruxellensis species faster, simpler and less costly. These results open up very promising new perspectives in oenology for the study of microbial ecosystems.

Toward application of biocontrol to inhibit wine spoilage yeasts: The use of statistical designs for screening and optimisation

Spoilage yeasts generate considerable economic losses in the wine industry, and although sulphur dioxide (SO2) is traditionally used for control, its use has become controversial because of its negative effects on health. Biocontrol has emerged as a partial alternative to SO2, and most research has focused on the selection of biocontrol yeasts and/or the mechanisms involved, while little research has been directed to the environmental conditions that make biocontrol effective for application. When there are two or more interacting yeasts, the physicochemical factors that affect their antagonism are many and therefore the application of biocontrol is complex. To reduce SO2, the present study aimed to elucidate biocontrol mechanisms of two yeast interactions and to establish optimal physicochemical conditions for biocontrol of the spoilage yeast during grape must fermentation. Through the use of statistical design, it was possible to find relevant physicochemical factors and optimise them. Wickerhamomyces anomalus “BWa156” developed an active supernatant against ZygoSaccharomyces rouxii “BZr6” while supernatant from Metschnikowia pulcherrima “BMp29” was ineffective. In mixed must fermentations, the first interaction (BWa156 vs. BZr6) showed fewer physicochemical factors impacting biocontrol compared to the second interaction (BMp29 vs. BZr6). However, the fewer factors of the first interaction had a stronger effect on the decline in the spoilage population. Validations showed that the optimal conditions for biocontrol with the first interaction could be predicted. Analysis of the results with BWa156 vs. BZr6 and BMp29 vs. BZr6 suggests that the first interaction is a competition that includes a killer toxin, while the second interaction involves competition for iron resources. Response surface methodology (RSM) allowed a reduction in the number of experiments and permitted to find the optimal biocontrol conditions (SO2: 0 mg mL-1; pH: 3.7; Reducing sugars: 23 °Brix) for the interaction between BWa156 and BZr6.

Growth Prediction of the Food Spoilage Yeast Debaryomyces Hansenii using Multivariate Data Analysis

The main aim of the present study was to predict the growth of the food spoilage yeast Debaryomyces hansenii by multivariate data analysis (MVDA) using temperature, pH and NaCl concentration as growth parameters. Growth of five strains of D. hansenii (DHI, DHII, DHIII, DHIV and DHV) was measured as optical density at 620 nm (OD620) at different values of temperature, pH and NaCl concentrations. It was found that salt was the most important factor, which affects yeast growth followed by temperature. The growth of all yeast strains was reduced by increasing salt concentration and decreasing temperature. On the other hand, pH was found to have a little effect on the growth of D. hansenii. Strain DHII was the most salt-tolerant strains among the five yeast strains investigated. Partial least squares (PLS) prediction model was created out using pH, temperature and NaCl concentration to predict the growth of D. hansenii. The model was acceptable with a correlation of 0.86. The developed PLS model will help in optimizing the food process conditions that will prevent food spoilage by D. hansenii.

Growth Prediction of the Food Spoilage Yeast Debaryomyces Hansenii using Multivariate Data Analysis

The main aim of the present study was to predict the growth of the food spoilage yeast Debaryomyces hansenii by multivariate data analysis (MVDA) using temperature, pH and NaCl concentration as growth parameters. Growth of five strains of D. hansenii (DHI, DHII, DHIII, DHIV and DHV) was measured as optical density at 620 nm (OD620) at different values of temperature, pH and NaCl concentrations. It was found that salt was the most important factor, which affects yeast growth followed by temperature. The growth of all yeast strains was reduced by increasing salt concentration and decreasing temperature. On the other hand, pH was found to have a little effect on the growth of D. hansenii. Strain DHII was the most salt-tolerant strains among the five yeast strains investigated. Partial least squares (PLS) prediction model was created out using pH, temperature and NaCl concentration to predict the growth of D. hansenii. The model was acceptable with a correlation of 0.86. The developed PLS model will help in optimizing the food process conditions that will prevent food spoilage by D. hansenii.

Growth Inhibitory Effect of Garlic Powder and Cinnamon Extract on White Colony-Forming Yeast in Kimchi

White colony-forming yeast (WCFY), also referred to as film forming yeast or spoilage yeast, that appear on the surface of kimchi can deteriorate the sensory properties of kimchi, such as odor and texture. Thus, the aim of this study was to develop a method to inhibit the formation of the white colony in kimchi. First, alterations in kimchi manufacturing and storage conditions, including temperatures, pH, salinity, and anaerobic condition, were investigated to determine if they could inhibit the growth of WCFY (i.e., Kazachstania servazzii, Candida sake, Debaryomyces hansenii, Pichia kudriavzevii, and Hanseniaspora uvarum). Thereafter, the anti yeast activity of freeze-dried garlic powder (FGP) and cinnamon ethanol extract (CEE) was evaluated against WCFY using the agar-well diffusion assay. Following the direct application of FGP and CEE to the surface of the kimchi, the inhibitory effects on white colony were determined. The results showed that WCFY can grow under various manufacturing and storage conditions of kimchi. Regarding the growth inhibitory effect on WCFY, FGP exhibited anti yeast activity against four WCFYs. It did not show anti yeast activity against K. servazzii. However, CEE showed anti yeast activity against K. servazzii. In particular, the mixture of 10% FGP and 1.75% CEE, which was manufactured considering the influence of sensory properties in kimchi, exhibited anti yeast activity against all WCFY. Furthermore, the application of the FGP and CEE mixture supplemented with 0.02% xanthan gum to kimchi to enhance adhesion to the kimchi surface, led to a delay in the formation of a white colony on the surface of the kimchi by an average of 17 d at 10 °C compared to the control group. Collectively, the use of a FGP, CEE, and xanthan gum mixture could be an effective method for the inhibition of white colony formation on the surface of kimchi, extending its shelf life.

Assessing the Biofilm Formation Capacity of the Wine Spoilage Yeast Brettanomyces bruxellensis through FTIR Spectroscopy

Brettanomyces bruxellensis is a wine spoilage yeast known to colonize and persist in production cellars. However, knowledge on the biofilm formation capacity of B. bruxellensis remains limited. The present study investigated the biofilm formation of 11 B. bruxellensis strains on stainless steel coupons after 3 h of incubation in an aqueous solution. FTIR analysis was performed for both planktonic and attached cells, while comparison of the obtained spectra revealed chemical groups implicated in the biofilm formation process. The increased region corresponding to polysaccharides and lipids clearly discriminated the obtained spectra, while the absorption peaks at the specific wavenumbers possibly reveal the presence of β-glucans, mannas and ergosterol. Unsupervised clustering and supervised classification were employed to identify the important wavenumbers of the whole spectra. The fact that all the metabolic fingerprints of the attached versus the planktonic cells were similar within the same cell phenotype class and different between the two phenotypes, implies a clear separation of the cell phenotype; supported by the results of the developed classification model. This study represents the first to succeed at applying a non-invasive technique to reveal the metabolic fingerprint implicated in the biofilm formation capacity of B. bruxellensis, underlying the homogenous mechanism within the yeast species.