Genome Editing to Generate Sake Yeast Strains with Eight Mutations That Confer Excellent Brewing Characteristics

Abstract

Sake yeast is mostly diploid, so the introduction of recessive mutations to improve brewing characteristics requires considerable effort. To construct sake yeast with multiple excellent brewing characteristics, we used an evidence-based approach that exploits genome editing technology. Our breeding targeted the AWA1, CAR1, MDE1, and FAS2 genes. We introduced eight mutations into standard sake yeast to construct a non-foam-forming strain that makes sake without producing carcinogens or an unpleasant odor, while producing a sweet ginjo aroma. Small-scale fermentation tests showed that the desired sake could be brewed with our genome-edited strains. The existence of a few unexpected genetic perturbations introduced during breeding proved that genome editing technology is extremely effective for the serial breeding of sake yeast.

Quality Evaluation of Drinks Based on Liposome Shape Changes Induced by Flavor Molecules

“…The membrane properties are according to cell, organism, and membrane type . Studies of actual yeast cells reported that cell size differences corresponded to flavor production. ,, Morphological change of liposomes has received considerable attention recently, for example, the morphological change phase diagram is formed by the balance between the surface area and the encapsulation volume in the cell model. , It is well analyzed that the resulting excess surface area and deformation of liposomes due to osmotic pressure make the internal water escape to the surface to reduce the volume, increase the surface area of membrane lipids via isomerization, and so on. − Some studies have shown that membrane deformation occurs because the area of the membrane increases due to stimuli such as heat, and excess surface area is created while maintaining the internal volume. ,,, It has also been reported that liposome transformation is caused by a specific molecular interaction such as detergent , or polyphenol , with membrane lipid molecules. Recently, the impact of solvent selection on the microfluidic production of liposomes was reported .…”

“…28 Studies of actual yeast cells reported that cell size differences corresponded to flavor production. 25,26,29 Morphological change of liposomes has received considerable attention recently, for example, the morphological change phase diagram is formed by the balance between the surface area and the encapsulation volume in the cell model. 30,31 It is well analyzed that the resulting excess surface area and deformation of liposomes due to osmotic pressure make the internal water escape to the surface to reduce the volume, increase the surface area of membrane lipids via isomerization, and so on.…”

Phase Separation in Liposomes Determined by Ergosterol and Classified Using Machine Learning

“…Image analysis can determine the function of biological materials. Previously, our group evaluated the adsorption capacity of activated carbon fibers in a series of scanning electron microscopy studies (Yoda et al, 2018, 2019), and others used an image processing system (e.g., Calmorph26) for the analysis of the functional morphology of yeast cells (Ohya et al, 2005; Chadani et al, 2021). The results of the present study revealed that machine learning for phase separation classification decreased the amount of time spent; however, the accuracy remains low.…”

Assignment of unimodal probability distribution models for quantitative morphological phenotyping

“…Furthermore, we can employ various machine learning approaches to recognize, predict, understand, and obtain data/knowledge. In this way, parametric approaches can be applied in the future to perform correlation analyses to compare morphologies [ 28 ], classification analyses to distinguish categories based on morphology [ 29 ], prediction analyses to identify similar morphologies [ 30 ], factor analyses to explore potential common factors [ 31 ], analysis of morphological diversity for breeding purposes [ 32 , 33 ], and analysis of sources of bias in microfluidic cell culture research [ 34 ].…”