Masahiko Nonaka

A microorganism producing transglutaminase was screened as an indication of hydroxamateform ing activity. The microbial transglutaminase was purified from the culture filtrate of the strain, S-8112, which was supposed to belong to the genus Streptoverticillium. The molecular weight of the purified enzyme was found to be about 40, 000 on SDS-polyacrylamide gel electrophoresis, the isoelectric point 8.9 and the optimal pH of the reaction 6-7. The present enzyme requires no calcium ions for its activity. Thus, it clearly differs from known transglutaminases derived from mammalian organs, which have been defined as calcium-dependent enzymes.

αs1-Casein and soybean globulins were polymerized and gelatinized by Ca2+-independent transglutaminase that was isolated from the culture filtrate of a microorganism thought to belong to Streptoverticillium sp. of actinomycetes. This enzyme polymerized such albumins as bovine serum albumin, human serum albumin and conalbumin in the presence of dithiothreitol. Rabbit myosin was polymerized by the present emzyme but actin was not. An RP-HPLC analysis after enzymic digestion of the polymerized αs1-casein showed existence of the ε-(γ-Glu)Lys bond. Thus, it was confirmed that the polymerization was formed by a catalytic reaction of the transglutaminase.

αs1-Casein and soybean globulins were polymerized and gelatinized by Ca2+-independent transglutaminase that was isolated from the culture filtrate of a microorganism thought to belong to Streptoverticillium sp. of actinomycetes. This enzyme polymerized such albumins as bovine serum albumin, human serum albumin and conalbumin in the presence of dithiothreitol. Rabbit myosin was polymerized by the present emzyme but actin was not. An RP-HPLC analysis after enzymic digestion of the polymerized asl -casein showed existence of the £-(y-Glu)Lys bond. Thus, it was confirmed that the polymerization was formed by a catalytic reaction of the transglutaminase.

The aim of this study was to conceptualize a new dish design process used by highly reputable chefs at fine dining restaurants, using cognitive modeling methods, and prioritize the important culinary success factors (CSFs) of the cognitive structures involved in creating new dishes characteristic to Japanese chefs of Japanese and French cuisine in fine dining restaurants. We asked 12 chefs of Japanese cuisine and 7 chefs of French cuisine at fine dining restaurants to answer questionnaires designed according to the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. All participants were Japanese. We preselected CSFs via text mining, using the laddering method in discussions with 9 chefs of Japanese cuisine about 54 new dishes that they had created. We identified 10 CSFs, as follows: (1) utilization of main ingredient texture, (2) utilization of main ingredient flavor, (3) utilization of main ingredient umami, (4) featured main ingredient, (5) good pairings (complements) between main and secondary ingredients, (6) not too rich, (7) good balance, (8) cuisine more Japanese in style, (9) elegance, and (10) surprise. We then created a DEMATEL diagram as a visual representation of each chef’s thinking pattern with respect to dish creation. In the average diagram of chefs of Japanese cuisine, “utilization of main ingredient flavor” held the greatest importance and was influenced most by “cuisine more Japanese in style” in dish creation. Therefore, making cuisine more Japanese in style would result in chefs of Japanese cuisine using the main ingredient’s flavor. Therefore, chefs of Japanese cuisine believed that when a chef prioritized using the main ingredient’s flavor in the creation of Japanese cuisine, the new dish would be valuable. In addition, the average diagram of chefs of French cuisine was created and compared to that of chefs of Japanese cuisine. This study shows that the cognitive analysis of highly reputable chefs at fine dining restaurants can provide cognitive models of dish creation for 10 CSFs of Japanese chefs of Japanese and French cuisine and can be used as references for beginners creating the new dishes.

Irritable bowel syndrome (IBS) is diagnosed by subjective clinical symptoms. We aimed to establish an objective IBS prediction model based on gut microbiome analyses employing machine learning. We collected fecal samples and clinical data from 85 adult patients who met the Rome III criteria for IBS, as well as from 26 healthy controls. The fecal gut microbiome profiles were analyzed by 16S ribosomal RNA sequencing, and the determination of short-chain fatty acids was performed by gas chromatography–mass spectrometry. The IBS prediction model based on gut microbiome data after machine learning was validated for its consistency for clinical diagnosis. The fecal microbiome alpha-diversity indices were significantly smaller in the IBS group than in the healthy controls. The amount of propionic acid and the difference between butyric acid and valerate were significantly higher in the IBS group than in the healthy controls (p < 0.05). Using LASSO logistic regression, we extracted a featured group of bacteria to distinguish IBS patients from healthy controls. Using the data for these featured bacteria, we established a prediction model for identifying IBS patients by machine learning (sensitivity >80%; specificity >90%). Gut microbiome analysis using machine learning is useful for identifying patients with IBS.