The budding yeast Saccharomyces cerevisiae has been used as a model organism for the basic mechanism of aging, which provides useful assay systems for measuring both replicative and chronological lifespans. In the course of our screening program for substances that extend replicative lifespan, cucurbitacin B (CuB) was found as a hit compound from a compound library, which contains cerebrosides, phenols, sesquiterpenoid, triterpenoids, and sterols isolated from natural products by our research group. Importantly, it prolonged not only the replicative lifespan but also the chronological lifespan in yeast. CuB increased ATG32 gene expression, suggesting that CuB induces autophagy. Indeed, the GFP signal generated from the cleavage of GFP-Atg8, which is a signature of autophagy, was increased upon CuB treatment. On the other hand, CuB failed to increase the chronological lifespans when either ATG2 or ATG32, essential autophagy genes, was deleted, indicating that the lifespan extension by CuB depends on autophagy induction. Furthermore, CuB significantly increased superoxide dismutase (Sod) activity and the survival rate of yeast under oxidative stress, while it decreased the amount of reactive oxygen species (ROS) and malondialdehyde (MDA) production, indicating that CuB has activity to antagonize oxidative stress. Additionally, CuB did not affect replicative lifespans of sod1, sod2, uth1, and skn7 mutants with the K6001 background, indicating that aging-related genes including SOD1, SOD2, UTH1, and SKN7 participate in the antiaging effect of CuB. These results suggest that CuB exerts antiaging activity by regulating autophagy, ROS, antioxidative ability, and aging-related genes. Finally, we discuss the possible intracellular targets of CuB based on the phenotypic comparison between the CuB and global gene deletion databases.

Objective: Skin microbiomes vary across individuals. They are known to play essential roles in maintaining homeostasis and preventing infectious pathogens.In recent years, cosmetic product development has begun to focus on the relationship between skin microbiomes and skin conditions. However, the statistical methods used in many studies include the standard t-test and small-scale correlation analysis, which do not take into account the internal correlation structure in data on skin microbiomes and skin features. In this study, we aimed to understand the relationship between skin microbiomes and skin features by analysing complex microbiomes and skin data. Methods:We obtained data on 19 skin characteristics and skin microbiomes based on 16s ribosomal RNA (16S rRNA) gene analysis of 276 healthy Japanese women. We then performed the principal component analysis (PCA), a method that takes into account the internal correlation structure, on 234 panels of them that did not contain outliers or missing values. We confirmed the relationship between skin microbiomes and skin features with principal component regression analysis and hierarchical clustering analysis (HCA). Results:The principal component regression analysis showed strong relationships between skin microbiomes and sebum-related skin characteristics and skin pH. In the HCA, the female panel was classified into two major groups based on the skin microbiome. Furthermore, there were significant differences in sebumrelated skin characteristics and the way skin condition changes with ageing between those groups, suggesting the possibility of measuring skin condition and age-related skin risk based on microbiome data. In addition, sebum-related characteristics differed significantly among middle-aged participants, suggesting a strong relationship between skin microbiomes and sebum-related characteristics. Conclusion:Analysis of skin condition and skin microbiome in Japanese women, taking into account the correlation between variables, showed that skin microbiome was significantly related to the number of pores and the amount of