Garland chrysanthemum consumption ameliorates age-related hearing loss in C57BL/6 mouse; model system to explore hearing loss prevention foods in a short period.

Garland chrysanthemum (Glebionis coronaria L.) is an antioxidant-rich leafy vegetable. We found that garland chrysanthemum consumption ameliorated age-related hearing loss (AHL) in C57BL/6J mice, an early onset model. We also found that AHL progression was significantly ameliorated by three of ten products. Metabolome analysis of the 10 products using nuclear magnetic resonance (NMR) spectroscopy indicated that phytosterols may be involved in the amelioration of AHL. However, the direct inhibitory effect of phytosterol mixture on mouse AHL progression was not identified. These results suggest that garland chrysanthemum consumption delays AHL development in mice and its efficiency varies depending on the source of the product. Our findings also suggest that phytosterol content in garland chrysanthemum functions as an evaluation marker for the efficiency. Furthermore, to accelerate the search for foods that prevent AHL, we have used these data to develop an automatic threshold determination method for auditory brainstem response using machine learning.

Analysis of genomic characteristics and their influence on metabolism in Aspergillus luchuensis albino mutants using genome sequencing.

Black Aspergillus luchuensis and its white albino mutant are essential fungi for making alcoholic beverages in Japan. A large number of industrial strains have been created using novel isolation or gene/genome mutation techniques. Such mutations influence metabolic and phenotypic characteristics in industrial strains, but few comparative studies of inter-strain mutation have been conducted. We carried out comparative genome analyses of 8 industrial strains of A. luchuensis and A. kawachii IFO 4308, the latter being the first albino strain to be isolated. Phylogenetic analysis based on 8938 concatenated genes exposed the diversity of black koji strains and uniformity among albino industrial strains, suggesting that passaged industrial albino strains have more genetic mutations compared with strain IFO 4308 and black koji strains. Comparative analysis showed that the albino strains had mutations in genes not only for conidial pigmentation but also in those that encode N-terminal acetyltransferase A and annexin XIV-like protein. The results also suggest that some mutations may have emerged through subculturing of albino strains. For example, mutations in the genes for isocitrate lyase and sugar transporters were observed only in industrial albino strains. This implies that selective pressure for increasing enzyme activity or secondary metabolites may have influenced the mutation of genes associated with environmental stress responses in A. luchuensis albino strains. Our study clarifies hitherto unknown genetic and metabolic characteristics of A. luchuensis industrial strains and provides potential applications for comparative genome analysis for breeding koji strains.

Fractional parentage analysis and a scale-free reproductive network of brown trout.

In this study, we developed a method of fractional parentage analysis using microsatellite markers. We propose a method for calculating parentage probability, which considers missing data and genotyping errors due to null alleles and other causes, by regarding observed alleles as realizations of random variables which take values in the set of alleles at the locus and developing a method for simultaneously estimating the true and null allele frequencies of all alleles at each locus. We then applied our proposed method to a large sample collected from a wild population of brown trout (Salmo trutta). On analyzing the data using our method, we found that the reproductive success of brown trout obeyed a power law, indicating that when the parent-offspring relationship is regarded as a link, the reproductive system of brown trout is a scale-free network. Characteristics of the reproductive network of brown trout include individuals with large bodies as hubs in the network and different power exponents of degree distributions between males and females.

The effects of vegetable pickling conditions on the dynamics of microbiota and metabolites.

BACKGROUND: Salting is a traditional procedure for producing pickled vegetables. Salting can be used as a pretreatment, for safe lactic acid fermentation and for salt stock preparation. This study aimed to provide valuable knowledge to improve pickle production by investigating the dynamics of microbiota and metabolites during the pretreatment and salt stock preparation processes, which have previously been overlooked. The differences in these process conditions would be expected to change the microbiota and consequently influence the content of metabolites in pickles. METHODS: Samples, collected from eight commercial pickle manufacturers in Japan, consisted of the initial raw materials, pickled vegetables and used brine. The microbiota were analyzed by 16S rRNA sequencing and the metabolites quantified by liquid chromatograph-mass spectrometry. Statistical analyses helped to identify any significant differences between samples from the initial raw materials, pretreatment process and salt stock preparation process groups. RESULTS: Under pretreatment conditions, aerobic and facultative anaerobic bacteria were predominant, including Vibrio, a potentially undesirable genus for pickle production. Under salt stock preparation conditions, the presence of halophilic bacteria, Halanaerobium, suggested their involvement in the increase in pyruvate derivatives such as branched-chain amino acids (BCAA). PICRUSt analysis indicated that the enhanced production of BCAA in salt stock was caused not by quantitative but by qualitative differences in the biosynthetic pathway of BCAA in the microbiota. CONCLUSION: The differences in the microbiota between pretreatment and previously studied lactic acid fermentation processes emphasized the importance of anaerobic conditions and low pH under moderate salinity conditions for assuring safe pickle production. The results from the salt stock preparation process suggested that the Halanaerobium present may provide a key enzyme in the BCAA biosynthetic pathway which prefers NADH as a coenzyme. This feature can enhance BCAA production under anaerobic conditions where NADH is in excess. The effects shown in this study will be important for adjusting pickling conditions by changing the abundance of bacteria to improve the quality of pickled vegetables.

The relationships between microbiota and the amino acids and organic acids in commercial vegetable pickle fermented in rice-bran beds.

The microbial community during fermented vegetable production has a large impact on the quality of the final products. Lactic acid bacteria have been well-studied in such processes, but knowledge about the roles of non-lactic acid bacteria is limited. This study aimed to provide useful knowledge about the relationships between the microbiota, including non-lactic acid bacteria, and metabolites in commercial pickle production by investigating Japanese pickles fermented in rice-bran. The samples were provided by six manufacturers, divided into two groups depending on the production conditions. The microbiological content of these samples was investigated by high-throughput sequencing, and metabolites were assessed by liquid chromatography-mass spectrometry and enzymatic assay. The data suggest that Halomonas, halophilic Gram-negative bacteria, can increase glutamic acid content during the pickling process under selective conditions for bacterial growth. In contrast, in less selective conditions, the microbiota consumed glutamic acid. Our results indicate that the glutamic acid content in fermented pickle is influenced by the microbiota, rather than by externally added glutamic acid. Our data suggest that both lactic acid bacteria and non-lactic acid bacteria are positive key factors in the mechanism of commercial vegetable fermentation and affect the quality of pickles.

Taxon Richness of "Megaviridae" Exceeds those of Bacteria and Archaea in the Ocean.

Since the discovery of the giant mimivirus, evolutionarily related viruses have been isolated or identified from various environments. Phylogenetic analyses of this group of viruses, tentatively referred to as the family "Megaviridae", suggest that it has an ancient origin that may predate the emergence of major eukaryotic lineages. Environmental genomics has since revealed that Megaviridae represents one of the most abundant and diverse groups of viruses in the ocean. In the present study, we compared the taxon richness and phylogenetic diversity of Megaviridae, Bacteria, and Archaea using DNA-dependent RNA polymerase as a common marker gene. By leveraging existing microbial metagenomic data, we found higher richness and phylogenetic diversity in this single viral family than in the two prokaryotic domains. We also obtained results showing that the evolutionary rate alone cannot account for the observed high diversity of Megaviridae lineages. These results suggest that the Megaviridae family has a deep co-evolutionary history with diverse marine protists since the early "Big-Bang" radiation of the eukaryotic tree of life.

Maximum margin classifier working in a set of strings.

Numbers and numerical vectors account for a large portion of data. However, recently, the amount of string data generated has increased dramatically. Consequently, classifying string data is a common problem in many fields. The most widely used approach to this problem is to convert strings into numerical vectors using string kernels and subsequently apply a support vector machine that works in a numerical vector space. However, this non-one-to-one conversion involves a loss of information and makes it impossible to evaluate, using probability theory, the generalization error of a learning machine, considering that the given data to train and test the machine are strings generated according to probability laws. In this study, we approach this classification problem by constructing a classifier that works in a set of strings. To evaluate the generalization error of such a classifier theoretically, probability theory for strings is required. Therefore, we first extend a limit theorem for a consensus sequence of strings demonstrated by one of the authors and co-workers in a previous study. Using the obtained result, we then demonstrate that our learning machine classifies strings in an asymptotically optimal manner. Furthermore, we demonstrate the usefulness of our machine in practical data analysis by applying it to predicting protein-protein interactions using amino acid sequences and classifying RNAs by the secondary structure using nucleotide sequences.

Quantifying biodiversity and asymptotics for a sequence of random strings.

We present a methodology for quantifying biodiversity at the sequence level by developing the probability theory on a set of strings. Further, we apply our methodology to the problem of quantifying the population diversity of microorganisms in several extreme environments and digestive organs and reveal the relation between microbial diversity and various environmental parameters.