The recent development of research on lactic acid bacteria and bifidobacteria in the 30th anniversary of the establishment of the Japan Society for Lactic Acid Bacteria is remarkable. In particular, it is no exaggeration to say that the field of research on bifidobacteria is one of the fields that have developed remarkably in the past 10 years or so. That is, the metabolic pathway of bifidobacteria for human milk oligosaccharides has been clarified, and especially, the research on the transporter of them has been advanced, and the relation with the formation of Bifidus flora in the intestine of the host has been clarified. Furthermore, a rational mechanism for cross-feeding between different bifidobacterial species has been elucidated on the basis of transporter knowledge. In addition, degrading enzymes for indigestible carbohydrates and adhesion factor to the intestine, which are thought to be provided as a survival strategy for bifidobacteria in an oligotrophic environment, have been found. Moreover, their anti-aging effects and involvement in diseases such as Alzheimer’s disease and Parkinson’s disease have been suggested. Studies of the structure and function of extracellular polysaccharides secreted by lactic acid bacteria as probiotics have also progressed remarkably.

These new findings are reviewed in this article.

Data deposition of the newly determined nucleotide sequences in the public sequence database is considered to be prerequisite for the publication in most of the scientific journals. The deposited sequence data can be accessed, used, and redistributed without any restriction by any party. Hence, the public sequence database has contributed as a data infrastructure in life science. The first whole-genome sequence of lactic acid bacteria was determined in the early 2000s, and for 20 years since then, more than 5,000 genome sequences for lactic acid bacteria have become publicly available. In this paper, I will review the advancement of the genome research of lactic acid bacteria by overviewing the genomic data deposited in the public sequence database.

Antimicrobials usage in livestock industry is important to stabilize the meat production and maintain the livestock health. On the other hands, the risk for developing the antimicrobial-resistant bacteria was concerned in human and livestock medical care, alternative agents required for sustainable meat supply. Among the alternative agents, probiotics is one of the major candidates. In general, the efficacy of probiotics is summarized in three functions. First, maintenance of gut health in host including the immune modulation. Second, preventing the pathogen colonization in the intestine by competitive exclusion. Third, improving the meat production by the metabolites producing by probiotic bacteria and/or the gut microbiota such as short-chain fatty acids. In this review, the author was described the application of probiotics not only for alternative agents of antimicrobials, but also the preventive effect of viral disease and the alleviating effect of indigenous microbiota disturbance by antimicrobial administration.

Although the effect of probiotic administration was depended on the immune status and indigenous gut microbiota in livestock, it is necessary to always consider the health condition of the livestock such as disease infection and stress. However, probiotic usage will be developed in the livestock industry because of its flexibility.

Gene expression data refers to a numerical matrix in which genes are arranged in each row and samples in each column, and each element stores numerical information on the amount of expression. In this paper, we use the numerical matrix which measured the acid stress response of Lactobacillus rhamnosus GG, consisting of 2,949 genes × 9 samples. We first describe the basic usage of RStudio, an integrated development environment for R. Next, we discuss the significance of clustering samples having similar expression patterns, and the interpretation of the results. For gene clustering of RNA-seq data, we outline MBCluster.Seq, a representative package for the purpose. Finally, we introduce a modified version of the package (called MBCdeg) that can also be used for detecting differentially expressed genes (DEGs). Supplementary materials are available online at https://www.iu.a.u-tokyo.ac.jp/kadota/r_seq2.html.