It has been reported that there are more than 1,000 types of intestinal bacteria and more than 40 trillion intestinal bacteria in the human intestine. The human intestinal microbiota weighs 1.5 to 2 kg and is also called the intestinal flora. About half to one third of feces is of bacterial origin and humans shed 2 to 3 trillion bacteria per day. In recent years, next-generation sequencers have made it possible to accelerate gene analysis of the gut microbiota, and metagenomic analysis. As a result, it has become clear that the composition of the intestinal flora of subjects with digestive diseases as well as various diseases such as metabolic diseases, neurological diseases, allergic diseases, and arteriosclerosis is different from that of healthy subjects. Our studies have revealed links between gastrointestinal disorders such as ulcerative colitis, colorectal adenoma, colorectal cancer, liver cirrhosis, and irritable bowel syndrome and the gut microbiota. Metabolites of gut microbiota have also been suggested to be a related mechanism of gut microbiota, but further detailed investigation is awaited. Furthermore, it has been reported that the intestinal flora may be associated with the onset and treatment outcomes of patients with cancer via intestinal epithelial cells and the intestinal immune system. In this paper, we introduce recent findings regarding immune regulation in the intestine, mainly regarding the involvement of antigen-presenting dendritic cells.

Multiple sclerosis (MS) is an autoimmune disease characterized by chronic inflammation and demyelination in the central nervous system (CNS) that leads to multiple symptoms such as visual disturbance and quadriplegia. Although the etiology of MS remains elusive, it is thought that both genetic and environmental factors are involved and that autoreactive Th17 cells play a pivotal role in disease development. Recently, several groups have reported that MS patients have a distinct microbiota compared to healthy subjects. Studies using the experimental autoimmune encephalomyelitis (EAE), an animal model of MS, have also demonstrated that disease development and severity are attenuated when mice are raised under a germ-free condition. These results indicate that some gut microbes promote the inflammatory responses in the CNS of MS patients and EAE mice. It is, however, unknown what kind of gut microbes are involved in and how they regulate the inflammation in the CNS. In the present study, we demonstrated that two specific gut bacteria coordinately act to promote the CNS inflammation in EAE mice. In EAE mice, myelin-specific Th17 cells migrate from the periphery to the CNS to initiate neuroinflammation. We first found that myelin-specific Th17 cells are recruited to the small intestine and activated by gut bacteria there during EAE development. The Erysipelotrichaceae strain acts like an adjuvant for the Th17 responses increasing the pathogenicity of the autoreactive T cells, and myelin-specific T cells cross-react with UvrA expressed in Lactobacillus reuteri and proliferate upon the stimulation. It is worthy of note that mono-colonization of germ-free mice with these bacteria did not or only slightly increased the susceptibility and clinical symptoms of EAE, pointing to the importance of the synergic effects of their pathogenic functions in fully activating autoimmune T cells. Taken together, our results imply that the microbiota of the small intestine is a potential target for the prevention and treatment of MS. Further studies using human microbes and autoreactive T cells are needed to confirm our findings in MS patients, because mouse and human gut microbes and target myelin epitopes are different

Recently the number of studies of gut microbiome has been rapidly increasing, and the analysis techniques supporting the development of research in this field have been improving in tandem. The mainstream method of gut microbiome analysis started with culture-based methods, progressed to 16S rRNA analysis, and is currently transitioning to metagenomic analysis. Metagenomic analysis has contributed to an increase in the amount of information; however, handling and interpreting metagenomic data is comparatively difficult. Furthermore, there are few reports in the literature which introduce the workflows of metagenomic analyses in detail. In this review, we introduce the various kinds of gut microbiome analysis, the workflows of each method, and the latest analysis techniques for both beginners and advanced learners.