Effects of Platycodon grandiflorum on Gut Microbiome and Immune System of Immunosuppressed Mouse.

Platycodon grandiflorum (PG) is a perennial plant that has been used as a traditional remedy to control immune-related diseases. PG was steamed and dried to improve its taste (PGS). The aim of the study was to investigate the effects of PG and PGS (PG-diets) on the gut microbiome and immune system. We treated PG-diets to immunosuppressed mice via cyclophosphamide (CPA) injection. After two weeks of the supplement, we evaluated specific genera related to body weight and serum immunoglobulin levels and analyzed 16S rRNA sequencing and metagenomics statistical analysis. PG-diets groups showed an increased abundance of microorganisms in immunodeficient mice compared to the control group (NC). Moreover, Akkermansia significantly decreased in response to the CPA in the NC group at the genus level, whereas its abundance increased in the PG-diets groups. We also found that the modulation of the gut microbiome by PG-diets was correlated with body weight, IgA, and IgM levels. The results demonstrate that PG-diets may improve the health benefits of immunosuppressed mice by altering the gut microbiome, though not much difference was found between PG and PGS treatments. Finally, this is the first study showing the effects of PGS-diets on the gut microbiome and immune system as a potential nourishing immunity supplement.

Establishment and evaluation of prediction model for multiple disease classification based on gut microbial data.

Diseases prediction has been performed by machine learning approaches with various biological data. One of the representative data is the gut microbial community, which interacts with the host's immune system. The abundance of a few microorganisms has been used as markers to predict diverse diseases. In this study, we hypothesized that multi-classification using machine learning approach could distinguish the gut microbiome from following six diseases: multiple sclerosis, juvenile idiopathic arthritis, myalgic encephalomyelitis/chronic fatigue syndrome, acquired immune deficiency syndrome, stroke and colorectal cancer. We used the abundance of microorganisms at five taxonomy levels as features in 696 samples collected from different studies to establish the best prediction model. We built classification models based on four multi-class classifiers and two feature selection methods including a forward selection and a backward elimination. As a result, we found that the performance of classification is improved as we use the lower taxonomy levels of features; the highest performance was observed at the genus level. Among four classifiers, LogitBoost-based prediction model outperformed other classifiers. Also, we suggested the optimal feature subsets at the genus-level obtained by backward elimination. We believe the selected feature subsets could be used as markers to distinguish various diseases simultaneously. The finding in this study suggests the potential use of selected features for the diagnosis of several diseases.