Ammonia is constantly produced as a metabolic waste from amino acid catabolism in mammals. Ammonia, the toxic waste metabolite, is resolved in the liver where the urea cycle converts free ammonia to urea. Liver malfunctions cause hyperammonemia that leads to central nervous system (CNS) dysfunctions, such as brain edema, convulsions, and coma. The current treatments for hyperammonemia, such as antibiotics or lactulose, are designed to decrease the intestinal production of ammonia and/or its absorption into the body and are not effective, besides being often accompanied by side effects. In recent years, increasing evidence has shown that modifications of the gut microbiota could be used to treat hyperammonemia. Considering the role of the gut microbiota and the physiological characteristics of the intestine, the removal of ammonia from the intestine by modulating the gut microbiota would be an ideal approach to treat hyperammonemia. In this review, we discuss the significance of hyperammonemia and its related diseases and the efficacy of the current management methods for hyperammonemia to understand the mechanism of ammonia transport in the human body. The possibility to use the gut microbiota as pharmabiotics to treat hyperammonemia and its related diseases is also explored.

Background The proliferation and survival of microbial organisms including intestinal microbes are determined by their surrounding environments. Contrary to popular myth, the nutritional and chemical compositions, water contents, O2 contents, temperatures, and pH in the gastrointestinal (GI) tract of a human are very different in a location-specific manner, implying heterogeneity of the microbial composition in a location-specific manner. Results We first investigated the environmental conditions at 6 different locations along the GI tract and feces of ten weeks’ old male SPF C57BL/6 mice. As previously known, the pH and water contents of the GI contents at the different locations of the GI tract were very different from each other in a location-specific manner, and none of which were not even similar to those of feces. After confirming the heterogeneous nature of the GI contents in specific locations and feces, we thoroughly analyzed the composition of the microbiome of the GI contents and feces. 16S rDNA-based metagenome sequencing on the GI contents and feces showed the presence of 13 different phyla. The abundance of Firmicutes gradually decreased from the stomach to feces while the abundance of Bacteroidetes gradually increased. The taxonomic α-diversities measured by ACE (Abundance-based Coverage Estimator) richness, Shannon diversity, and Fisher’s alpha all indicated that the diversities of gut microbiome at colon and cecum were much higher than that of feces. The diversities of microbiome compositions were lowest in jejunum and ileum while highest in cecum and colon. Interestingly, the diversities of the fecal microbiome were lower than those of the cecum and colon. Beta diversity analyses by NMDS plots, PCA, and unsupervised hierarchical clustering all showed that the microbiome compositions were very diverse in a location-specific manner. Direct comparison of the fecal microbiome with the microbiome of the whole GI tracts by α-and β-diversities showed that the fecal microbiome did not represent the microbiome of the whole GI tract. Conclusion The fecal microbiome is different from the whole microbiome of the GI tract, contrary to a baseline assumption of contemporary microbiome research work.

The determining factors of the composition of the gut microbiome are one of the main interests in current science. In this work, we compared the effect of diet shift (DS) from heavily relying on meatatarian diets to vegetarian diets and physical exercise (EX) on the composition of the gut microbiome after 3 months. Although both DS and EX affected the composition of the gut microbiome, the patterns of alteration were different. The α-diversity analyzed by InvSimpson, Shannon, Simpson, and Evenness showed that both EX and DS affected the microbiome, causing it to become more diverse, but EX affected the gut microbiome more significantly than DS. The β-diversity analyses indicated that EX and DS modified the gut microbiome in two different directions. Co-occurrence network analysis confirmed that both EX and DS modified the gut microbiome in different directions, although EX modified the gut microbiome more significantly. Most notably, the abundance of Dialister succinatiphilus was upregulated by EX, and the abundances of Bacteroides fragilis, Phascolarctobacterium faecium, and Megasphaera elsdenii were downregulated by both EX and DS. Overall, EX modulated the composition of the gut microbiome more significantly than DS, meaning that host factors are more important in determining the gut microbiome than diets. This work also provides a new theoretical basis for why physical exercise is more health-beneficial than vegetarian diets.

The current gut microbiome research relies on the fecal microbiome under the assumption that the fecal microbiome represents the microbiome of the entire gastrointestinal (GI) tract. However, there have been growing concerns about using feces as a proxy to study the gut microbiome. Here, we comprehensively analyzed the composition of microbiome and metabolites in the feces and at 14 different locations of GI tracts of genetically homogenous sibling pigs to evaluate the validity of using feces as a proxy to the whole gut microbiome. The composition of intestinal microbes constituting the gut microbiome at each intestinal content and feces and their metabolic compositions were thoroughly investigated through metagenome sequencing and an ultraperformance LC-MS/MS, respectively. The fluctuation in the composition of the microbiome in the stomach and the small intestine became stabilized from the large intestine to feces and was able to be categorized into 3 groups. The taxonomic α-diversities measured by ACE (abundance-based coverage estimator) richness and Shannon diversity indicated that the microbiome in the large intestine was much more diverse than those of the small intestine and feces. The highly independent intestinal microbes in the stomach and the small intestine became flourished in the large intestine and converged into a community with tightly connected networks. β-Diversity analyses by NMDS plots, PCA, and unsupervised hierarchical clustering all showed that the diversities of microbiome compositions were lowest in feces while highest in the large intestine. In accordance with fluctuation of the composition of gut microbiome along with the GI tract, the metabolic composition also completely differed in a location-specific manner along with the GI tract. Comparative analysis of the fecal microbiome and metabolites with those of the whole GI tract indicated that fecal microbiome is insufficient to represent the whole gut microbiome.

The precise mechanisms of action of the host’s gut microbiome at the level of its constituting bacteria are obscure in most cases despite its definitive role. To study the precise role of the gut microbiome on the phenotypes of a host by excluding host factors, we analyzed two different gut microbiomes within the same individual mouse after replacing the gut microbiome with a new one to exclude the host factors. The gut microbiome of conventional C57BL/6 mice was randomly reestablished by feeding fecal samples from obese humans to the mice, and depleting their original gut microbiome with an antibiotic and antifungal treatment. Comparison of body weight changes before and 3 months after the replacement of the gut microbiome showed that the gut microbiome replacement affected the body weight gain in three different ways: positive, medium, and negative. The differences in body weight gain were associated with establishment of a different kind of gut microbiome in each of the mice. In addition, body weight gaining was negatively associated with the Firmicutes/Bacteroidetes ratio, which is consistent with previous recent findings. Thorough statistical analysis at low taxonomic levels showed that uncultured bacteria NR_074436.1, NR_144750.1, and NR_0421101.1 were positively associated with body weight gain, while Trichinella pseudospiralis and uncultured bacteria NR_024815.1 and NR_144616.1 were negatively associated. This work shows that replacement of the gut microbiome within the same individual provides an excellent opportunity for the purpose of gut microbiome analysis by excluding the host factors.