Irritable bowel syndrome (IBS) is diagnosed by subjective clinical symptoms. We aimed to establish an objective IBS prediction model based on gut microbiome analyses employing machine learning. We collected fecal samples and clinical data from 85 adult patients who met the Rome III criteria for IBS, as well as from 26 healthy controls. The fecal gut microbiome profiles were analyzed by 16S ribosomal RNA sequencing, and the determination of short-chain fatty acids was performed by gas chromatography–mass spectrometry. The IBS prediction model based on gut microbiome data after machine learning was validated for its consistency for clinical diagnosis. The fecal microbiome alpha-diversity indices were significantly smaller in the IBS group than in the healthy controls. The amount of propionic acid and the difference between butyric acid and valerate were significantly higher in the IBS group than in the healthy controls (p < 0.05). Using LASSO logistic regression, we extracted a featured group of bacteria to distinguish IBS patients from healthy controls. Using the data for these featured bacteria, we established a prediction model for identifying IBS patients by machine learning (sensitivity >80%; specificity >90%). Gut microbiome analysis using machine learning is useful for identifying patients with IBS.

The purpose of this study was to establish reference ranges for gut microbial indices by collecting real-world Japanese microbiome data from a Mykinso cohort. Although several large cohort studies have focused on the human gut microbiome, large cohort studies of the gut microbiome from Japanese populations are scarce, especially from healthy or non-diseased individuals. We collected stool samples and original survey lifestyle information from 5,843 Japanese individuals through the Mykinso gut microbiome testing service. From the obtained 16S rRNA sequence data derived from stool samples, the ratio and distribution of each taxon were analyzed. The relationship between different epidemiological attributes and gut microbial indicators were statistically analyzed. The qualitative and quantitative indicators of these common gut microbiota were confirmed to be strongly correlated with age, sex, constipation/diarrhea, and history of lifestyle-related diseases. Therefore, we set up a healthy sub-cohort that controlled for these attribute factors and defined reference ranges from the distribution of gut microbial index in that population. Taken together, these results show that the gut microbiota of Japanese people had high beta-diversity, with no single "typical" gut microbiota type. We believe that the reference ranges for the gut microbial indices obtained in this study can be new reference values for determining the balance and health of the gut microbiota of an individual. In the future, it is necessary to clarify the clinical validity of these reference values by comparing them with a clinical disease cohort.

Understanding hydrocarbon cycling in the subsurface is important in various disciplines including climate science, energy resources and astrobiology. Mud volcanoes provide insights into biogeochemical processes occurring in the subsurface. They are usually associated with natural gas reservoirs consisting mainly of methane and other hydrocarbons as well as CO₂. Stable isotopes have been used to decipher the sources and sinks of hydrocarbons in the subsurface, although the interpretation can be ambiguous due to the numerous processes involved. Here we report new data for hydrocarbon isotope analysis, including position-specific isotope composition of propane, for samples from the Tokamachi mud volcano area, Japan. The data suggest that C₂₊ hydrocarbons are being biodegraded, with indirect production of methane (“secondary methanogenesis”). Data from chemical and isotopic composition are discussed with regard to 16S rRNA analysis, which exhibits the presence of hydrogenotrophic and acetoclastic methoanogens. Overall, the combination of isotopologue analysis with 16S rRNA gene data allows refining of our understanding of hydrocarbon cycling in subsurface environments.

Hot spring associated phototrophic microbial mats are purely microbial communities, in which phototrophic bacteria function as primary producers and thus shape the community. The microbial mats at Nakabusa hot springs in Japan harbor diverse photosynthetic bacteria, mainly Thermosynechococcus, Chloroflexus, and Roseiflexus, which use light of different wavelength for energy conversion. The aim of this study was to investigate the effect of the phototrophs on biodiversity and community composition in hot spring microbial mats. For this, we specifically activated the different phototrophs by irradiating the mats with different wavelengths in situ. We used 625, 730, and 890 nm wavelength LEDs alone or in combination and confirmed the hypothesized increase in relative abundance of different phototrophs by 16S rRNA gene sequencing. In addition to the increase of the targeted phototrophs, we studied the effect of the different treatments on chemotrophic members. The specific activation of Thermosynechococcus led to increased abundance of several other bacteria, whereas wavelengths specific to Chloroflexus and Roseiflexus induced a decrease in >50% of the community members as compared to the dark conditions. This suggests that the growth of Thermosynechococcus at the surface layer benefits many community members, whereas less benefit is obtained from an increase in filamentous anoxygenic phototrophs Chloroflexus and Roseiflexus. The increases in relative abundance of chemotrophs under different light conditions suggest a relationship between the two groups. Aerobic chemoheterotrophs such as Thermus sp. and Meiothermus sp. are thought to benefit from aerobic conditions and organic carbon in the form of photosynthates by Thermosynechococcus, while the oxidation of sulfide and production of elemental sulfur by filamentous anoxygenic phototrophs benefit the sulfur-disproportionating Caldimicrobium thiodismutans. In this study, we used an experimental approach under controlled environmental conditions for the analysis of natural microbial communities, which proved to be a powerful tool to study interspecies relationships in the microbiome.

Microbial community assembly is shaped by deterministic and stochastic processes, but the relationship between these processes and the environment is not understood. Here we describe a rule for the determinism and stochasticity of microbial community assembly affected by the environment using in silico, in situ, and ex situ experiments. The in silico experiment with a simple mathematical model showed that the existence of essential symbiotic microorganisms caused stochastic microbial community assembly, unless the community was exposed to a non-adapted nutritional concentration. Then, a deterministic assembly occurred due to the low number of microorganisms adapted to the environment. In the in situ experiment in the middle of a river, the microbial community composition was relatively deterministic after the drastic environmental change caused by the treated wastewater contamination, as analyzed by 16S rRNA gene sequencing. Furthermore, by culturing microbial communities collected from the upstream natural area and downstream urban area of the river in test tubes with varying carbon source concentrations, the upstream community assembly became deterministic with high carbon concentrations while the downstream community assembly became deterministic with low carbon concentrations. These results suggest that large environmental changes, which are different from the original environment, result in a deterministic microbial community assembly.

Among the various biological devices developed and characterized in synthetic biology, light-sensing biological devices can serve as an input--output system owing to their light modulation property. The well-characterized devices in living systems are useful for modulating cellular sensing and transducing information. In this study, we examined short pulse responsiveness of a light-sensing two-component system (TCS), Cph8-OmpR, which was generated by replacing the sensor domain of the EnvZ-OmpR osmoregulatory system with the light sensor Cph1. We varied the input pulse width of the Cph8-OmpR system and found that an input width of <1 s was sufficient to alter the accumulation of a reporter gene upregulated by Cph8 phosphorylation of OmpR. Based on this result and the mathematical model showing that the timescale for the upstream Cph8-activity transition was much faster than that of downstream gene expression, we evaluated the merit of a TCS with such an unbalanced cascade. Our mathematical simulation of a cascade TCS suggests that high-frequency noise arising from fast transitions in kinase activity was attenuated throughout the cascade reaction. In terms of noise attenuation, these results can contribute to analyze biological cascade systems with the balance of reaction rates in each process.