Ramen Consumption and Gut Microbiota Diversity in Japanese Women: Cross-Sectional Data from the NEXIS Cohort Study.

A cross-sectional study involving 224 healthy Japanese adult females explored the relationship between ramen intake, gut microbiota diversity, and blood biochemistry. Using a stepwise regression model, ramen intake was inversely associated with gut microbiome alpha diversity after adjusting for related factors, including diets, Age, BMI, and stool habits (ß = -0.018; r = -0.15 for Shannon index). The intake group of ramen was inversely associated with dietary nutrients and dietary fiber compared with the no-intake group of ramen. Sugar intake, Dorea as a short-chain fatty acid (SCFA)-producing gut microbiota, and γ-glutamyl transferase as a liver function marker were directly associated with ramen intake after adjustment for related factors including diets, gut microbiota, and blood chemistry using a stepwise logistic regression model, whereas Dorea is inconsistently less abundant in the ramen group. In conclusion, the increased ramen was associated with decreased gut bacterial diversity accompanying a perturbation of Dorea through the dietary nutrients, gut microbiota, and blood chemistry, while the methodological limitations existed in a cross-sectional study. People with frequent ramen eating habits need to take measures to consume various nutrients to maintain and improve their health, and dietary management can be applied to the dietary feature in ramen consumption.

DruMAP: A Novel Drug Metabolism and Pharmacokinetics Analysis Platform.

We developed a novel drug metabolism and pharmacokinetics (DMPK) analysis platform named DruMAP. This platform consists of a database for DMPK parameters and programs that can predict many DMPK parameters based on the chemical structure of a compound. The DruMAP database includes curated DMPK parameters from public sources and in-house experimental data obtained under standardized conditions; it also stores predicted DMPK parameters produced by our prediction programs. Users can predict several DMPK parameters simultaneously for novel compounds not found in the database. Furthermore, the highly flexible search system enables users to search for compounds as they desire. The current version of DruMAP comprises more than 30,000 chemical compounds, about 40,000 activity values (collected from public databases and in-house data), and about 600,000 predicted values. Our platform provides a simple tool for searching and predicting DMPK parameters and is expected to contribute to the acceleration of new drug development. DruMAP can be freely accessed at: https://drumap.nibiohn.go.jp/.

Characteristic Gut Bacteria in High Barley Consuming Japanese Individuals without Hypertension.

BACKGROUND: Barley, a grain rich in soluble dietary fiber ß-glucan, is expected to lower blood pressure. Conversely, individual differences in its effects on the host might be an issue, and gut bacterial composition may be a determinant. METHODS: Using data from a cross-sectional study, we examined whether the gut bacterial composition could explain the classification of a population with hypertension risks despite their high barley consumption. Participants with high barley intake and no occurrence of hypertension were defined as "responders" (n = 26), whereas participants with high barley intake and hypertension risks were defined as "non-responders" (n = 39). RESULTS: 16S rRNA gene sequencing revealed that feces from the responders presented higher levels of Faecalibacterium, Ruminococcaceae UCG-013, Lachnospira, and Subdoligranulum and lower levels of Lachnoclostridium and Prevotella 9 than that from non-responders. We further created a machine-learning responder classification model using random forest based on gut bacteria with an area under the curve value of 0.75 for estimating the effect of barley on the development of hypertension. CONCLUSIONS: Our findings establish a link between the gut bacteria characteristics and the predicted control of blood pressure provided by barley intake, thereby providing a framework for the future development of personalized dietary strategies.

Identification of Human Gut Microbiome Associated with Enterolignan Production.

Dietary plant lignans are converted inside the gut to enterolignans enterodiol (ED) and enterolactone (EL), which have several biological functions, and health benefits. In this study, we characterized the gut microbiome composition associated with enterolignan production using data from a cross-sectional study in the Japanese population. We identified enterolignan producers by measuring ED and EL levels in subject's serum using liquid chromatography-tandem mass spectrometry. Enterolignan producers show more abundant proportion of Ruminococcaceae and Lachnospiraceae than non-enterolignan producers. In particular, subjects with EL in their serum had a highly diverse gut microbiome that was rich in Ruminococcaceae and Rikenellaceae. Moreover, we built a random forest classification model to classify subjects to either EL producers or not using three characteristic bacteria. In conclusion, our analysis revealed the composition of gut microbiome that is associated with lignan metabolism. We also confirmed that it can be used to classify the microbiome ability to metabolize lignan using machine learning approach.

Utilizing public and private sector data to build better machine learning models for the prediction of pharmacokinetic parameters.

One solution to compensate for the shortage of publicly available data is to collect more quality-controlled data from the private sector through public-private partnerships. However, several issues must be resolved before implementing such a system. Here, we review the technical aspects of public-private partnerships using our initiative in Japan as an example. In particular, we focus on the procedure for collecting data from multiple private sector companies and building prediction models and discuss how merging public and private sector datasets will help to improve the chemical space coverage and prediction performance. Teaser: Japan's first public-private consortium in pharmacokinetics has incorporated data from multiple pharmaceutical companies to create useful predictive models.

Oral administration of Blautia wexlerae ameliorates obesity and type 2 diabetes via metabolic remodeling of the gut microbiota.

The gut microbiome is an important determinant in various diseases. Here we perform a cross-sectional study of Japanese adults and identify the Blautia genus, especially B. wexlerae, as a commensal bacterium that is inversely correlated with obesity and type 2 diabetes mellitus. Oral administration of B. wexlerae to mice induce metabolic changes and anti-inflammatory effects that decrease both high-fat diet-induced obesity and diabetes. The beneficial effects of B. wexlerae are correlated with unique amino-acid metabolism to produce S-adenosylmethionine, acetylcholine, and L-ornithine and carbohydrate metabolism resulting in the accumulation of amylopectin and production of succinate, lactate, and acetate, with simultaneous modification of the gut bacterial composition. These findings reveal unique regulatory pathways of host and microbial metabolism that may provide novel strategies in preventive and therapeutic approaches for metabolic disorders.

Dietary Vitamin B1 Intake Influences Gut Microbial Community and the Consequent Production of Short-Chain Fatty Acids.

The gut microbiota is closely related to good health; thus, there have been extensive efforts dedicated to improving health by controlling the gut microbial environment. Probiotics and prebiotics are being developed to support a healthier intestinal environment. However, much work remains to be performed to provide effective solutions to overcome individual differences in the gut microbial community. This study examined the importance of nutrients, other than dietary fiber, on the survival of gut bacteria in high-health-conscious populations. We found that vitamin B1, which is an essential nutrient for humans, had a significant effect on the survival and competition of bacteria in the symbiotic gut microbiota. In particular, sufficient dietary vitamin B1 intake affects the relative abundance of Ruminococcaceae, and these bacteria have proven to require dietary vitamin B1 because they lack the de novo vitamin B1 synthetic pathway. Moreover, we demonstrated that vitamin B1 is involved in the production of butyrate, along with the amount of acetate in the intestinal environment. We established the causality of possible associations and obtained mechanical insight, through in vivo murine experiments and in silico pathway analyses. These findings serve as a reference to support the development of methods to establish optimal intestinal environment conditions for healthy lifestyles.

Classification of the Occurrence of Dyslipidemia Based on Gut Bacteria Related to Barley Intake.

Barley is a grain rich in ß-glucan, a soluble dietary fiber, and its consumption can help maintain good health and reduce the risk of metabolic disorders, such as dyslipidemia. However, the effect of barley intake on the risk of dyslipidemia has been found to vary among individuals. Differences in gut bacteria among individuals may be a determining factor since dietary fiber is metabolized by gut bacteria and then converted into short-chain fatty acids with physiological functions that reduce the risk of dyslipidemia. This study examined whether gut bacteria explained individual differences in the effects of barley intake on dyslipidemia using data from a cross-sectional study. In this study, participants with high barley intake and no dyslipidemia were labeled as "responders" to the reduced risk of dyslipidemia based on their barley intake and their gut bacteria. The results of the 16S rRNA gene sequencing showed that the fecal samples of responders (n = 22) were richer in Bifidobacterium, Faecalibacterium, Ruminococcus 1, Subdoligranulum, Ruminococcaceae UCG-013, and Lachnospira than those of non-responders (n = 43), who had high barley intake but symptoms of dyslipidemia. These results indicate the presence of certain gut bacteria that define barley responders. Therefore, we attempted to generate a gut bacteria-based responder classification model through machine learning using random forest. The area under the curve value of the classification model in estimating the effect of barley on the occurrence of dyslipidemia in the host was 0.792 and the Matthews correlation coefficient was 0.56. Our findings connect gut bacteria to individual differences in the effects of barley on lipid metabolism, which could assist in developing personalized dietary strategies.

Relationships between barley consumption and gut microbiome characteristics in a healthy Japanese population: a cross-sectional study.

BACKGROUND: Barley contains abundant soluble beta-glucan fibers, which have established health benefits. In addition, the health benefits conferred by the gut bacteria have attracted considerable interest. However, few studies have focused on the barley consumption and gut bacteria of the Japanese population. In this study, we aimed to identify the relationship between the barley consumption and gut bacteria composition of the Japanese population. METHODS: In total, 236 participants were recruited in Japan, and 94 participants with no complications of diabetes, hypertension, or dyslipidemia were selected for the study. We analyzed fecal samples from the participants, their medical check-up results, and responses to questionnaires about dietary habits. The participants were grouped according to their median barley consumption. Then, we assessed the relative abundance of 50 genera. Characteristic bacteria were evaluated for their relationship with barley consumption by multiple regression analysis, adjusted for disease and dietary habits, in all participants. We also analyzed the networks and clustering of the 20 selected genera. RESULTS: According to the comparison between barley groups, Bifidobacterium, Butyricicoccus, Collinsella, Ruminococcus 2, and Dialister were characteristic candidate bacterias of the group that consumed large amounts of barley (P < 0.05). The relationship between barley consumption and Bifidobacterium remained after adjusting for disease and dietary habits, and that of Butyricicoccus remained after adjusting for disease. Furthermore, network and cluster analyses revealed that barley consumption was directly correlated with Bifidobacterium and Butyricicoccus. CONCLUSIONS: Barley consumption generates changes in the intestinal bacteria of the Japanese population. We found that Bifidobacterium and Butyricicoccus abundance was positively associated with barley consumption.

Comprehensive analysis of gut microbiota of a healthy population and covariates affecting microbial variation in two large Japanese cohorts.

BACKGROUND: Inter-individual variations in gut microbiota composition are observed even among healthy populations. The gut microbiota may exhibit a unique composition depending on the country of origin and race of individuals. To comprehensively understand the link between healthy gut microbiota and host state, it is beneficial to conduct large-scale cohort studies. The aim of the present study was to elucidate the integrated and non-redundant factors associated with gut microbiota composition within the Japanese population by 16S rRNA sequencing of fecal samples and questionnaire-based covariate analysis. RESULTS: A total of 1596 healthy Japanese individuals participated in this study via two independent cohorts, NIBIOHN cohort (n = 954) and MORINAGA cohort (n = 642). Gut microbiota composition was described and the interaction of these microorganisms with metadata parameters such as anthropometric measurements, bowel habits, medical history, and lifestyle were obtained. Thirteen genera, including Alistipes, Anaerostipes, Bacteroides, Bifidobacterium, Blautia, Eubacterium halli group, Faecalibacterium, Fusicatenibacter, Lachnoclostridium, Parabacteroides, Prevotella_9, Roseburia, and Subdoligranulum were predominant among the two cohorts. On the basis of univariate analysis for overall microbiome variation, 18 matching variables exhibited significant association in both cohorts. A stepwise redundancy analysis revealed that there were four common covariates, Bristol Stool Scale (BSS) scores, gender, age, and defecation frequency, displaying non-redundant association with gut microbial variance. CONCLUSIONS: We conducted a comprehensive analysis of gut microbiota in healthy Japanese individuals, based on two independent cohorts, and obtained reliable evidence that questionnaire-based covariates such as frequency of bowel movement and specific dietary habit affects the microbial composition of the gut. To our knowledge, this was the first study to investigate integrated and non-redundant factors associated with gut microbiota among Japanese populations.

Relationship between Nutrient Intake and Human Gut Microbiota in Monozygotic Twins.

Background and Objectives: The gut microbiota is associated with human health and dietary nutrition. Various studies have been reported in this regard, but it is difficult to clearly analyze human gut microbiota as individual differences are significant. The causes of these individual differences in intestinal microflora are genetic and/or environmental. In this study, we focused on differences between identical twins in Japan to clarify the effects of nutrients consumed on the entire gut microbiome, while excluding genetic differences. Materials and Methods: We selected healthy Japanese monozygotic twins for the study and confirmed their zygosity by matching 15 short tandem repeat loci. Their fecal samples were subjected to 16S rRNA sequencing and bioinformatics analyses to identify and compare the fluctuations in intestinal bacteria. Results: We identified 12 genera sensitive to environmental factors, and found that Lactobacillus was relatively unaffected by environmental factors. Moreover, we identified protein, fat, and some nutrient intake that can affect 12 genera, which have been identified to be more sensitive to environmental factors. Among the 12 genera, Bacteroides had a positive correlation with retinol equivalent intake (rs = 0.38), Lachnospira had a significantly negative correlation with protein, sodium, iron, vitamin D, vitamin B6, and vitamin B12 intake (rs = -0.38, -0.41, -0.39, -0.63, -0.42, -0.49, respectively), Lachnospiraceae ND3007 group had a positive correlation with fat intake (rs = 0.39), and Lachnospiraceae UCG-008 group had a negative correlation with the saturated fatty acid intake (rs = -0.45). Conclusions: Our study is the first to focus on the relationship between human gut microbiota and nutrient intake using samples from Japanese twins to exclude the effects of genetic factors. These findings will broaden our understanding of the more intuitive relationship between nutrient intake and the gut microbiota and can be a useful basis for finding useful biomarkers that contribute to human health.

Development of an In Silico Prediction Model for P-glycoprotein Efflux Potential in Brain Capillary Endothelial Cells toward the Prediction of Brain Penetration.

Developing in silico models to predict the brain penetration of drugs remains a challenge owing to the intricate involvement of multiple transport systems in the blood brain barrier, and the necessity to consider a combination of multiple pharmacokinetic parameters. P-glycoprotein (P-gp) is one of the most important transporters affecting the brain penetration of drugs. Here, we developed an in silico prediction model for P-gp efflux potential in brain capillary endothelial cells (BCEC). Using the representative values of P-gp net efflux ratio in BCEC, we proposed a novel prediction system for brain-to-plasma concentration ratio (Kp,brain) and unbound brain-to-plasma concentration ratio (Kp,uu,brain) of P-gp substrates. We validated the proposed prediction system using newly acquired experimental brain penetration data of 28 P-gp substrates. Our system improved the predictive accuracy of brain penetration of drugs using only chemical structure information compared with that of previous studies.

A public-private partnership to enrich the development of in silico predictive models for pharmacokinetic and cardiotoxic properties.

A novel framework for a public-private (PP) partnership was established by a national initiative of the Development of a Drug Discovery Informatics System, supported by the Japan Agency for Medical Research and Development (AMED). This informatics PP partnership consortium comprised private and public sectors. A database of pharmacokinetic (PK) and cardiotoxic properties was developed, with considerable expansion after integrating proprietary data from private-sector members. This database led to robust in silico prediction models with higher performance than those from the original database. This partnership is a unique example worldwide and could substantially strengthen drug discovery capabilities in both sectors.

Gut microbial composition in patients with atrial fibrillation: effects of diet and drugs.

Atrial fibrillation (AF) reduces the quality of life by triggering stroke and heart failure. The association between AF onset and gut metabolites suggests a causal relationship between AF and gut microbiota dysbiosis; however, the relationship remains poorly understood. We prospectively enrolled 34 hospitalized patients with AF and 66 age-, sex-, and comorbidity-matched control subjects without a history of AF. Gut microbial compositions were evaluated by amplicon sequencing targeting the 16S ribosomal RNA gene. We assessed differences in dietary habits by using a brief-type self-administered diet history questionnaire (BDHQ). Gut microbial richness was lower in AF patients, although the diversity of gut microbiota did not differ between the two groups. At the genus level, Enterobacter was depleted, while Parabacteroides, Lachnoclostridium, Streptococcus, and Alistipes were enriched in AF patients compared to control subjects. The BDHQ revealed that the intake of n-3 polyunsaturated fatty acids and eicosadienoic acid was higher in AF patients. Our results suggested that AF patients had altered gut microbial composition in connection with dietary habits.

MANTA, an integrative database and analysis platform that relates microbiome and phenotypic data.

With an ever-increasing interest in understanding the relationships between the microbiota and the host, more tools to map, analyze and interpret these relationships have been developed. Most of these tools, however, focus on taxonomic profiling and comparative analysis among groups, with very few analytical tools designed to correlate microbiota and the host phenotypic data. We have developed a software program for creating a web-based integrative database and analysis platform called MANTA (Microbiota And pheNoType correlation Analysis platform). In addition to storing the data, MANTA is equipped with an intuitive user interface that can be used to correlate the microbial composition with phenotypic parameters. Using a case study, we demonstrated that MANTA was able to quickly identify the significant correlations between microbial abundances and phenotypes that are supported by previous studies. Moreover, MANTA enabled the users to quick access locally stored data that can help interpret microbiota-phenotype relations. MANTA is available at https://mizuguchilab.org/manta/ for download and the source code can be found at https://github.com/chenyian-nibio/manta.

2 × 2 16-ch silicon photonics wavelength-selective switch based on waveguide gratings.

We demonstrate a 2 × 2 16-ch silicon photonics wavelength-selective switch consisting of contra-directional couplers and thermo-optic Mach-Zehnder switches. The contra-directional couplers are based on sidewall corrugated Bragg gratings with an unlimited free spectral range and thus enable the device to operate over a very wide wavelength range of the C- and L-band. We obtain a fiber-to-fiber insertion loss of 9.2 dB, an on-chip loss of 5.4 dB, a 3-dB bandwidth of 4.2 nm, a bar-port extinction of 15.0 dB, and a cross-port extinction of 23.0 dB, all in 16-ch average. An average trimming power for the bar states and an average switching power for the cross states are 3.8 mW (σ = 2.3 mW) and 15.6 mW (σ = 2.1 mW), respectively. The wavelength dependence of the spectral responses and the resonant effect from the apodization are discussed in detail to show how to further improve the spectral performance.

Inter-layer light transition in hybrid III-V/Si waveguides integrated by µ-transfer printing.

We demonstrate low-loss and broadband light transition from III-V functional layers to a Si platform via two-stage adiabatic-crossing coupler waveguides. A 900-µm-long and 2.7-µm-thick III-V film waveguide consisting of a GaInAsP core and InP cladding layers is transferred onto an air-cladding Si photonic chip by the µ-transfer printing (µ-TP) method. An average optical coupling loss per joint of 1.26 dB is obtained in C + L telecommunication bands (1530-1635 nm). The correlation between alignment offset and measured optical coupling loss is discussed with the frequency distribution of µ-TP samples. We also performed a photoluminescence measurement to investigate the material properties in the GaInAsP layer to see if they are distorted by the strong bending stress produced during the pick-up and print steps of the µ-TP process. The peak intensity reduction of 80-90% and a wavelength shift of 0-5 nm (blue shift) were observed after the process. The series of fundamental studies presented here, which combine multiple analyses, contribute to improving our understanding of III-V/Si photonic integration by µ-TP.

Development of an in silico prediction system of human renal excretion and clearance from chemical structure information incorporating fraction unbound in plasma as a descriptor.

Prediction of pharmacokinetic profiles of new chemical entities is essential in drug development to minimize the risks of potential withdrawals. The excretion of unchanged compounds by the kidney constitutes a major route in drug elimination and plays an important role in pharmacokinetics. Herein, we created in silico prediction models of the fraction of drug excreted unchanged in the urine (fe) and renal clearance (CLr), with datasets of 411 and 401 compounds using freely available software; notably, all models require chemical structure information alone. The binary classification model for fe demonstrated a balanced accuracy of 0.74. The two-step prediction system for CLr was generated using a combination of the classification model to predict excretion-type compounds and regression models to predict the CLr value for each excretion type. The accuracies of the regression models increased upon adding a descriptor, which was the observed and predicted fraction unbound in plasma (fu,p); 78.6% of the samples in the higher range of renal clearance fell within 2-fold error with predicted fu,p value. Our prediction system for renal excretion is freely available to the public and can be used as a practical tool for prioritization and optimization of compound synthesis in the early stage of drug discovery.

Impact of quality trimming on the efficiency of reads joining and diversity analysis of Illumina paired-end reads in the context of QIIME1 and QIIME2 microbiome analysis frameworks.

BACKGROUND: To increase the accuracy of microbiome data analysis, solving the technical limitations of the existing sequencing machines is required. Quality trimming is suggested to reduce the effect of the progressive decrease in sequencing quality with the increased length of the sequenced library. In this study, we examined the effect of the trimming thresholds (0-20 for QIIME1 and 0-30 for QIIME2) on the number of reads that remained after the quality control and chimera removal (the good reads). We also examined the distance of the analysis results to the gold standard using simulated samples. RESULTS: Quality trimming increased the number of good reads and abundance measurement accuracy in Illumina paired-end reads of the V3-V4 hypervariable region. CONCLUSIONS: Our results suggest that the pre-analysis trimming step should be included before the application of QIIME1 or QIIME2.

SiN/Si double-layer platform for ultralow-crosstalk multiport optical switches.

We experimentally demonstrate a double-layer platform of silicon nitride and silicon for ultralow-crosstalk multiport optical switches. By using a silicon nitride overpass with a large gap of 1.5 µm, we achieve a crosstalk of less than -50 dB and -45 dB almost entirely in the C-band for 4 × 4 and 16 × 16 switches, respectively. To demonstrate the scalability of the platform, we also measured a 32 × 32 passive test device and show that a worst-case crosstalk of less than -50 dB is feasible with appropriate gate switches.