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.

Distinct Age-Specific miRegulome Profiling of Isolated Small and Large Intestinal Epithelial Cells in Mice.

The intestinal epithelium serves as a dynamic barrier to protect the host tissue from exposure to a myriad of inflammatory stimuli in the luminal environment. Intestinal epithelial cells (IECs) encompass differentiated and specialized cell types that are equipped with regulatory genes, which allow for sensing of the luminal environment. Potential inflammatory cues can instruct IECs to undergo a diverse set of phenotypic alterations. Aging is a primary risk factor for a variety of diseases; it is now well-documented that aging itself reduces the barrier function and turnover of the intestinal epithelium, resulting in pathogen translocation and immune priming with increased systemic inflammation. In this study, we aimed to provide an effective epigenetic and regulatory outlook that examines age-associated alterations in the intestines through the profiling of microRNAs (miRNAs) on isolated mouse IECs. Our microarray analysis revealed that with aging, there is dysregulation of distinct clusters of miRNAs that was present to a greater degree in small IECs (22 miRNAs) compared to large IECs (three miRNAs). Further, miRNA-mRNA interaction network and pathway analyses indicated that aging differentially regulates key pathways between small IECs (e.g., toll-like receptor-related cascades) and large IECs (e.g., cell cycle, Notch signaling and small ubiquitin-related modifier pathway). Taken together, current findings suggest novel gene regulation pathways by epithelial miRNAs in aging within the gastrointestinal tissues.

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.

Effects of levocetirizine and diphenhydramine on regional glucose metabolic changes and hemodynamic responses in the human prefrontal cortex during cognitive tasks.

OBJECTIVE: Antihistamines often have sedative side effects. This was the first study to measure regional cerebral glucose (energy) consumption and hemodynamic responses in young adults during cognitive tests after antihistamine administration. METHODS: In this double-blind, placebo-controlled, three-way crossover study, 18 healthy young Japanese men received single doses of levocetirizine 5 mg and diphenhydramine 50 mg at intervals of at least six days. Subjective feeling, task performances, and brain activity were evaluated during three cognitive tests (word fluency, two-back, and Stroop). Regional cerebral glucose consumption changes were measured using positron emission tomography with [18 F]fluorodeoxyglucose. Regional hemodynamic responses were measured using near-infrared spectroscopy. RESULTS: Energy consumption in prefrontal regions was significantly increased after antihistamine administration, especially diphenhydramine, whereas prefrontal hemodynamic responses, evaluated with oxygenated hemoglobin levels, were significantly lower with diphenhydramine treatment. Stroop test accuracy was significantly impaired by diphenhydramine, but not by levocetirizine. There was no significant difference in subjective sleepiness. CONCLUSIONS: Physiological "coupling" between metabolism and perfusion in the healthy human brain may not be maintained under pharmacological influence due to antihistamines. This uncoupling may be caused by a combination of increased energy demands in the prefrontal regions and suppression of vascular permeability in brain capillaries after antihistamine treatment. Further research is needed to validate this hypothesis.

Histamine H3 receptor in primary mouse microglia inhibits chemotaxis, phagocytosis, and cytokine secretion.

Histamine is a physiological amine which initiates a multitude of physiological responses by binding to four known G-protein coupled histamine receptor subtypes as follows: histamine H1 receptor (H1 R), H2 R, H3 R, and H4 R. Brain histamine elicits neuronal excitation and regulates a variety of physiological processes such as learning and memory, sleep-awake cycle and appetite regulation. Microglia, the resident macrophages in the brain, express histamine receptors; however, the effects of histamine on critical microglial functions such as chemotaxis, phagocytosis, and cytokine secretion have not been examined in primary cells. We demonstrated that mouse primary microglia express H2 R, H3 R, histidine decarboxylase, a histamine synthase, and histamine N-methyltransferase, a histamine metabolizing enzyme. Both forskolin-induced cAMP accumulation and ATP-induced intracellular Ca(2+) transients were reduced by the H3 R agonist imetit but not the H2 R agonist amthamine. H3 R activation on two ubiquitous second messenger signalling pathways suggests that H3 R can regulate various microglial functions. In fact, histamine and imetit dose-dependently inhibited microglial chemotaxis, phagocytosis, and lipopolysaccharide (LPS)-induced cytokine production. Furthermore, we confirmed that microglia produced histamine in the presence of LPS, suggesting that H3 R activation regulate microglial function by autocrine and/or paracrine signalling. In conclusion, we demonstrate the involvement of histamine in primary microglial functions, providing the novel insight into physiological roles of brain histamine.

Predominant role of plasma membrane monoamine transporters in monoamine transport in 1321N1, a human astrocytoma-derived cell line.

Monoamine neurotransmitters should be immediately removed from the synaptic cleft to avoid excessive neuronal activity. Recent studies have shown that astrocytes and neurons are involved in monoamine removal. However, the mechanism of monoamine transport by astrocytes is not entirely clear. We aimed to elucidate the transporters responsible for monoamine transport in 1321N1, a human astrocytoma-derived cell line. First, we confirmed that 1321N1 cells transported dopamine, serotonin, norepinephrine, and histamine in a time- and dose-dependent manner. Kinetics analysis suggested the involvement of low-affinity monoamine transporters, such as organic cation transporter (OCT) 2 and 3 and plasma membrane monoamine transporter (PMAT). Monoamine transport in 1321N1 cells was not Na(+) /Cl(-) dependent but was inhibited by decynium-22, an inhibitor of low-affinity monoamine transporters, which supported the importance of low-affinity transporters. RT-PCR assays revealed that 1321N1 cells expressed OCT3 and PMAT but no other neurotransmitter transporters. Another human astrocytoma-derived cell line, U251MG, and primary human astrocytes also exhibited the same gene expression pattern. Gene-knockdown assays revealed that 1321N1 and primary human astrocytes could transport monoamines predominantly through PMAT and partly through OCT3. These results might indicate that PMAT and OCT3 in human astrocytes are involved in monoamine clearance.

Insufficient intake of L-histidine reduces brain histamine and causes anxiety-like behaviors in male mice.

L-histidine is one of the essential amino acids for humans, and it plays a critical role as a component of proteins. L-histidine is also important as a precursor of histamine. Brain histamine is synthesized from L-histidine in the presence of histidine decarboxylase, which is expressed in histamine neurons. In the present study, we aimed to elucidate the importance of dietary L-histidine as a precursor of brain histamine and the histaminergic nervous system. C57BL/6J male mice at 8 wk of age were assigned to 2 different diets for at least 2 wk: the control (Con) diet (5.08 g L-histidine/kg diet) or the low L-histidine diet (LHD) (1.28 g L-histidine/kg diet). We measured the histamine concentration in the brain areas of Con diet-fed mice (Con group) and LHD-fed mice (LHD group). The histamine concentration was significantly lower in the LHD group [Con group vs. LHD group: histamine in cortex (means ± SEs): 13.9 ± 1.25 vs. 9.36 ± 0.549 ng/g tissue; P = 0.002]. Our in vivo microdialysis assays revealed that histamine release stimulated by high K(+) from the hypothalamus in the LHD group was 60% of that in the Con group (P = 0.012). However, the concentrations of other monoamines and their metabolites were not changed by the LHD. The open-field tests showed that the LHD group spent a shorter amount of time in the central zone (87.6 ± 14.1 vs. 50.0 ± 6.03 s/10 min; P = 0.019), and the light/dark box tests demonstrated that the LHD group spent a shorter amount of time in the light box (198 ± 8.19 vs. 162 ± 14.1 s/10 min; P = 0.048), suggesting that the LHD induced anxiety-like behaviors. However, locomotor activity, memory functions, and social interaction did not differ between the 2 groups. The results of the present study demonstrated that insufficient intake of histidine reduced the brain histamine content, leading to anxiety-like behaviors in the mice.

Machine Learning Approaches for Stroke Risk Prediction: Findings from the Suita Study.

Stroke constitutes a significant public health concern due to its impact on mortality and morbidity. This study investigates the utility of machine learning algorithms in predicting stroke and identifying key risk factors using data from the Suita study, comprising 7389 participants and 53 variables. Initially, unsupervised k-prototype clustering categorized participants into risk clusters, while five supervised models including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosted Machine (LightGBM) were employed to predict stroke outcomes. Stroke incidence disparities among identified risk clusters using the unsupervised k-prototype clustering method are substantial, according to the findings. Supervised learning, particularly RF, was a preferable option because of the higher levels of performance metrics. The Shapley Additive Explanations (SHAP) method identified age, systolic blood pressure, hypertension, estimated glomerular filtration rate, metabolic syndrome, and blood glucose level as key predictors of stroke, aligning with findings from the unsupervised clustering approach in high-risk groups. Additionally, previously unidentified risk factors such as elbow joint thickness, fructosamine, hemoglobin, and calcium level demonstrate potential for stroke prediction. In conclusion, machine learning facilitated accurate stroke risk predictions and highlighted potential biomarkers, offering a data-driven framework for risk assessment and biomarker discovery.

Molecular mechanism of histamine clearance by primary human astrocytes.

Histamine clearance is an essential process for avoiding excessive histaminergic neuronal activity. Previous studies using rodents revealed the predominant role of astrocytes in brain histamine clearance. However, the molecular mechanism of histamine clearance has remained unclear. We detected histamine N-methyltransferase (HNMT), a histamine-metabolizing enzyme, in primary human astrocytes and the astrocytes of human brain specimens. Immunocytochemical analysis and subcellular fractionation assays revealed that active HNMT localized to the cytosol, suggesting that histamine transport into the cytosol is crucial for histamine inactivation. We showed that primary human astrocytes transported histamine in a time-dependent manner. Kinetics analysis showed that two low-affinity transporters were involved in histamine transport. Histamine uptake by primary human astrocytes was not dependent on the extracellular Na(+) /Cl(-) concentration. Histamine is reported to be a substrate for three low-affinity and Na(+) /Cl(-) -independent transporters: organic cation transporter 2 (OCT2), OCT3, and plasma membrane monoamine transporter (PMAT). RT-PCR analysis revealed that OCT3 and PMAT were expressed in primary human astrocytes. Immunohistochemistry confirmed OCT3 and PMAT expression in the astrocytes of human brain specimens. Drug inhibition assays and gene knockdown assays revealed the major contribution of PMAT and the minor contribution of OCT3 to histamine transport. The present study demonstrates for the first time that the molecular mechanism of histamine clearance is by primary human astrocytes. These findings might indicate that PMAT, OCT3 and HNMT in human astrocytes play a role in the regulation of extraneuronal histamine concentration and the activities of histaminergic neurons.

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.

Co-expression analysis to identify key modules and hub genes associated with COVID-19 in platelets.

Corona virus disease 2019 (COVID-19) increases the risk of cardiovascular occlusive/thrombotic events and is linked to poor outcomes. The underlying pathophysiological processes are complex, and remain poorly understood. To this end, platelets play important roles in regulating the cardiovascular system, including via contributions to coagulation and inflammation. There is ample evidence that circulating platelets are activated in COVID-19 patients, which is a primary driver of the observed thrombotic outcome. However, the comprehensive molecular basis of platelet activation in COVID-19 disease remains elusive, which warrants more investigation. Hence, we employed gene co-expression network analysis combined with pathways enrichment analysis to further investigate the aforementioned issues. Our study revealed three important gene clusters/modules that were closely related to COVID-19. These cluster of genes successfully identify COVID-19 cases, relative to healthy in a separate validation data set using machine learning, thereby validating our findings. Furthermore, enrichment analysis showed that these three modules were mostly related to platelet metabolism, protein translation, mitochondrial activity, and oxidative phosphorylation, as well as regulation of megakaryocyte differentiation, and apoptosis, suggesting a hyperactivation status of platelets in COVID-19. We identified the three hub genes from each of three key modules according to their intramodular connectivity value ranking, namely: COPE, CDC37, CAPNS1, AURKAIP1, LAMTOR2, GABARAP MT-ND1, MT-ND5, and MTRNR2L12. Collectively, our results offer a new and interesting insight into platelet involvement in COVID-19 disease at the molecular level, which might aid in defining new targets for treatment of COVID-19-induced thrombosis.

Snaq: A Dynamic Snakemake Pipeline for Microbiome Data Analysis With QIIME2.

Optimizing and automating a protocol for 16S microbiome data analysis with QIIME2 is a challenging task. It involves a multi-step process, and multiple parameters and options that need to be tested and determined. In this article, we describe Snaq, a snakemake pipeline that helps automate and optimize 16S data analysis using QIIME2. Snaq offers an informative file naming system and automatically performs the analysis of a data set by downloading and installing the required databases and classifiers, all through a single command-line instruction. It works natively on Linux and Mac and on Windows through the use of containers, and is potentially extendable by adding new rules. This pipeline will substantially reduce the efforts in sending commands and prevent the confusion caused by the accumulation of analysis results due to testing multiple parameters.

Integrative Network Modeling Highlights the Crucial Roles of Rho-GDI Signaling Pathway in the Progression of non-Small Cell Lung Cancer.

Non-small cell lung cancer (NSCLC) is the most prevalent form of lung cancer and a leading cause of cancer-related deaths worldwide. Using an integrative approach, we analyzed a publicly available merged NSCLC transcriptome dataset using machine learning, protein-protein interaction (PPI) networks and bayesian modeling to pinpoint key cellular factors and pathways likely to be involved with the onset and progression of NSCLC. First, we generated multiple prediction models using various machine learning classifiers to classify NSCLC and healthy cohorts. Our models achieved prediction accuracies ranging from 0.83 to 1.0, with XGBoost emerging as the best performer. Next, using functional enrichment analysis (and gene co-expression network analysis with WGCNA) of the machine learning feature-selected genes, we determined that genes involved in Rho GTPase signaling that modulate actin stability and cytoskeleton were likely to be crucial in NSCLC. We further assembled a PPI network for the feature-selected genes that was partitioned using Markov clustering to detect protein complexes functionally relevant to NSCLC. Finally, we modeled the perturbations in RhoGDI signaling using a bayesian network; our simulations suggest that aberrations in ARHGEF19 and/or RAC2 gene activities contributed to impaired MAPK signaling and disrupted actin and cytoskeleton organization and were arguably key contributors to the onset of tumorigenesis in NSCLC. We hypothesize that targeted measures to restore aberrant ARHGEF19 and/or RAC2 functions could conceivably rescue the cancerous phenotype in NSCLC. Our findings offer promising avenues for early predictive biomarker discovery, targeted therapeutic intervention and improved clinical outcomes in NSCLC.

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.

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.

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.

The impact of community containment implementation timing on the spread of COVID-19: A simulation study.

Background: Community containment is one of the common methods used to mitigate infectious disease outbreaks. The effectiveness of such a method depends on how strictly it is applied and the timing of its implementation. An early start and being strict is very effective; however, at the same time, it impacts freedom and economic opportunity. Here we created a simulation model to understand the effect of the starting day of community containment on the final outcome, that is, the number of those infected, hospitalized and those that died, as we followed the dynamics of COVID-19 pandemic. Methods: We used a stochastic recursive simulation method to apply disease outbreak dynamics measures of COVID-19 as an example to simulate disease spread. Parameters are allowed to be randomly assigned between higher and lower values obtained from published COVID-19 literature. Results: We simulated the dynamics of COVID-19 spread, calculated the number of active infections, hospitalizations and deaths as the outcome of our simulation and compared these results with real world data. We also represented the details of the spread in a network graph structure, and shared the code for the simulation model to be used for examining other variables. Conclusions: Early implementation of community containment has a big impact on the final outcome of an outbreak.

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.

BioHackathon 2015: Semantics of data for life sciences and reproducible research.

We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

A prospective compound screening contest identified broader inhibitors for Sirtuin 1.

Potential inhibitors of a target biomolecule, NAD-dependent deacetylase Sirtuin 1, were identified by a contest-based approach, in which participants were asked to propose a prioritized list of 400 compounds from a designated compound library containing 2.5 million compounds using in silico methods and scoring. Our aim was to identify target enzyme inhibitors and to benchmark computer-aided drug discovery methods under the same experimental conditions. Collecting compound lists derived from various methods is advantageous for aggregating compounds with structurally diversified properties compared with the use of a single method. The inhibitory action on Sirtuin 1 of approximately half of the proposed compounds was experimentally accessed. Ultimately, seven structurally diverse compounds were identified.