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.

Predicting the fraction unbound in plasma provides a good understanding of the pharmacokinetic properties of a drug to assist candidate selection in the early stages of drug discovery. It is also an effective tool to mitigate the risk of late-stage attrition and to optimize further screening. In this study, we built in silico prediction models of fraction unbound in human plasma with freely available software, aiming specifically to improve the accuracy in the low value ranges. We employed several machine learning techniques and built prediction models trained on the largest ever data set of 2738 experimental values. The classification model showed a high true positive rate of 0.826 for the low fraction unbound class on the test set. The strongly biased distribution of the fraction unbound in plasma was mitigated by a logarithmic transformation in the regression model, leading to improved accuracy at lower values. Overall, our models showed better performance than those of previously published methods, including commercial software. Our prediction tool can be used on its own or integrated into other pharmacokinetic modeling systems.

Metagenomic analysis based on the 16S rRNA gene is generally performed to examine the diversity and abundance of commensal bacteria in feces, which is now recognized to be associated with human health and diseases. Guanidine thiocyanate (GuSCN) solution is used as a less onerous way compared with a frozen method to transport and stock fecal samples at room temperature for DNA analysis; however, optimal methods to measure fecal bacterial composition in GuSCN solution remain to be investigated. Here, we examined the influence of various factors such as pretreatment (e.g., removing GuSCN solution and washing feces with phosphate-buffered saline (PBS) before mechanical lysis), fecal concentration in the GuSCN solution, storage time, and position of fecal subsampling on the 16S rRNA-based analysis of fecal bacteria in GuSCN solution. We found that pretreatment and fecal concentration affected the bacterial composition, and a little change was noted with subsampling position. Based on these results, we propose a basic protocol, including fecal sampling, sample storage, and DNA extraction, for the 16S rRNA-based analysis of bacterial composition in feces suspended in GuSCN solution.

Recent advancements in cell-based therapies for the treatment of cardiovascular disease (CVD) show continuing promise for the use of transplanted stem and cardiac progenitor cells (CPCs) to promote cardiac restitution. However, a detailed understanding of the molecular mechanisms that control the development of these cells remains incomplete and is critical for optimizing their use in such therapy. Long non-coding (lnc) RNA has recently emerged as a crucial class of regulatory molecules involved in directing a variety of critical biological processes including development, homeostasis and disease. As such, a rising body of evidence suggests that they also play key regulatory roles in CPC development, though many questions remain regarding the expression landscape and specific identity of lncRNA involved in this process. To address this, we performed whole transcriptome sequencing of two murine CPC populations-Nkx2-5 EmGFP reporter-sorted embryonic stem (ES) cell-derived and ex vivo, cardiosphere-derived-in an effort to characterize their lncRNA profiles and potentially identify novel CPC regulators. The resulting sequencing data revealed an enrichment in both CPC populations for a panel of previously-identified lncRNA genes associated with cardiac differentiation. Additionally, a total of 1,678 differentially expressed and as-of-yet unannotated, putative lncRNA genes were found to be enriched for in the two CPC populations relative to undifferentiated ES cells.

Caspase recruitment domain family member 14 (CARD14) was recently identified as a psoriasis-susceptibility gene, but its immunological role in the pathogenesis of psoriasis in vivo remains unclear. In this study, we examined the role of CARD14 in murine experimental models of psoriasis induced by either imiquimod (IMQ) cream or recombinant IL-23 injection. In all models tested, the psoriasiform skin inflammation was abrogated in mice. Comparison of the early gene signature of the skin between IMQ-cream-treated mice and mice revealed not only their similarity, but also distinct gene sets targeted by IL-23. Cell type-specific analysis of these mice identified skin Langerin Langerhans cells as a potent producer of IL-23, which was dependent on both TLR7 and TLR9 but independent of CARD14, suggesting that CARD14 is acting downstream of IL-23, not TLR7 or TLR9. Instead, a bone marrow chimera study suggested that CARD14 in radio-sensitive hematopoietic cells was required for IMQ-induced psoriasiform skin inflammation, controlling the number of Vγ4 T cells producing IL-17 or IL-22 infiltrating through the dermis to the inflamed epidermis. These data indicate that CARD14 is essential and a potential therapeutic target for psoriasis.

A key consideration at the screening stages of drug discovery is in vitro metabolic stability, often measured in human liver microsomes. Computational prediction models can be built using a large quantity of experimental data available from public databases, but these databases typically contain data measured using various protocols in different laboratories, raising the issue of data quality. In this study, we retrieved the intrinsic clearance ( CL int ) measurements from an open database and performed extensive manual curation. Then, chemical descriptors were calculated using freely available software, and prediction models were built using machine learning algorithms. The models trained on the curated data showed better performance than those trained on the non‐curated data and achieved performance comparable to previously published models, showing the importance of manual curation in data preparation. The curated data were made available, to make our models fully reproducible.

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 (K p,brain ) and unbound brain-to-plasma concentration ratio (K p,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.

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.