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.