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.

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.

Autism spectrum disorder (ASD) is a complex group of clinically heterogeneous neurodevelopmental disorders with unclear etiology and pathogenesis. Genetic studies have identified numerous candidate genetic variants, including de novo mutated ASD-associated genes; however, the function of these de novo mutated genes remains unclear despite extensive bioinformatics resources. Accordingly, it is not easy to assign priorities to numerous candidate ASD-associated genes for further biological analysis. Here we developed a convenient system for identifying an experimental evidence-based annotation of candidate ASD-associated genes. We performed trio-based whole-exome sequencing in 30 sporadic cases of ASD and identified 37 genes with de novo single-nucleotide variations (SNVs). Among them, 5 of those 37 genes, POGZ, PLEKHA4, PCNX, PRKD2 and HERC1, have been previously reported as genes with de novo SNVs in ASD; and consultation with in silico databases showed that only HERC1 might be involved in neural function. To examine whether the identified gene products are involved in neural functions, we performed small hairpin RNA-based assays using neuroblastoma cell lines to assess neurite development. Knockdown of 8 out of the 14 examined genes significantly decreased neurite development (P<0.05, one-way analysis of variance), which was significantly higher than the number expected from gene ontology databases (P=0.010, Fisher's exact test). Our screening system may be valuable for identifying the neural functions of candidate ASD-associated genes for further analysis and a substantial portion of these genes with de novo SNVs might have roles in neuronal systems, although further detailed analysis might eliminate false positive genes from identified candidate ASD genes.

To determine the region of human tumor necrosis factor-alpha (TNF-α), essential for cytotoxic activity against mouse L-M cells, single amino-acid-substituted TNF-α mutant proteins (muteins) were produced in Escherichia coli by protein engineering techniques. An expression plasmid for TNF-α was mutagenized by passage through an E.coli mutD5 mutator strain and by oligonucleotide-directed mutagenesis. Approximately 100 single amino-acid-substituted TNF-α muteins were produced and assayed for cytotoxic activity. The cytotoxic activities of purified TNF-α muteins, e.g. TNF-31T, -32Y, -82D, -85H, -115L, -141Y, -144K and -146E, were < 1% of that of parent TNF-α. These results indicate that the integrity of at least four distinct regions of the TNF-α molecule is required for full biological activity. These regions are designated as follows: region I, from position 30 to 32; region II, from position 82 to 89; region III, from position 115 to 117; region FV, from position 141 to 146. In addition, TNF-141Y could not completely compete with parent TNF-α for binding to the receptor. This demonstrates that region IV, and at least aspartk acid at position 141, must be involved in the TNF receptor binding site.