Complex insoluble dietary fiber alleviates obesity and liver steatosis, and modulates the gut microbiota in C57BL/6J mice fed a high-fat diet.

BACKGROUND: Obesity has been demonstrated as a risk factor that seriously affects health. Insoluble dietary fiber (IDF), as a major component of dietary fiber, has positive effects on obesity, inflammation and diabetes. RESULTS: In this study, complex IDF was prepared using 50% enoki mushroom IDF, 40% carrot IDF, and 10% oat IDF. The effects and potential mechanism of complex IDF on obesity were investigated in C57BL/6 mice fed a high-fat diet. The results showed that feeding diets containing 5% complex IDF for 8 weeks significantly reduced mouse body weight, epididymal lipid index, and ectopic fat deposition, and improved mouse liver lipotoxicity (reduced serum levels of alanine aminotransferase, aspartate aminotransferase, and alkaline phosphatase), fatty liver, and short-chain fatty acid composition. High-throughput sequencing of 16S rRNA and analysis of fecal metabolomics showed that the intervention with complex IDF reversed the high-fat-diet-induced dysbiosis of gut microbiota, which is associated with obesity and intestinal inflammation, and affected metabolic pathways, such as primary bile acid biosynthesis, related to fat digestion and absorption. CONCLUSION: Composite IDF intervention can effectively inhibit high-fat-diet-induced obesity and related symptoms and affect the gut microbiota and related metabolic pathways in obesity. Complex IDF has potential value in the prevention of obesity and metabolic syndrome. © 2024 Society of Chemical Industry.

Investigation of the frequency of kdr mutation of voltage-gated sodium channel in Anopheles sinensis in Yunnan Province / 中国热带医学

@#Abstract: Objective　To understand the kdr (knockdown resistance, kdr) gene mutation of the voltage-gated sodium channel (VGSC) of Anopheles sinensis in Yunnan Province. Methods　From 2018 to 2019, mosquitoes were collected in Luoping County, Suijiang County, Tengchong City, Yingjiang County, Yuanjiang County and Mengla County in Yunnan Province. The collected mosquitoes were morphologically identified as Anopheles sinensis and genomic DNA was extracted by kits. The DNA templates were sequenced after PCR amplification and the sequencing results were identify as Anopheles sinensis by homology alignment in NCBI. After the ⅡS5 and ⅡS6 fragments of the sodium channels in Anopheles sinensis were amplified and sequenced, the sequencing results were multiple aligned by DNAMAN software, and the mutations were analyzed one by one with BioEdit software to determine the kdr allele types and genotypes, and the frequencies were calculated. Results This survey amplified 287 sequences, and the sequence maps showed that 1014 loci had three alleles, including wild type TTG/L (89.20%), mutant type TTT/F (9.76%) and TCG/S (1.04%). Five genotypes: homozygous wildtype L/L (85.02%), homozygous mutant F/F (6.27%) and S/S (0.35%), heterozygous mutant L/F (6.97%) and L/S (1.39%). The wild type allele TTG/L was the main allele in six sampling sites except Suijiang County. The frequency of wild type allele in Tengchong City was the highest (100.00%). That is, no mutation was detected, while the rest of counties occurred different degrees of mutation at 1014 loci. The frequency of mutant allele in Suijiang County was the highest, reaching 55.68%. Luoping County, Mengla County and Suijiang County had two mutant types. Yingjiang County and Yuanjiang County had one heterozygous mutant L/F. Conclusion　Wild type L1014 (TTG/L) is still dominant in most areas of Yunnan Province. The kdr mutation type is mainly L1014F, followed by L1014S, and the mutation frequency is lower than that in central provinces of China.

Prediction of drug efficacy from transcriptional profiles with deep learning.

Drug discovery focused on target proteins has been a successful strategy, but many diseases and biological processes lack obvious targets to enable such approaches. Here, to overcome this challenge, we describe a deep learning-based efficacy prediction system (DLEPS) that identifies drug candidates using a change in the gene expression profile in the diseased state as input. DLEPS was trained using chemically induced changes in transcriptional profiles from the L1000 project. We found that the changes in transcriptional profiles for previously unexamined molecules were predicted with a Pearson correlation coefficient of 0.74. We examined three disorders and experimentally tested the top drug candidates in mouse disease models. Validation showed that perillen, chikusetsusaponin IV and trametinib confer disease-relevant impacts against obesity, hyperuricemia and nonalcoholic steatohepatitis, respectively. DLEPS can generate insights into pathogenic mechanisms, and we demonstrate that the MEK-ERK signaling pathway is a target for developing agents against nonalcoholic steatohepatitis. Our findings suggest that DLEPS is an effective tool for drug repurposing and discovery.