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Jiang Tang Xiao Ke (JTXK) granule, a Chinese herbal formula, has been used clinically to treat type 2 diabetes (T2DM) for decades. Our previous studies showed that JTXK granule exhibited anti-diabetic and anti-oxidative functions in experimental diabetic rats induced by a high fat diet and streptozotocin. However, the underlying mechanisms remain poorly understood. Herein, we aimed to investigate the therapeutic effect of JTXK granule on T2DM KKAy mice and the possible associations with skeletal muscle in the current study. Our results showed that JTXK granule significantly reduced food intake and body weight in T2DM KKAy mice. JTXK granule treatment also decreased the blood glucose and HbA1c levels and increased the insulin sensitivity in a time-dependent manner. Additionally, it ameliorated hyperlipidaemia and induced a lower free fatty acid level, displaying an effect on disorders of lipid metabolism. JTXK granule significantly increased the expression of insulin receptor substrate-1 (IRS-1), phosphoinositide 3-kinase (PI3K), protein kinase B (PKB/Akt) and glucose transporter 4 (Glut4) and decreased the expression of glycogen synthase kinase 3β (GSK3β). We concluded that JTXK granule is an effective drug for T2DM through regulating the PI3K/Akt signalling pathway in skeletal muscle.

Many metabolic studies employ tissue-specific gene knockout mice, which requires breeding of floxed gene mice, available mostly on C57BL/6N (NN) genetic background, with cre or Flp recombinase-expressing mice, available on C57BL/6J (JJ) background, resulting in the generation of mixed C57BL/6NJ (NJ) genetic background mice. Recent awareness of many genetic differences between NN and JJ strains including the deletion of nicotinamide nucleotide transhydrogenase (nnt), necessitates examination of the consequence of mixed NJ background on glucose tolerance, beta cell function and other metabolic parameters. Male mice with NN and NJ genetic background were fed with normal or high fat diets (HFD) for 12 weeks and glucose and insulin homeostasis were studied. Genotype had no effect on body weight and food intake in mice fed normal or high fat diets. Insulinemia in the fed and fasted states and after a glucose challenge was lower in HFD-fed NJ mice, even though their glycemia and insulin sensitivity were similar to NN mice. NJ mice showed mild glucose intolerance. Moreover, glucose- but not KCl-stimulated insulin secretion in isolated islets was decreased in HFD-fed NJ vs NN mice without changes in insulin content and beta cell mass. Under normal diet, besides reduced fed insulinemia, NN and NJ mice presented similar metabolic parameters. However, HFD-fed NJ mice displayed lower fed and fasted insulinemia and glucose-induced insulin secretion in vivo and ex vivo, as compared to NN mice. These results strongly caution against using unmatched mixed genetic background C57BL/6 mice for comparisons, particularly under HFD conditions.

Breast cancer has various molecular subtypes and displays high heterogeneity. Aberrant DNA methylation is involved in tumor origin, development and progression. Moreover, distinct DNA methylation patterns are associated with specific breast cancer subtypes. We explored DNA methylation patterns in association with gene expression to assess their impact on the prognosis of breast cancer based on Infinium 450K arrays (training set) from The Cancer Genome Atlas (TCGA). The DNA methylation patterns of 12 featured genes that had a high correlation with gene expression were identified through univariate and multivariable Cox proportional hazards models and used to define the methylation risk score (MRS). An improved ability to distinguish the power of the DNA methylation pattern from the 12 featured genes (p = 0.00103) was observed compared with the average methylation levels (p = 0.956) or gene expression (p = 0.909). Furthermore, MRS provided a good prognostic value for breast cancers even when the patients had the same receptor status. We found that ER-, PR- or Her2- samples with high-MRS had the worst 5-year survival rate and overall survival time. An independent test set including 28 patients with death as an outcome was used to test the validity of the MRS of the 12 featured genes; this analysis obtained a prognostic value equivalent to the training set. The predict power was validated through two independent datasets from the GEO database. The DNA methylation pattern is a powerful predictor of breast cancer survival, and can predict outcomes of the same breast cancer molecular subtypes.

Abnormal DNA methylation is known as playing an important role in the tumorgenesis. It is helpful for distinguishing the specificity of diagnosis and therapeutic targets for cancers based on characteristics of DNA methylation patterns across cancers. High throughput DNA methylation analysis provides the possibility to comprehensively filter the epigenetics diversity across various cancers. We integrated whole-genome methylation data detected in 798 samples from seven cancers. The hierarchical clustering revealed the existence of cancer-specific methylation pattern. Then we identified 331 differentially methylated genes across these cancers, most of which (266) were specifically differential methylation in unique cancer. A DNA methylation correlation network (DMCN) was built based on the methylation correlation between these genes. It was shown the hubs in the DMCN were inclined to cancer-specific genes in seven cancers. Further survival analysis using the part of genes in the DMCN revealed high-risk group and low-risk group were distinguished by seven biomarkers (PCDHB15, WBSCR17, IGF1, GYPC, CYGB, ACTG2, and PRRT1) in breast cancer and eight biomarkers (ZBTB32, OR51B4, CCL8, TMEFF2, SALL3, GPSM1, MAGEA8, and SALL1) in colon cancer, respectively. At last, a protein-protein interaction network was introduced to verify the biological function of differentially methylated genes. It was shown that MAP3K14, PTN, ACVR1 and HCK sharing different DNA methylation and gene expression across cancers were relatively high degree distribution in PPI network. The study suggested that not only the identified cancer-specific genes provided reference for individual treatment but also the relationship across cancers could be explained by differential DNA methylation.

There are at least 250 enzymes in Mycobacterium tuberculosis (M. tuberculosis) involved in lipid metabolism. Some of the enzymes are required for bacterial survival and full virulence. The esterase Rv0045c shares little amino acid sequence similarity with other members of the esterase/lipase family. Here, we report the 3D structure of Rv0045c. Our studies demonstrated that Rv0045c is a novel member of α/β hydrolase fold family. The structure of esterase Rv0045c contains two distinct domains: the α/β fold domain and the cap domain. The active site of esterase Rv0045c is highly conserved and comprised of two residues: Ser154 and His309. We proposed that Rv0045c probably employs two kinds of enzymatic mechanisms when hydrolyzing C-O ester bonds within substrates. The structure provides insight into the hydrolysis mechanism of the C-O ester bond, and will be helpful in understanding the ester/lipid metabolism in M. tuberculosis.