Optimization and mechanism exploration for Escherichia coli transformed with plasmid pUC19 by the combination with ultrasound treatment and chemical method.

As a basic technique of molecular cloning, bio-transformation has been successfully used in the fields of biomedicine and food processing. In this study, we established a transformation system of exogenous DNA into E. coli cells mediated by ultrasound. Under the optimal conditions (i.e. 35 °C, 40 W, 25 s, OD600 = 0.4-0.6) optimized by RSM, the transformation efficiency reached at 1.006 × 107 CFU/µg DNA. The results of membrane permeability, macromolecular substance and cell structure analysis before and after ultrasound treatment showed that the damage of host cells induced by lower (40 W) ultrasound and shorter ultrasound time (25 s) was reversible, and the transformation efficiency and cell survival rate were not significantly affected under this condition. In brief, proper changes in cell membrane and cell wall were the basic conditions for host cells to uptake exogenous DNA, while, whether exogenous DNA could be replicated and expressed in cells depends on the viability of host cells.

Gene signature for prognosis in comparison of pancreatic cancer patients with diabetes and non-diabetes.

BACKGROUND: Pancreatic cancer (PC) has much weaker prognosis, which can be divided into diabetes and non-diabetes. PC patients with diabetes mellitus will have more opportunities for physical examination due to diabetes, while pancreatic cancer patients without diabetes tend to have higher risk. Identification of prognostic markers for diabetic and non-diabetic pancreatic cancer can improve the prognosis of patients with both types of pancreatic cancer. METHODS: Both types of PC patients perform differently at the clinical and molecular levels. The Cancer Genome Atlas (TCGA) is employed in this study. The gene expression of the PC with diabetes and non-diabetes is used for predicting their prognosis by LASSO (Least Absolute Shrinkage and Selection Operator) Cox regression. Furthermore, the results are validated by exchanging gene biomarker with each other and verified by the independent Gene Expression Omnibus (GEO) and the International Cancer Genome Consortium (ICGC). The prognostic index (PI) is generated by a combination of genetic biomarkers that are used to rank the patient's risk ratio. Survival analysis is applied to test significant difference between high-risk group and low-risk group. RESULTS: An integrated gene prognostic biomarker consisted by 14 low-risk genes and six high-risk genes in PC with non-diabetes. Meanwhile, and another integrated gene prognostic biomarker consisted by five low-risk genes and three high-risk genes in PC with diabetes. Therefore, the prognostic value of gene biomarker in PC with non-diabetes and diabetes are all greater than clinical traits (HR = 1.102, P-value < 0.0001; HR = 1.212, P-value < 0.0001). Gene signature in PC with non-diabetes was validated in two independent datasets. CONCLUSIONS: The conclusion of this study indicated that the prognostic value of genetic biomarkers in PCs with non-diabetes and diabetes. The gene signature was validated in two independent databases. Therefore, this study is expected to provide a novel gene biomarker for predicting prognosis of PC with non-diabetes and diabetes and improving clinical decision.