Bacterial gene transformation used with Escherichia coli as a desired microorganism is one of the important techniques in genetic engineering. In this study, the preparation of E. coli DH5α competent cells treated with SrCl2 and transformation by heat-shock with pUC19 plasmid was optimized by Response Surface Methodology (RSM). Other five E. coli strains including BL21 (DE3), HB-101, JM109, TOP10 and TG1, three different sizes plasmids (pUC19, pET32a, pPIC9k) were used to verify the protocol, respectively. The transformation mechanism was explored by scanning electron microscope combined with energy dispersive spectrometer (SEM-EDS), atomic absorption spectroscopy (AAS) and Fourier-transform infrared spectroscopy (FT-IR). An equation of regression model was obtained, and the ideal parameters were Sr2 + ions of 90 mM, heat-shock time of 90 s and 9 ng of plasmid. Under this conditions, the transformation efficiency could almost reach to 106 CFU/µg DNA. A small change of the cell surface structure has been observed between E. coli DH5α strain and competent cells by abovementioned spectrum technologies, which implied that a strict regulation mechanism involved in the formation of competent cells and transformation of plasmids. An equation of regression model for the competent cells preparation and plasmid transformation could be applied in gene cloning technology
Fungal endophytes from medicinal plants have the potential to promote plant growth and the accumulation of active ingredients via different mechanisms. In this study, an endophytic fungus was isolated from the roots of Codonopsis pilosula, and was identified as Trichoderma strain RHTA01 based on morphological analysis, fatty acid composition, Fourier infrared spectroscopy and molecular analysis. The strain exhibited good plant growth promoting capacity. In C. pilosula plants inoculated with Trichoderma strain RHTA01, the plant defense system, total chlorophyll content and root activity were enhanced, and levels of antioxidant enzymes, non-enzymatic ingredients and single molecules including nitric oxide (NO), jasmonic acid (JA), salicylic acid (SA) and hydrogen peroxide (H2O2) were up-regulated in different tissues (Root, Stem and Leave). Activities of enzymes involved in polysaccharide and Lobetyolin biosynthesis were up-regulated, thus increasing their accumulation in C. pilosula plants. The function of endophytic fungi was further clarified by structural equation modeling (SEM). Overall, our results provide a strong foundation for further investigation of the interaction between endophytic fungi and plants.
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.
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