Palmitoleic acid (16:1delta9), an unusual monounsaturated fatty acid, is highly valued for human nutrition, medication and industry. Plant oils containing large amounts of palmitoleic acid are the ideal resource for biodiesel production. To increase accumulation of palmitoleic acid in plant tissues, we used a yeast (Saccharomyees cerevisiae) acyl-CoA-delta9 desaturase (Scdelta9D) for cytosol- and plastid-targeting expression in tobacco (Nicotiana tabacum L.). By doing this, we also studied the effects of the subcellular-targeted expression of this enzyme on lipid synthesis and metabolism in plant system. Compared to the wild type and vector control plants, the contents of monounsaturated palmitoleic (16:1delta9) and cis-vaccenic (18:1delta11) were significantly enhanced in the Scdelta9D-transgenic leaves whereas the levels of saturated palmitic acid (16:0) and polyunsaturated linoleic (18:2) and linolenic (18:3) acids were reduced in the transgenics. Notably, the contents of 16:1delta9 and 18:1delta11 in the Scdelta9D plastidal-expressed leaves were 2.7 and 1.9 folds of that in the cytosolic-expressed tissues. Statistical analysis appeared a negative correlation coefficient between 16:0 and 16:1delta9 levels. Our data indicate that yeast cytosolic acyl-CoA-delta9 desaturase can convert palmitic (16:0) into palmitoleic acid (16:1delta9) in high plant cells. Moreover, this effect of the enzyme is stronger with the plastid-targeted expression than the cytosol-target expression. The present study developed a new strategy for high accumulation of omega-7 fatty acids (16:1delta9 andl8:1delta11) in plant tissues by protein engineering of acyl-CoA-delta9 desaturase. The findings would particularly benefit the metabolic assembly of the lipid biosynthesis pathway in the large-biomass vegetative organs such as tobacco leaves for the production of high-quality biodiesel.
Fatty Acid Desaturases ; genetics ; metabolism ; Fatty Acids, Monounsaturated ; metabolism ; Plants, Genetically Modified ; Recombinant Proteins ; genetics ; metabolism ; Saccharomyces cerevisiae ; enzymology ; Saccharomyces cerevisiae Proteins ; genetics ; metabolism ; Tobacco ; genetics ; metabolism

Objective:To construct a predictive nomogram incorporating pretreatment CT-based radiomics for radiation pneumonitis (RP) in esophageal cancer (EC) patients treated with intensity-modulated radiotherapy (IMRT), and to evaluate the value of CT radiomics in predicting RP.Methods:Clinical data of 267 EC patients sequentially treated with IMRT in Quanzhou First Hospital affiliated to Fujian Medical University from January 2019 to December 2021 were prospectively analyzed. Among them, the first 206 patients were assigned into the training cohort and the last 61 patients were enrolled in the validation cohort. Radiomics features of bilateral lungs were extracted by radiotherapy CT simulation. Univariate analysis was performed to screen the potential predictive variables for symptomatic RP. Machine learning algorithms, such as least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGboost), and support vector machine (SVM), were performed for radiomic features selection, respectively. The best classifier was chosen to construct a radiomic signature (RS). Clinical, radiomics and combined nomogram predictive model were developed, respectively. The predictive efficiency and clinical benefits of three models were compared by calculating the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve and decision curve analysis (DCA), and then validated in the validation cohort. Multivariate logistic regression analysis was conducted. Different ROC curves were compared by Delong test.Results:Cardiovascular disease, minimum internal diameter of esophagus and adjuvant chemotherapy and RS were the independent related factors of RP. The AUC of clinical, radiomics and combined models were 0.772, 0.745, 0.842 in the training cohort, and 0.851, 0.811, 0.901 in the validation cohort, respectively. DCA showed that combined radiomic model yielded better clinical benefits compared with clinical model.Conclusion:Radiomics features from pretreatment CT have the potential of improving the efficiency of RP prediction models for EC patients treated with IMRT.