Purification and characterization of alcohol dehydrogenase from Gluconobacter suboxydans

Purification and characterization of alcohol dehydrogenase (ADH) from Gluconobacter suboxydans was done in order to biotechnological and industrial application. Solubilization of enzyme from bacterial membrane fraction by Triton X-100 and subsequent fractionation on DEAE-Sephadex A-50 and Hydroxyapatite was successful in enzyme purification. Enzyme assay reaction mixture contained potassium ferricyanide 0.1 M, McIlvaine buffer 0.1 M (pH 5.5), Triton X-100 10%, ethanol 1 M and enzyme solution. The purified ADH Optimum pH activity was 5.5. The enzyme was in maximum stability in pH 5.8. The substrate specificity of the enzyme was determined using the same enzyme assay method as described above, except that various substrates (100 mM) were used instead of ethanol. The relative activity of the ADH for ethanol was higher than the others. The effects of metal ions and inhibitors on the activity of the enzyme were examined by measuring the activity using the same assay method as described above. Activity of purified enzyme was increased in the presence of Ca(+2) and was decreased in presence the of ethylenediamine tetra acetic acid (EDTA). Because the proper structure and function of the enzyme is related to structural Ca(+2) and EDTA can chelate Ca(+2). An apparent Michaelis constant for ethanol were examined to be 1.7 x 10(-3) M for ethanol as substrate.     

Predicting Cationic Exchange Capacity in Calcareous Soils of East-Azerbaijan Province,Northwest Iran

The aim of this research is to study the efficiency of pedotransfer functions (PTFs) and artificial neural networks (ANNs) for cationic exchange capacity (CEC) prediction using readily available soil properties. Here, 417 soil samples were collected from the calcareous soils located in East-Azerbaijan province, northwest Iran and readily available soil properties, such as particle size distribution (PSD), organic matter (OM) and calcium carbonate equivalent (CCE), were measured. The entire 417 soil samples were divided into two groups, a training data set (83 soil samples) and test data set (334 soil samples). The performances of several published and derived PTFs and developed neural network algorithms using multilayer perceptron were compared, using a test data set. Results showed that, based on statistics of RMSE and R², PTFs and ANNs had a similar performance, and there was no significant difference in the accuracy of the model results. The result of the sensitivity analysis showed that the ANN models were very sensitive to the clay variable (due to the high variability of the clay). Finally, the models tested in this study could account for 85% of the variations in cationic exchange capacity (CEC) of soils in the studied area.

Abbreviations: ANN: arti?cial neural networks; MLP: multilayer perceptron; MLR: multiple linear regression; PTFs: Pedotransfer Functions; RBF: Radial Basis Function; MAE: mean absolute error; MSE: mean square error; CEC: cationic exchange capacity     

Population genetic structure and ecological niche modelling of the leafhopper Hishimonus phycitis

Witches’ broom disease of lime, caused by ‘Candidatus Phytoplasma aurantifolia’, is responsible for major losses of Mexican lime trees in Southern Iran, Oman and the United Arab Emirates. The causative phytoplasma is transmitted by the leafhopper, Hishimonus phycitis. We combined ecological niche modelling with environmental and genetic data for six populations of H. phycitis from Iran and one from Oman. The mitochondrial cytochrome c oxidase I (COI) gene and nine microsatellite DNA markers were used for the genetic analyses. Although the Oman population had specific haplotypes, the COI sequences were highly conserved among all populations studied. In contrast, the microsatellite data divided the populations from Iran and Oman into two separate clades. An analysis of molecular variance indicated a high level of variation within populations. The Mantel test showed no correlation between genetic and geographical distances. Gene flow values were small between the populations from Iran and north of Oman but significantly higher among the Iranian populations supporting the differentiation between Iran and Oman. In addition, we found that patterns of genetic divergence within Iranian populations were associated strongly with divergence in terms of their ecological niches. Data on six climatic variables, including elevation, were used to create ecological niche models. Our results suggest that the genetic differentiation of H. phycitis may be attributable to climatic conditions and/or geographical barriers.