Identification and characterization of a new protein isoform of human 5-lipoxygenase

Leukotrienes (LTs) are inflammatory mediators that play a pivotal role in many diseases like asthma bronchiale, atherosclerosis and in various types of cancer. The key enzyme for generation of LTs is the 5-lipoxygenase (5-LO). Here, we present a novel putative protein isoform of human 5-LO that lacks exon 4, termed 5-LOΔ4, identified in cells of lymphoid origin, namely the Burkitt lymphoma cell lines Raji and BL41 as well as primary B and T cells. Deletion of exon 4 does not shift the reading frame and therefore the mRNA is not subjected to non-mediated mRNA decay (NMD). By eliminating exon 4, the amino acids Trp144 until Ala184 are omitted in the corresponding protein. Transfection of HEK293T cells with a 5-LOΔ4 expression plasmid led to expression of the corresponding protein which suggests that the 5-LOΔ4 isoform is a stable protein in eukaryotic cells. We were also able to obtain soluble protein after expression in E. coli and purification. The isoform itself lacks canonical enzymatic activity as it misses the non-heme iron but it still retains ATP-binding affinity. Differential scanning fluorimetric analysis shows two transitions, corresponding to the two domains of 5-LO. Whilst the catalytic domain of 5-LO WT is destabilized by calcium, addition of calcium has no influence on the catalytic domain of 5-LOΔ4. Furthermore, we investigated the influence of 5-LOΔ4 on the activity of 5-LO WT and proved that it stimulates 5-LO product formation at low protein concentrations. Therefore regulation of 5-LO by its isoform 5-LOΔ4 might represent a novel mechanism of controlling the biosynthesis of lipid mediators

Drug-Mediated Intracellular Donation of Nitric Oxide Potently Inhibits 5-Lipoxygenase: A Possible Key to Future Antileukotriene Therapy

Aims: 5-Lipoxygenase (5-LO) is the key enzyme of leukotriene (LT) biosynthesis and is critically involved in a number of inflammatory diseases such as arthritis, gout, bronchial asthma, atherosclerosis, and cancer. Because 5-LO contains critical nucleophilic amino acids, which are sensitive to electrophilic modifications, we determined the consequences of a drug-mediated intracellular release of nitric oxide (NO) on 5-LO product formation by human granulocytes and on 5-LO-dependent pulmonary inflammation in vivo. Results: Clinically relevant concentrations of NO-releasing nonsteroidal anti-inflammatory drugs and other agents releasing NO intracellularly suppress 5-LO product synthesis in isolated human granulocytes via direct S-nitrosylation of 5-LO at the catalytically important cysteines 416 and 418. Furthermore, suppression of 5-LO product formation was observed in ionophore-stimulated human whole blood and in an animal model of pulmonary inflammation. Innovation: Here, we report for the first time that drugs releasing NO intracellularly are efficient 5-LO inhibitors in vitro and in vivo at least equivalent to approved 5-LO inhibitors. Conclusion: Our findings provide a novel mechanistic strategy for the development of a new class of drugs suppressing LT biosynthesis by site-directed nitrosylation. The results may also help to better understand the well-recognized anti-inflammatory clinically relevant actions of NO-releasing drugs. Furthermore, our study describes in detail a novel molecular mode of action of NO

Development of a machine vision system using the support vector machine regression (SVR) algorithm for the online prediction of iron ore grades

The mineral industry needs fast and efficient mineral quality monitoring equipment, and a machine vision system could be a suitable alternative to the traditional quality monitoring system. This study attempts to develop a machine vision-based expert system using support vector machine regression (SVR) model for the online quality monitoring of iron ores (hereafter known as ore grades). The images of the ore samples were captured during the run of condition on the fabricated conveyor belt transportation system. A total of 280 image features were extracted from each of the selected captured images in order to evaluate its suitability in object identification. A sequential forward floating selection (SFFS) algorithm was developed using the support vector machine regression (SVR) as a criterion function for selecting the optimum set of image features. The optimised feature subset was used as input, and the iron ore grade value was used as an output parameter for the model development. The grade of iron ore corresponding to each captured image was analysed in the laboratory using X-Ray Fluorescence (XRF) for grade estimation. The model was trained using 70% of the dataset and tested using 30% of the sample dataset. The model performance was evaluated using a test dataset with the five indices viz. the sum of squared errors (SSE), root mean squared error (RMSE), normalised mean squared error (NMSE), R-square (R2 ) and bias. The SSE, RMSE, NMSE and bias values of the model were obtained as 537.5367, 5.9863, 0.0063, and 0.8875, respectively. The R2 value of the model was obtained as 0.9402. The results indicate that the model performs satisfactorily for the iron ore grade prediction from the image collected in a controlled laboratory environment. The performance of the proposed model was compared with other models used in the previous studies. It was observed that the proposed model performs better than the other studied models (Gaussian Process Regression and Artificial Neural Network)