Comparison of the hemoglobin variability in non-chronic kidney disease or end-stage renal disease participants and patients with CKD and ESRD.

Introduction: Hemoglobin (Hb) variability occurs frequently in hemodialysis (HD) patients during erythropoietin (EPO) therapy. Guidelines define a narrow target range for the anemia treatment in these patients that is difficult to adhere to in practice. Our aim was to evaluate whether the Hb variability in HD patients is higher compared to non-chronic kidney disease or end-stage renal disease (ESRD) participants and patients with CKD stage I or II.

Three-dimensional growth reveals fine-tuning of 5-lipoxygenase by proliferative pathways in cancer.

The leukotriene (LT) pathway is positively correlated with the progression of solid malignancies, but the factors that control the expression of 5-lipoxygenase (5-LO), the central enzyme in LT biosynthesis, in tumors are poorly understood. Here, we report that 5-LO along with other members of the LT pathway is up-regulated in multicellular colon tumor spheroids. This up-regulation was inversely correlated with cell proliferation and activation of PI3K/mTORC-2- and MEK-1/ERK-dependent pathways.

Knock-out of 5-lipoxygenase in overexpressing tumor cells-consequences on gene expression and cellular function.

5-Lipoxygenase (5-LO), the central enzyme in the biosynthesis of leukotrienes, is frequently expressed in human solid malignancies even though the enzyme is not present in the corresponding healthy tissues. There is little knowledge on the consequences of this expression for the tumor cells regarding gene expression and cellular function. We established a knockout (KO) of 5-LO in different cancer cell lines (HCT-116, HT-29, U-2 OS) and studied the consequences on global gene expression using next generation sequencing.

Predicting genes associated with RNA methylation pathways using machine learning.

RNA methylation plays an important role in functional regulation of RNAs, and has thus attracted an increasing interest in biology and drug discovery. Here, we collected and collated transcriptomic, proteomic, structural and physical interaction data from the Harmonizome database, and applied supervised machine learning to predict novel genes associated with RNA methylation pathways in human. We selected five types of classifiers, which we trained and evaluated using cross-validation on multiple training sets.