Down-regulated miR-448 relieves spinal cord ischemia/reperfusion injury by up-regulating SIRT1

MicroRNAs play a crucial role in the progression of spinal cord ischemia/reperfusion injury (SCII). The role of miR-448 and SIRT1 in SCII was investigated in this study, to provide further insights into prevention and improvement of this disorder. In this study, expressions of miR-448 and SIRT1 protein were determined by qRT-PCR and western blot, respectively. Flow cytometry was used to analyze cell apoptosis. The endogenous expression of genes was modulated by recombinant plasmids and cell transfection. Dual-luciferase reporter assay was performed to determine the interaction between miR-448 and SIRT1. The Basso, Beattie, and Bresnahan score was used to measure the hind-limb function of rat. The spinal cord ischemia reperfusion injury model of adult rats was developed by abdominal aorta clamping, and the nerve function evaluation was completed by motor deficit index score. In SCII tissues and cells treated with hypoxia, miR-448 was up-regulated while SIRT1 was down-regulated. Hypoxia treatment reduced the expression of SIRT1 through up-regulating miR-448 in nerve cells. Up-regulation of miR-448 induced by hypoxia promoted apoptosis of nerve cells through down-regulating SIRT1. Down-regulated miR-448 improved neurological function and hind-limb motor function of rats with SCII by up-regulating SIRT1. Down-regulated miR-448 inhibited apoptosis of nerve cells and improved neurological function by up-regulating SIRT1, which contributes to relieving SCII.

Comparisons Between Different Methods in Measuring Enzyme Similarity for Metabolic Network Alignment

Metabolic network alignments enable comparison of the similarities and differences between pathways in two metabolic networks and help to uncover the conserved sub-blocks therein. Such analysis is important in the understanding of metabolic networks and species evolution. The fundamental parts of metabolic network alignment algorithms all involve comparisons of the similarity between two enzymes as a similarity measure of network nodes. As a result, the study of methods for measuring enzyme similarity becomes highly relevant. Currently, two approaches are mainly used to measure enzyme similarity. One of the methods is based on similarity measures of gene or protein sequences; the other is based on enzyme classification. In this study, multiple metabolic network alignments were performed using both the methods. The results showed that, in general, the sequence similarity method yielded higher accuracy, especially with respect to reflecting evolutionary distances.