Mission of randomness

Metabolism of Oxalate in Humans: A Potential Role Kynurenine Aminotransferase/Glutamine Transaminase/Cysteine Conjugate Beta-lyase Plays in Hyperoxaluria

Hyperoxaluria, excessive urinary oxalate excretion, is a significant health problem worldwide. Disrupted oxalate metabolism has been implicated in hyperoxaluria and accordingly, an enzymatic disturbance in oxalate biosynthesis can result in the primary hyperoxaluria. Alanine glyoxylate aminotransferase-1 and glyoxylate reductase, the enzymes involving glyoxylate (precursor for oxalate) metabolism, have been related to primary hyperoxalurias. Some studies suggest that other enzymes such as glycolate oxidase and alanine glyoxylate aminotransferase-2 might be associated with primary hyperoxaluria as well, but evidence of a definitive link is not strong between the clinical cases and gene mutations. There are still some idiopathic hyperoxalurias, which require a further study for the etiologies. Some aminotransferases, particularly kynurenine aminotransferases, can convert glyoxylate to glycine. Based on biochemical and structural characteristics, expression level, subcellular localization of some aminotransferases, a number of them appear able to catalyze the transamination of glyoxylate to glycine more efficiently than alanine glyoxylate aminotransferase-1. The aim of this minireview is to explore other undermining causes of primary hyperoxaluria and stimulate research toward achieving a comprehensive understanding of underlying mechanisms leading to the disease. Herein, we reviewed all aminotransferases in the liver for their functions in glyoxylate metabolism. Particularly, kynurenine aminotransferase-I and III were carefully discussed regarding their biochemical and structural characteristics, cellular localization, and enzyme inhibition. Kynurenine aminotransferase-III is, so far, the most efficient putative mitochondrial enzyme to transaminate glyoxylate to glycine in mammalian livers, might be an interesting enzyme to look over in hyperoxaluria etiology of primary hyperoxaluria and should be carefully investigated for its involvement in oxalate metabolism.

The Multiple Applications and Possible Mechanisms of the Hyperbaric Oxygenation Therapy

Hyperbaric Oxygenation Therapy (HBOT) is used as an adjunctive method for multiple diseases. The method meets the routine treating and is non-invasive, as well as provides 100% pure oxygen (O2), which is at above-normal atmospheric pressure in a specialized chamber. It is well known that in the condition of O2 deficiency, it will induce a series of adverse events. In order to prevent the injury induced by anoxia, the capability of offering pressurized O2 by HBOT seems involuntary and significant. In recent years, HBOT displays particular therapeutic efficacy in some degree, and it is thought to be beneficial to the conditions of angiogenesis, tissue ischemia and hypoxia, nerve system disease, diabetic complications, malignancies, Carbon monoxide (CO) poisoning and chronic radiation-induced injury. Single and combination HBOT are both applied in previous studies, and the manuscript is to review the current applications and possible mechanisms of HBOT. The applicability and validity of HBOT for clinical treatment remain controversial, even though it is regarded as an adjunct to conventional medical treatment with many other clinical benefits. There also exists a negative side effect of accepting pressurized O2, such as oxidative stress injury, DNA damage, cellular metabolic, activating of coagulation, endothelial dysfunction, acute neurotoxicity and pulmonary toxicity. Then it is imperative to comprehensively consider the advantages and disadvantages of HBOT in order to obtain a satisfying therapeutic outcome.

Prioritization of candidate disease genes by enlarging the seed set and fusing information of the network topology and gene expression

The identification of disease genes is very important not only to provide greater understanding of gene function and cellular mechanisms which drive human disease, but also to enhance human disease diagnosis and treatment. Recently, high-throughput techniques have been applied to detect dozens or even hundreds of candidate genes. However, experimental approaches to validate the many candidates are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Therefore, numerous theoretical and computational methods (e.g. network-based approaches) have been developed to prioritize candidate disease genes. Many network-based approaches implicitly utilize the observation that genes causing the same or similar diseases tend to correlate with each other in gene-protein relationship networks. Of these network approaches, the random walk with restart algorithm (RWR) is considered to be a state-of-the-art approach. To further improve the performance of RWR, we propose a novel method named ESFSC to identify disease-related genes, by enlarging the seed set according to the centrality of disease genes in a network and fusing information of the protein-protein interaction (PPI) network topological similarity and the gene expression correlation. The ESFSC algorithm restarts at all of the nodes in the seed set consisting of the known disease genes and their k-nearest neighbor nodes, then walks in the global network separately guided by the similarity transition matrix constructed with PPI network topological similarity properties and the correlational transition matrix constructed with the gene expression profiles. As a result, all the genes in the network are ranked by weighted fusing the above results of the RWR guided by two types of transition matrices. Comprehensive simulation results of the 10 diseases with 97 known disease genes collected from the Online Mendelian Inheritance in Man (OMIM) database show that ESFSC outperforms existing methods for prioritizing candidate disease genes. The top prediction results of Alzheimer's disease are consistent with previous literature reports.