S1P lyase regulates DNA damage responses through a novel sphingolipid feedback mechanism

The injurious consequences of ionizing radiation (IR) to normal human cells and the acquired radioresistance of cancer cells represent limitations to cancer radiotherapy. IR induces DNA damage response pathways that orchestrate cell cycle arrest, DNA repair or apoptosis such that irradiated cells are either repaired or eliminated. Concomitantly and independent of DNA damage, IR activates acid sphingomyelinase (ASMase), which generates ceramide, thereby promoting radiation-induced apoptosis. However, ceramide can also be metabolized to sphingosine-1-phosphate (S1P), which acts paradoxically as a radioprotectant. Thus, sphingolipid metabolism represents a radiosensitivity pivot point, a notion supported by genetic evidence in IR-resistant cancer cells. S1P lyase (SPL) catalyzes the irreversible degradation of S1P in the final step of sphingolipid metabolism. We show that SPL modulates the kinetics of DNA repair, speed of recovery from G2 cell cycle arrest and the extent of apoptosis after IR. SPL acts through a novel feedback mechanism that amplifies stress-induced ceramide accumulation, and downregulation/inhibition of either SPL or ASMase prevents premature cell cycle progression and mitotic death. Further, oral administration of an SPL inhibitor to mice prolonged their survival after exposure to a lethal dose of total body IR. Our findings reveal SPL to be a regulator of ASMase, the G2 checkpoint and DNA repair and a novel target for radioprotection

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure