![]() ![]() In particular, compounds that specifically inhibit some of the downstream isoprenoid biosynthesis enzymes have been developed and their effects in cancer models are emerging. While detailed in vivo data for many of these putative targets is lacking, there have been several breakthroughs in recent years that could facilitate further studies. Inhibition of these enzymes, from mevalonate kinase to geranylgeranyl diphosphate synthase, could be attractive as a single agent therapy or in combination with current agents for treatment of cancers in which isoprenylated proteins have been implicated. ![]() In addition to the reactions that are targeted in current clinical applications, there are other enzymes that have not been studied as extensively. This has drawn significant attention to inhibitors of protein prenyl transferases. The rho proteins are important for regulating cell motility, and also must be isoprenylated. For example, ras oncogenes are well known ras proteins are overexpressed in many human cancers, and these proteins must be isoprenylated to function. Due to the importance of core human isoprenoid biosynthesis for diverse cellular processes related to cancer cell growth and metastasis, inhibition of this pathway may produce beneficial anticancer consequences. Inhibitors of isoprenoid biosynthesis are widely used to treat human disease including statins and nitrogenous bisphosphonates. Overall, our model and methodology can be used to pinpoint key reactions, which, upon manipulation, may predict altered cholesterol levels and reveal insights into potential drug therapy targets under diseased conditions. A sensitivity analysis correctly uncovers the Hmgcr, Idi2 and Fdft1 sites that regulate cholesterol homeostasis. The model is capable of replicating the trends of relative cholesterol levels in Alzheimer's and Huntington's diseases and reliably simulated SLOS, desmosterolosis, and Dhcr14/Lbr knockout studies. To build the model the baseline mRNA expression levels of genes involved in cholesterol metabolism were obtained from the Allen Mouse Brain Atlas. The core metabolic reactions of cholesterol in the brain, particularly in the hippocampus, were simulated. We present a method to derive enzymatic kinetic values from mRNA expression levels for modeling biological networks without requiring further tuning. Expression levels of 15 genes altered in this study were used as a "molecular signature" in a validation study of an additional 26 subjects and predicted them as FSHD or control with 90% accuracy based on biceps and 80% accuracy based on deltoids.Īn important part of the challenge of building models of biochemical reactions is determining reaction rate constants that transform substrates into products. Controlling for a false-discovery rate of <0.25 reduced the number of differentially expressed genes in biceps to 188 and in deltoid to 7. For both muscle types, the expression differences were mild: using relaxed cutoffs for differential expression (fold change ≥1.2 nominal P value <0.01), we identified 191 and 110 genes differentially expressed between affected and control samples of biceps and deltoid muscle tissues, respectively, with 29 genes in common. Gene expression in two muscle types was analyzed using GeneChip Gene 1.0 ST arrays: biceps, which typically shows an early and severe disease involvement and deltoid, which is relatively uninvolved. To better understand the pathophysiology of FSHD and develop mRNA-based biomarkers of affected muscles, we compared global analysis of gene expression in two distinct muscles obtained from a large number of FSHD subjects and their unaffected first-degree relatives. The pathophysiology of FSHD is unknown and, as a result, there is currently no effective treatment available for this disease. Facioscapulohumeral muscular dystrophy (FSHD) is a progressive neuromuscular disorder caused by contractions of repetitive elements within the macrosatellite D4Z4 on chromosome 4q35. ![]()
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