EP1-4 Receptors


(33). primarily a transcriptional view of the cell. Augmenting these methods still further with networks and additional -omics data has been found to yield pathways that play more fundamental functions. We propose a previously undescribed method for identification of such pathways that takes a more direct approach to the problem than any published to date. Our method, called latent pathway identification analysis (LPIA), looks for statistically significant evidence of dysregulation in a network of pathways constructed in a manner that implicitly links pathways through their common function in the cell. We describe the LPIA methodology and illustrate its effectiveness through analysis of data on (pathways called by LPIA into a single gene set, for = 5, 10, 15, and 20 pathways. Each gene set was then compared with the full set of Hsp90 interactors, computing an enrichment value according to a hypergeometrical distribution. The results show that in all three experiments, the collapsed gene set reported by LPIA is usually highly enriched with Hsp90 interactors (values 10? 4 for all those cases but IC50 at 24 h with = 5, which was 0.19). Thus, as a whole, the collection of pathways identified by LPIA is usually strongly linked to the set of Hsp90 interactors. We then looked at Hsp90 enrichment with respect to individual pathway ranking. We computed a value for each KEGG pathway, summarizing its enrichment with Hsp90 interactors, and ranked pathways according to their values. This ranked list, acting as a gold standard, was then compared, in turn, with each of the ranked lists produced by LPIA for all those three treatments. Comparisons between the top pathways of the gold standard and those of the method being assessed were made using both Euclidean distance of rank vectors and number of true-positive findings as a function of pathways assessed by LPIA from the gold standard list. (pathways in the gold standard list. The dotted lines in and indicate the mean expected by random chance (via simulating random ranked lists) with 1-SD error bars. Type 2 Diabetes. The third dataset we chose to evaluate was an analysis of the transcriptional differences of skeletal muscle tissue among patients with type 2 diabetes, impaired glucose tolerance, and normal glucose tolerance, as reported by Gallagher et al. (33). We performed the analysis on the two populations possessing the strongest binary comparison: gene transcription of skeletal muscle cells of people diagnosed with type 2 Tmem47 diabetes (45 subjects) compared with that of skeletal muscle cells exhibiting normal glucose tolerance (47 subjects). Under this comparison, LPIA uniquely identified the oxidative phosphorylation pathway after multiple test correction with an FDR level of 0.20. Dysregulation of genes in the mitochondrial oxidative phosphorylation pathway is usually characteristic of both human diabetic skeletal muscle and liver samples of patients with type 2 diabetes (34, U0126-EtOH 35). Additionally, muscle biopsies in patients with type U0126-EtOH 2 diabetes showed decreased activity of mitochondrial oxidative enzymes (36), and analysis of healthy patients revealed that increased levels of intramyocellular lipid content, an indicator of U0126-EtOH insulin resistance, were caused by inherited mitochondrial oxidative phosphorylation defects (37). Takamura et al. (38) further explored the correlation between obesity and diabetes, showing that oxidative gene expression significantly correlated with insulin resistance and reactive oxygen species generation in liver specimens. Oxidative phosphorylation was not identified at the transcriptional level in the same dataset by Gallagher et al. (33). These results demonstrate that LPIA may be implemented as a diagnostic tool capable of identifying disease characteristics, such as insulin resistance caused by dysfunctional oxidative phosphorylation in patients with type 2 diabetes. Discussion The analyses performed in these three biological contexts highlight the ability of LPIA to provide effective biological insight into alterations of cellular function. The first example demonstrates how use of LPIA resolves the multifactorial process of malignancy metastasis in the identification of cellular signaling pathways key to initiation, cell growth, and propagation. The KEGG pathways identified are instrumental as an aggregate leading to the progression of the tumor. The second example illustrates how the analysis may be used for drug target deconvolution strategies. Although not providing a direct protein target, the cellular processes governing the antiproliferative effects were identified. Finally, the U0126-EtOH type 2 diabetes analysis demonstrates how LPIA may be used U0126-EtOH as a diagnostic tool in a clinical context, identifying a clearly dysregulated biological pathway not readily resolved by using transcriptional measurements alone. Although each biological scenario uses transcriptional alterations as an analytical technique, each one is unique in the type of.