The differential expression of the $\beta\sb2$-adrenergic receptor modifies agonist-dependent adenylylcyclase activation

There have been multiple reports which indicate that variations in $\beta$AR expression affect the V$\sb{\rm max}$ observed for the agonist-dependent activation of adenylylcyclase. This observation has been ignored by most researchers when V$\sb{\rm max}$ values obtained for wild type and mutant receptors are compared. Such an imprecise analysis may lead to erroneous conclusions concerning the ability of a receptor to activate adenylylcyclase. Equations were derived from the Cassel-Selinger model of GTPase activity and Tolkovsky and Levitzki's Collision Coupling model which predict that the EC$\sb{50}$ and V$\sb{\rm max}$ for the activation of adenylylcyclase are a function of receptor number. Experimental results for L cell clones in which either hamster or human $\beta$AR were transfected at varying levels showed that EC$\sb{50}$ decreases and V$\sb{\rm max}$ increases as receptor number increases. Comparison of these results with simulations obtained from the equations describing EC$\sb{50}$ and V$\sb{\rm max}$ showed a close correlation. This documents that the kinetic parameters of adenylylcyclase activation change with the level of receptor expression and relates this phenomenon to a theoretical framework concerning the mechanisms involved in $\beta$AR signal transduction.^ One of the terms used in the equations which expressed the EC$\sb{50}$ and V$\sb{\rm max}$ as a function of receptor number is coupling efficiency, defined as $\rm k\sb1/k\sb{-1}$. Calculation of $\rm k\sb1/k\sb{-1}$ can be accomplished for wild type receptors with the easily measured experimental values of agonist K$\sb{\rm d}$, EC$\sb{50}$ and receptor number. This was demonstrated for hamster $\beta$AR which yielded a coupling efficiency of 0.15 $\pm$ 0.003 and human $\beta$AR which yielded a coupling efficiency of 0.90 $\pm$ 0.031. $\rm k\sb1/k\sb{-1}$ replaces the traditional qualitative evaluation of the ability to activate adenylylcyclase, which utilizes V$\sb{\rm max}$ without correction for variation in receptor number, with a quantitative definition that more accurately describes the ability of $\beta$AR to couple to G$\sb{\rm s}$.^ The equations which express the EC$\sb{50}$ and V$\sb{\rm max}$ for adenylylcyclase activation as a function of receptor number and coupling efficiency were tested to determine whether they could accurately simulate the changes seen in these parameters during desensitization. Data from original desensitization experiments and data from the literature (24,25,52,54,83) were compared to simulated changes in EC$\sb{50}$ and V$\sb{\rm max}$. In a variety of systems the predictions of the equations were consistent with the changes observed in EC$\sb{50}$ and V$\sb{\rm max}$. In addition reductions in the calculated value of $\rm k\sb1/k\sb{-1}$ was shown to correlate well with $\beta$AR phosphorylation and to be minimally affected by sequestration and down-regulation. ^

Comparison of multiple comparison methods for identifying differential gene expression in simulated and real papillary thyroid cancer microarray data

The difficulty of detecting differential gene expression in microarray data has existed for many years. Several correction procedures try to avoid the family-wise error rate in multiple comparison process, including the Bonferroni and Sidak single-step p-value adjustments, Holm's step-down correction method, and Benjamini and Hochberg's false discovery rate (FDR) correction procedure. Each multiple comparison technique has its advantages and weaknesses. We studied each multiple comparison method through numerical studies (simulations) and applied the methods to the real exploratory DNA microarray data, which detect of molecular signatures in papillary thyroid cancer (PTC) patients. According to our results of simulation studies, Benjamini and Hochberg step-up FDR controlling procedure is the best process among these multiple comparison methods and we discovered 1277 potential biomarkers among 54675 probe sets after applying the Benjamini and Hochberg's method to PTC microarray data.^