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. ^

Differential expression of urate oxidase in rat liver

An affinity-purified monospecific antibody was prepared to study the differential expression of the peroxisomal enzyme urate oxidase in rat liver during development and in various metabolic states. Monospecific antibody for urate oxidase was affinity purified from a pool of antibodies initially produced against a mixture of proteins from a Percoll density gradient fraction. Immunogold staining of samples of the gradient fraction and rat liver tissue with the affinity-purified antibody demonstrated labelling of peroxisomal core structures. Screening of liver homogenates from rats at different developmental stages using immunoblot analysis demonstrated low levels of urate oxidase prior to 20 days of age; at 20 days of age, urate oxidase levels are 2-fold greater than the 15-day old levels and approximate adult levels. Catalase expression during rat development mimicked the differential expression pattern of urate oxidase. The increase between days 15 and 20 was determined to be independent of the process of weaning. Administration of exogenous glucocorticoid hormone to 10-day old rats resulted in a precocious rise (2.5-fold) in urate oxidase levels, but adrenalectomy at 10 days of age did not cause decreased expression in the fourth week of life. In adult animals, exogenous glucocorticoid did not influence urate oxidase levels, but adrenalectomized rats had urate oxidase levels that were 40 percent of control expression 4 days post-surgery. Catalase expression was not influenced by glucocorticoid status in these studies. Glucocorticoid regulation of urate oxidase expression appears to be one part of a more complex mechanism controlling levels of the enzyme. Exogenous glucocorticoid administration influenced urate oxidase levels in an age-dependent manner; in addition, it is possible that the control mechanism for urate oxidase may include factors which can modulate expression in the absence of glucocorticoids. The effect of glucocorticoids on urate oxidase expression can not be extended to include all peroxisomal proteins, since catalase is unaffected. Glucocorticoids appear to participate in the complex regulation of urate oxidase expression; glucocorticoids influence urate oxidase specifically and do not modulate all peroxisomal proteins. ^

Interim analysis of clinical trials: Simulation studies of fractional Brownian motion

Interim clinical trial monitoring procedures were motivated by ethical and economic considerations. Classical Brownian motion (Bm) techniques for statistical monitoring of clinical trials were widely used. Conditional power argument and α-spending function based boundary crossing probabilities are popular statistical hypothesis testing procedures under the assumption of Brownian motion. However, it is not rare that the assumptions of Brownian motion are only partially met for trial data. Therefore, I used a more generalized form of stochastic process, called fractional Brownian motion (fBm), to model the test statistics. Fractional Brownian motion does not hold Markov property and future observations depend not only on the present observations but also on the past ones. In this dissertation, we simulated a wide range of fBm data, e.g., H = 0.5 (that is, classical Bm) vs. 0.5< H <1, with treatment effects vs. without treatment effects. Then the performance of conditional power and boundary-crossing based interim analyses were compared by assuming that the data follow Bm or fBm. Our simulation study suggested that the conditional power or boundaries under fBm assumptions are generally higher than those under Bm assumptions when H > 0.5 and also matches better with the empirical results. ^

Statistical Methods for Differential Expressions of Genes Detected in Multiple-Condition Experiment of Microarray

Most studies of differential gene-expressions have been conducted between two given conditions. The two-condition experimental (TCE) approach is simple in that all genes detected display a common differential expression pattern responsive to a common two-condition difference. Therefore, the genes that are differentially expressed under the other conditions other than the given two conditions are undetectable with the TCE approach. In order to address the problem, we propose a new approach called multiple-condition experiment (MCE) without replication and develop corresponding statistical methods including inference of pairs of conditions for genes, new t-statistics, and a generalized multiple-testing method for any multiple-testing procedure via a control parameter C. We applied these statistical methods to analyze our real MCE data from breast cancer cell lines and found that 85 percent of gene-expression variations were caused by genotypic effects and genotype-ANAX1 overexpression interactions, which agrees well with our expected results. We also applied our methods to the adenoma dataset of Notterman et al. and identified 93 differentially expressed genes that could not be found in TCE. The MCE approach is a conceptual breakthrough in many aspects: (a) many conditions of interests can be conducted simultaneously; (b) study of association between differential expressions of genes and conditions becomes easy; (c) it can provide more precise information for molecular classification and diagnosis of tumors; (d) it can save lot of experimental resources and time for investigators.^

Differential Activity of the KRAS Oncogene by Method of Activation: Implications for Signaling and Therapeutic Intervention

Despite having been identified over thirty years ago and definitively established as having a critical role in driving tumor growth and predicting for resistance to therapy, the KRAS oncogene remains a target in cancer for which there is no effective treatment. KRas is activated b y mutations at a few sites, primarily amino acid substitutions at codon 12 which promote a constitutively active state. I have found that different amino acid substitutions at codon 12 can activate different KRas downstream signaling pathways, determine clonogenic growth potential and determine patient response to molecularly targeted therapies. Computer modeling of the KRas structure shows that different amino acids substituted at the codon 12 position influences how KRas interacts with its effecters. In the absence of a direct inhibitor of mutant KRas several agents have recently entered clinical trials alone and in combination directly targeting two of the common downstream effecter pathways of KRas, namely the Mapk pathway and the Akt pathway. These inhibitors were evaluated for efficacy against different KRAS activating mutations. An isogenic panel of colorectal cells with wild type KRas replaced with KRas G12C, G12D, or G12V at the endogenous loci differed in sensitivity to Mek and Akt inhibition. In contrast, screening was performed in a broad panel of lung cell lines alone and no correlation was seen between types of activating KRAS mutation due to concurrent oncogenic lesions. To find a new method to inhibit KRAS driven tumors, siRNA screens were performed in isogenic lines with and without active KRas. The knockdown of CNKSR1 (CNK1) showed selective growth inhibition in cells with an oncogenic KRAS. The deletion of CNK1 reduces expression of mitotic cell cycle proteins and arrests cells with active KRas in the G1 phase of the cell cycle similar to the deletion of an activated KRas regardless of activating substitution. CNK1 has a PH domain responsible for localizing it to membrane lipids making KRas potentially amenable to inhibition with small molecules. The work has identified a series of small molecules capable of binding to this PH domain and inhibiting CNK1 facilitated KRas signaling.