MONITORING ENZYME REACTIONS BY FAST ATOM BOMBARDMENT MASS SPECTROMETRY

The potential for the direct analysis of enzyme reactions by fast atom bombardment (FAB) mass spectrometry has been investigated. Conditions are presented for the maintenance of enzymatic activity under FAB conditions along with FAB mass spectrometric data showing that these conditions can be applied to solutions of enzyme and substrate to follow enzymatic reactions inside the mass spectrometer in real-time. In addition, enzyme kinetic behavior under FAB mass spectrometric conditions is characterized using trypsin and its assay substrate, TAME, as an enzyme-substrate reaction model. These results show that two monitoring methods can be utilized to follow reactions by FAB mass spectrometry. The advantages of each method are discussed and illustrated by obtaining kinetic parameters from the direct analysis of enzyme reactions with assay or peptide substrates. ^

Hybrid stochastic simulations of intracellular reaction-diffusion systems.

With the observation that stochasticity is important in biological systems, chemical kinetics have begun to receive wider interest. While the use of Monte Carlo discrete event simulations most accurately capture the variability of molecular species, they become computationally costly for complex reaction-diffusion systems with large populations of molecules. On the other hand, continuous time models are computationally efficient but they fail to capture any variability in the molecular species. In this study a hybrid stochastic approach is introduced for simulating reaction-diffusion systems. We developed an adaptive partitioning strategy in which processes with high frequency are simulated with deterministic rate-based equations, and those with low frequency using the exact stochastic algorithm of Gillespie. Therefore the stochastic behavior of cellular pathways is preserved while being able to apply it to large populations of molecules. We describe our method and demonstrate its accuracy and efficiency compared with the Gillespie algorithm for two different systems. First, a model of intracellular viral kinetics with two steady states and second, a compartmental model of the postsynaptic spine head for studying the dynamics of Ca+2 and NMDA receptors.

Involvement of heparin-like glycosaminoglycans and expression of a novel heparan sulfate binding protein (p24) during human placentation and in a model for human implantation

Previous studies in our laboratory have indicated that heparan sulfate proteoglycans (HSPGs) play an important role in murine embryo implantation. To investigate the potential function of HSPGs in human implantation, two human cell lines (RL95 and JAR) were selected to model uterine epithelium and embryonal trophectoderm, respectively. A heterologous cell-cell adhesion assay showed that initial binding between JAR and RL95 cells is mediated by cell surface glycosaminoglycans (GAG) with heparin-like properties, i.e., heparan sulfate and dermatan sulfate. Furthermore, a single class of highly specific, protease-sensitive heparin/heparan sulfate binding sites exist on the surface of RL95 cells. Three heparin binding, tryptic peptide fragments were isolated from RL95 cell surfaces and their amino termini partially sequenced. Reverse transcription-polymerase chain reaction (RT-PCR) generated 1 to 4 PCR products per tryptic peptide. Northern blot analysis of RNA from RL95 cells using one of these RT-PCR products identified a 1.2 Kb mRNA species (p24). The amino acid sequence predicted from the cDNA sequence contains a putative heparin-binding domain. A synthetic peptide representing this putative heparin binding domain was used to generate a rabbit polyclonal antibody (anti-p24). Indirect immunofluorescence studies on RL95 and JAR cells as well as binding studies of anti-p24 to intact RL95 cells demonstrate that p24 is distributed on the cell surface. Western blots of RL95 membrane preparations identify a 24 kDa protein (p24) highly enriched in the 100,000 g pellet plasma membrane-enriched fraction. p24 eluted from membranes with 0.8 M NaCl, but not 0.6 M NaCl, suggesting that it is a peripheral membrane component. Solubilized p24 binds heparin by heparin affinity chromatography and $\sp{125}$I-heparin binding assays. Furthermore, indirect immunofluorescence studies indicate that cytotrophoblast of floating and attached villi of the human fetal-maternal interface are recognized by anti-p24. The study also indicates that the HSPG, perlecan, accumulates where chorionic villi are attached to uterine stroma and where p24-expressing cytotrophoblast penetrate the stroma. Collectively, these data indicate that p24 is a cell surface membrane-associated heparin/heparan sulfate binding protein found in cytotrophoblast, but not many other cell types of the fetal-maternal interface. Furthermore, p24 colocalizes with HSPGs in regions of cytotrophoblast invasion. These observations are consistent with a role for HSPGs and HSPG binding proteins in human trophoblast-uterine cell interactions. ^

New methods for quantification and analysis of quantitative real-time polymerase chain reaction data

Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantitation method that has been widely used in the biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle (CT) method, linear and non-linear model fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence is usually inaccurate, and therefore can distort results. Here, we propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtracted the fluorescence in the former cycle from that in the later cycle, transforming the n cycle raw data into n-1 cycle data. Then linear regression was applied to the natural logarithm of the transformed data. Finally, amplification efficiencies and the initial DNA molecular numbers were calculated for each PCR run. To evaluate this new method, we compared it in terms of accuracy and precision with the original linear regression method with three background corrections, being the mean of cycles 1-3, the mean of cycles 3-7, and the minimum. Three criteria, including threshold identification, max R2, and max slope, were employed to search for target data points. Considering that PCR data are time series data, we also applied linear mixed models. Collectively, when the threshold identification criterion was applied and when the linear mixed model was adopted, the taking-difference linear regression method was superior as it gave an accurate estimation of initial DNA amount and a reasonable estimation of PCR amplification efficiencies. When the criteria of max R2 and max slope were used, the original linear regression method gave an accurate estimation of initial DNA amount. Overall, the taking-difference linear regression method avoids the error in subtracting an unknown background and thus it is theoretically more accurate and reliable. This method is easy to perform and the taking-difference strategy can be extended to all current methods for qPCR data analysis.^