Characterization of osteopontin charge forms produced by osteoblasts

Osteopontin (OPN) is a highly-phosphorylated extracellular matrix protein localized in bone, kidney, placenta, T-lymphocytes, macrophages, smooth muscle of the vascular system, milk, urine, and plasma. In ROS 17/2.8 osteoblast-like osteosarcoma cells, 1,25-dihydroxyvitamin D3 [1,25(OH)2D 3] regulates OPN at the transcriptional level resulting in increased steady state mRNA levels and increased production of OPN protein, maximal at 48 hours. Using ROS 17/2.8 cells as an osteoblast model, OPN was purified from culture medium after three hour treatments of either vehicle (ethanol) or 1,25(OH)2D3 via barium citrate precipitation followed by immunoaffinity chromatography. ^ Here, further evidence of regulation of OPN by 1,25(OH)2D 3 at the posttranslational level is presented. Prior to the up-regulation of OPN at the transcriptional level, 1,25(OH)2D3 induces a shift in OPN isoelectric point (pI) detected on two-dimensional gels from pI 4.6 to pI 5.1. Loading equal amounts of [32P]-labeled OPN recovered from ROS 17/2.8 cells exposed to 1,25(OH)2D3 or vehicle alone for three hours reveals that the shift from pI 4.6 to 5.1 is the result of reduced phosphorylation. Using structural analogs to 1,25(OH) 2D3, analog AT [25-(OH)-16-ene-23-yne-D3], which triggers Ca2+ influx through voltage sensitive Ca2+ channels but does not bind to the vitamin D receptor, mimicked the OPN pI shift while analog BT [1,25(OH)2-22-ene-24-cyclopropyl-D 3], which binds to the vitamin D receptor but does not allow Ca 2+ influx, did not. Inclusion of the Ca2+ channel blocker nifedipine also blocks the charge shift conversion of OPN. Further analysis of the signaling pathway initiated by 1,25(OH)2D3 reveals that inhibition of the cyclic 3′,5′ -adenosine monophosphate-dependent kinase, protein kinase A, or inhibition of the cyclic 3′,5′-guanine monophosphate-dependent kinase, protein kinase G, also prevents the charge shift conversion. ^ Isolation of OPN from rat femurs and tibiae provides evidence for the existence of these two OPN charge forms in vivo, evidenced by differential migration on isoelectric focusing gels and sodium dodecyl sulfate-polyacrylamide gels. Peptide sequencing of rat long bone fractions revealed the presence of a presumed dentin specific protein, dentin matrix protein-1 (DMP-1). Western blot analysis confirmed the existence of DMP-1 in these fractions. ^ Using the OPN charge forms in functional assays, it was determined that the charge forms have differential roles in both cell surface and mineralization functions. In cell attachment assays and Ca2+ influx assays using PC-3 prostate cancer cells, the pI 5.1 charge form of OPN was found to permit binding and increase intracellular Ca2+ concentrations of PC-3 cells. The increase in intracellular Ca2+ concentration was found to be integrin αvβ3-dependent. In mineralization assays, the pI 4.6 charge form of OPN promoted hydroxyapatite formation, while the pI 5.1 charge form had improved Ca2+ binding ability. ^ In conclusion, these findings suggest that 1,25(OH) 2D3 regulates OPN not only at the transcriptional level, but also plays a role in determination of the OPN phosphorylation state. The latter involves a short term (less than three hours) treatment and is associated with membrane-initiated Ca2+ influx. Functional assays utilizing the two OPN charge forms reveal the dependence of OPN post-translational state on its function. ^

Dynamics of a minimal model of interlocked positive and negative feedback loops of transcriptional regulation by cAMP-response element binding proteins.

cAMP-response element binding (CREB) proteins are involved in transcriptional regulation in a number of cellular processes (e.g., neural plasticity and circadian rhythms). The CREB family contains activators and repressors that may interact through positive and negative feedback loops. These loops can be generated by auto- and cross-regulation of expression of CREB proteins, via CRE elements in or near their genes. Experiments suggest that such feedback loops may operate in several systems (e.g., Aplysia and rat). To understand the functional implications of such feedback loops, which are interlocked via cross-regulation of transcription, a minimal model with a positive and negative loop was developed and investigated using bifurcation analysis. Bifurcation analysis revealed diverse nonlinear dynamics (e.g., bistability and oscillations). The stability of steady states or oscillations could be changed by time delays in the synthesis of the activator (CREB1) or the repressor (CREB2). Investigation of stochastic fluctuations due to small numbers of molecules of CREB1 and CREB2 revealed a bimodal distribution of CREB molecules in the bistability region. The robustness of the stable HIGH and LOW states of CREB expression to stochastic noise differs, and a critical number of molecules was required to sustain the HIGH state for days or longer. Increasing positive feedback or decreasing negative feedback also increased the lifetime of the HIGH state, and persistence of this state may correlate with long-term memory formation. A critical number of molecules was also required to sustain robust oscillations of CREB expression. If a steady state was near a deterministic Hopf bifurcation point, stochastic resonance could induce oscillations. This comparative analysis of deterministic and stochastic dynamics not only provides insights into the possible dynamics of CREB regulatory motifs, but also demonstrates a framework for understanding other regulatory processes with similar network architecture.

Dynamics of a minimal model of interlocked positive and negative feedback loops of transcriptional regulation by cAMP-response element binding proteins.

cAMP-response element binding (CREB) proteins are involved in transcriptional regulation in a number of cellular processes (e.g., neural plasticity and circadian rhythms). The CREB family contains activators and repressors that may interact through positive and negative feedback loops. These loops can be generated by auto- and cross-regulation of expression of CREB proteins, via CRE elements in or near their genes. Experiments suggest that such feedback loops may operate in several systems (e.g., Aplysia and rat). To understand the functional implications of such feedback loops, which are interlocked via cross-regulation of transcription, a minimal model with a positive and negative loop was developed and investigated using bifurcation analysis. Bifurcation analysis revealed diverse nonlinear dynamics (e.g., bistability and oscillations). The stability of steady states or oscillations could be changed by time delays in the synthesis of the activator (CREB1) or the repressor (CREB2). Investigation of stochastic fluctuations due to small numbers of molecules of CREB1 and CREB2 revealed a bimodal distribution of CREB molecules in the bistability region. The robustness of the stable HIGH and LOW states of CREB expression to stochastic noise differs, and a critical number of molecules was required to sustain the HIGH state for days or longer. Increasing positive feedback or decreasing negative feedback also increased the lifetime of the HIGH state, and persistence of this state may correlate with long-term memory formation. A critical number of molecules was also required to sustain robust oscillations of CREB expression. If a steady state was near a deterministic Hopf bifurcation point, stochastic resonance could induce oscillations. This comparative analysis of deterministic and stochastic dynamics not only provides insights into the possible dynamics of CREB regulatory motifs, but also demonstrates a framework for understanding other regulatory processes with similar network architecture.

STUDIES ON THE PATTERNS OF NUCLEOTIDE AND AMINO ACID SUBSTITUTION (ELECTROPHORESIS, MUTATION RATE, SELECTION, BASE, COMPOSITION)

Theoretical and empirical studies were conducted on the pattern of nucleotide and amino acid substitution in evolution, taking into account the effects of mutation at the nucleotide level and purifying selection at the amino acid level. A theoretical model for predicting the evolutionary change in electrophoretic mobility of a protein was also developed by using information on the pattern of amino acid substitution. The specific problems studied and the main results obtained are as follows: (1) Estimation of the pattern of nucleotide substitution in DNA nuclear genomes. The pattern of point mutations and nucleotide substitutions among the four different nucleotides are inferred from the evolutionary changes of pseudogenes and functional genes, respectively. Both patterns are non-random, the rate of change varying considerably with nucleotide pair, and that in both cases transitions occur somewhat more frequently than transversions. In protein evolution, substitution occurs more often between amino acids with similar physico-chemical properties than between dissimilar amino acids. (2) Estimation of the pattern of nucleotide substitution in RNA genomes. The majority of mutations in retroviruses accumulate at the reverse transcription stage. Selection at the amino acid level is very weak, and almost non-existent between synonymous codons. The pattern of mutation is very different from that in DNA genomes. Nevertheless, the pattern of purifying selection at the amino acid level is similar to that in DNA genomes, although selection intensity is much weaker. (3) Evaluation of the determinants of molecular evolutionary rates in protein-coding genes. Based on rates of nucleotide substitution for mammalian genes, the rate of amino acid substitution of a protein is determined by its amino acid composition. The content of glycine is shown to correlate strongly and negatively with the rate of substitution. Empirical formulae, called indices of mutability, are developed in order to predict the rate of molecular evolution of a protein from data on its amino acid sequence. (4) Studies on the evolutionary patterns of electrophoretic mobility of proteins. A theoretical model was constructed that predicts the electric charge of a protein at any given pH and its isoelectric point from data on its primary and quaternary structures. Using this model, the evolutionary change in electrophoretic mobilities of different proteins and the expected amount of electrophoretically hidden genetic variation were studied. In the absence of selection for the pI value, proteins will on the average evolve toward a mildly basic pI. (Abstract shortened with permission of author.) ^

An ordinal logistic regression model with misclassification of the outcome variable and categorical covariate

Ordinal outcomes are frequently employed in diagnosis and clinical trials. Clinical trials of Alzheimer's disease (AD) treatments are a case in point using the status of mild, moderate or severe disease as outcome measures. As in many other outcome oriented studies, the disease status may be misclassified. This study estimates the extent of misclassification in an ordinal outcome such as disease status. Also, this study estimates the extent of misclassification of a predictor variable such as genotype status. An ordinal logistic regression model is commonly used to model the relationship between disease status, the effect of treatment, and other predictive factors. A simulation study was done. First, data based on a set of hypothetical parameters and hypothetical rates of misclassification was created. Next, the maximum likelihood method was employed to generate likelihood equations accounting for misclassification. The Nelder-Mead Simplex method was used to solve for the misclassification and model parameters. Finally, this method was applied to an AD dataset to detect the amount of misclassification present. The estimates of the ordinal regression model parameters were close to the hypothetical parameters. β1 was hypothesized at 0.50 and the mean estimate was 0.488, β2 was hypothesized at 0.04 and the mean of the estimates was 0.04. Although the estimates for the rates of misclassification of X1 were not as close as β1 and β2, they validate this method. X 1 0-1 misclassification was hypothesized as 2.98% and the mean of the simulated estimates was 1.54% and, in the best case, the misclassification of k from high to medium was hypothesized at 4.87% and had a sample mean of 3.62%. In the AD dataset, the estimate for the odds ratio of X 1 of having both copies of the APOE 4 allele changed from an estimate of 1.377 to an estimate 1.418, demonstrating that the estimates of the odds ratio changed when the analysis includes adjustment for misclassification. ^

Process to develop a State Model Law to reduce prescription drug trafficking while protecting pain management

This paper will discuss the intersection of pill mills and the under-treatment of pain, while addressing the unintended consequence that cracking down on pill mills actually has on medical professionals' treatment of legitimate pain in clinical settings. Moreover, the impact each issue has on the spectrum of related policy, regulatory issues and legislation will be analyzed while addressing the national impact on medical care. Lastly, this paper will outline a process to develop a State Model Law on this subject. This process will include suggestions for the future and how we can move forward to adequately address public safety needs and how we can attempt to mitigate the unintended impact prescription drug trafficking has had on a patient's right to appropriate pain management. This balance is achievable and this paper will address ways we can find this elusive balancing point through the development of a State Model Law. ^

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^