Sequence context dependence of tandem guanine:adenine mismatch conformations in RNA: a continuum solvent analysis

Guanine:adenine (G:A) mismatches and in particular tandem G:A (tG:A) mismatches are frequently observed in biological RNA molecules and can serve as sites for tertiary interaction, metal binding and protein recognition. Depending on the surrounding sequence tG:A mismatches can adopt different basepairing topologies. In the sequence context (5'-) GGAC (tandem G:A in bold) a face-to-face (imino or Watson-Crick-like) pairing is preferred whereas in the CGAG context, G and A adopt a sheared arrangement. Systematic conformational searches with a generalized Born continuum model and molecular dynamics simulations including explicit water molecules and ions have been used to generate face-to-face and sheared tG:A mismatches in both CGAG and GGAC sequence contexts. Conformations from both approaches were evaluated using the same force field and a Poisson-Boltzmann continuum solvent model. Although the substate analysis predicted the sheared arrangement to be energetically preferred in both sequence contexts, a significantly greater preference of the sheared form was found for the CGAG context. In agreement with the experimental observation, the analysis of molecular dynamics trajectories indicated a preference of the sheared form in the case of the CGAG-context and a favorization of the face-to-face form in the case of the GGAC context. The computational studies allowed to identify energetic contributions that stabilize or destabilize the face-to-face and sheared tandem mismatch topologies. The calculated nonpolar solvation and Lennard-Jones packing interaction were found to stabilize the sheared topology independent of the sequence context. Electrostatic contributions are predicted to make the most significant contribution to the sequence context dependence on the structural preference of tG:A mismatches.     

Seasonal alterations in adrenocortical cell function associated with stress-responsiveness and sex in the eastern fence lizard (Sceloporus undulatus)

We characterized steroidogenic properties of dispersed adrenocortical cells from field-active male and female eastern fence lizards (Sceloporus undulatus) to investigate whether alterations in cell function could, in part, explain seasonal variation in baseline and stress-induced plasma corticosterone (B). Lizards were collected during the breeding and postbreeding seasons and shortly prior to hibernation. Dispersed cells in vitro produced B, aldosterone (ALDO), and progesterone in response to 8-Br-cAMP, 25-(OH)cholesterol, adrenocorticotropin (ACTH; as little as 100 fM), and angiotensin II. Maximal progesterone, B, and ALDO responses to ACTH were roughly 1000%, 500%, and 100% greater than corresponding basal values. Angiotensin II was an effective steroidogenic stimulant but much less so than ACTH. Corticosteroid production exhibited considerable steroid-specific variation among seasons. Maximal ACTH-induced B production was lower in the postbreeding season than at either of the other two measurement points, essentially opposite to the pattern for ALDO. Males and females generally produced B at similar rates, but ALDO and progesterone showed numerous sex differences that usually covaried between the two steroids. Cellular sensitivity to 25-(OH)cholesterol and angiotensin II showed few sex differences or seasonal changes. In contrast, sensitivity to ACTH decreased markedly from the breeding to the postbreeding season in males, corresponding to the decrease in stress-responsiveness, and in both sexes was considerably lower prior to hibernation than during the breeding season. Under some conditions, plasma B may be limited by the production capacity of adrenocortical cells. In summary, seasonal variations in body condition, reproductive activity, and baseline and stress-induced plasma B may be attributed at least in part to alterations in adrenocortical cell steroidogenic function.     

A comparison of methods for predicting vegetation type

Predictive modeling of vegetation patterns has wide application in vegetation science. In this paper I discuss three methods of predictive modeling using data from the alpine treeline ecotone as a case study. The study area is a portion of Glacier National Park, Montana. Parametric general linear models (GLM), artificial neural networks (ANN) and classification tree (CT) methods of predicting vegetation type are compared to determine the relative strength of each predictive approach and how they may be used in concert to increase understanding of important vegetation – environment relations. For each predictive method, vegetation type within the alpine treeline ecotone is predicted using a suite of environmental indicator variables including elevation, moisture potential, solar radiation potential, snow potential index, and disturbance history. Results from each of the predictive methods are compared against the real vegetation types to determine the relative accuracy of the methods.When the entire data field is examined (i.e., not evaluated by smaller spatial aggregates of data) the ANN procedure produces the most accurate predictions (=0.571); the CT predictions are the least accurate (=0.351). The predicted patterns of vegetation on the landscape are considerably different using the three methods. The GLM and CT methods produce large contiguous swaths of vegetation types throughout the study area, whereas the ANN method produces patterns with much more heterogeneity and smaller patches.When predictions are compared to reality at catchment scale, it becomes evident that the accuracy of each method varies depending upon the specific situation. The ANN procedure remains the most accurate method in the majority of the catchments, but both the GLM and PCT produce the most accurate classifications in at least one basin each.The variability in predictive ability of the three methods tested here indicates that there may not be a single best predictive method. Rather it may be important to use a suite of predictive models to help understand the environment – vegetation relationships. The ability to use multiple predictive methods to determine which spatial subunits of a landscape are outliers is important when identifying locations useful for climate change monitoring studies.     

Symmetric DNA sites are functionally asymmetric within Flp and Cre site-specific DNA recombination synapses

Flp and Cre-mediated recombination on symmetrized FRT and loxP sites, respectively, in circular plasmid substrates yield both DNA inversion and deletion. However, upon sequestering three negative supercoils outside the recombination complex using the resII-resIII synapse formed by Tn3 resolvase and the LER synapse formed by phage Mu transposase in the case of Flp and Cre, respectively, the reactions are channeled towards inversion at the expense of deletion. The inversion product is a trefoil, its unique topology being conferred by the external resolvase or LER synapse. Thus, Flp and Cre assign their symmetrized substrates a strictly antiparallel orientation with respect to strand cleavage and exchange. These conclusions are supported by the product profiles from tethered parallel and antiparallel native FRT sites in dilution and competition assays. Furthermore, the observed recombination bias favoring deletion over inversion in a nicked circular substrate containing two symmetrized FRT sites is consistent with the predictions from Monte Carlo simulations based on antiparallel synapsis of the DNA partners.     

Gender difference regarding selenium penetration into the mouse brain

A sex difference in the penetration of selenium into the brain was observed using lipopolysaccharide (LPS)-injected mice. The selenium concentration increased in the brains of sodium selenite-injected LPS-treated female mice, but not males. The selenium concentration peaked when selenite was injected 3 h after the injection of LPS into female mice. In addition, selenium in the brain increased when a dosage of 30 μmol/kg and more of selenite was injected into LPS-treated female mice. Also, the selenium concentration in the brain increased and peaked 2–3 h after selenite injection; 24 h later, the level was similar to the Se-only group. The penetration of selenium into the brain was inhibited by pretreatment with aminoguanidine, an inhibitor of nitric oxide synthetase. From the present results, selenium more easily penetrated into the brains of female mice compared to males after LPS treatment, and nitric oxide may have affected the penetration. However, the sex difference mechanism for selenium penetration needs further investigation.     

Protein molecular dynamics with electrostatic force entirely determined by a single Poisson-Boltzmann calculation

The electrostatic force including the intramolecular Coulombic interactions and the electrostatic contribution of solvation effect were entirely calculated by using the finite difference Poisson-Boltzmann method (FDPB), which was incorporated into the GROMOS96 force field to complete a new finite difference stochastic dynamics procedure (FDSD). Simulations were performed on an insulin dimer. Different relative dielectric constants were successively assigned to the protein interior; a value of 17 was selected as optimal for our system. The simulation data were analyzed and compared with those obtained from 500-ps molecular dynamics (MD) simulation with explicit water and a 500-ps conventional stochastic dynamics (SD) simulation without the mean solvent force. The results indicate that the FDSD method with GROMOS96 force field is suitable to study the dynamics and structure of proteins in solution if used with the optimal protein dielectric constant.     

Robust inference for event probabilities with non-Markov event data

Multistate event data, in which a single subject is at risk for multiple events, is common in biomedical applications. This article considers nonparametric estimation of the vector of probabilities of state membership at time t. Estimators, derived under the Markov assumption, have been shown (Datta and Satten, 2001, Statistics and Probability Letters 55, 403-411) to be consistent for data that is non-Markov. Inference, however, must take into account possibly non-Markov transitions when constructing confidence bands for event curves. We develop robust confidence bands for these curves, evaluate them via simulation, and illustrate the method on two datasets.