Progesterone down-regulates the open probability of the amiloride-sensitive epithelial sodium channel via a Nedd4-2-dependent mechanism.

Activation of the mitogen-activated protein (MAP) kinase cascade by progesterone in Xenopus oocytes leads to a marked down-regulation of activity of the amiloride-sensitive epithelial sodium channel (ENaC). Here we have studied the signaling pathways involved in progesterone effect on ENaC activity. We demonstrate that: (i) the truncation of the C termini of the alphabetagammaENaC subunits results in the loss of the progesterone effect on ENaC; (ii) the effect of progesterone was also suppressed by mutating conserved tyrosine residues in the Pro-X-X-Tyr (PY) motif of the C termini of the beta and gamma ENaC subunits (beta(Y618A) and gamma(Y628A)); (iii) the down-regulation of ENaC activity by progesterone was also suppressed by co-expression ENaC subunits with a catalytically inactive mutant of Nedd4-2, a ubiquitin ligase that has been previously demonstrated to decrease ENaC cell-surface expression via a ubiquitin-dependent internalization/degradation mechanism; (iv) the effect of progesterone was significantly reduced by suppression of consensus sites (beta(T613A) and gamma(T623A)) for ENaC phosphorylation by the extracellular-regulated kinase (ERK), a MAP kinase previously shown to facilitate the binding of Nedd4 ubiquitin ligases to ENaC; (v) the quantification of cell-surface-expressed ENaC subunits revealed that progesterone decreases ENaC open probability (whole cell P(o), wcP(o)) and not its cell-surface expression. Collectively, these results demonstrate that the binding of active Nedd4-2 to ENaC is a crucial step in the mechanism of ENaC inhibition by progesterone. Upon activation of ERK, the effect of Nedd4-2 on ENaC open probability can become more important than its effect on ENaC cell-surface expression.

A geometric mechanism of diffusion: Rigorous verification in a priori unstable Hamiltonian systems

In this paper we consider a representative a priori unstable Hamiltonian system with 2+1/2 degrees of freedom, to which we apply the geometric mechanism for diffusion introduced in the paper Delshams et al., Mem.Amer.Math. Soc. 2006, and generalized in Delshams and Huguet, Nonlinearity 2009, and provide explicit, concrete and easily verifiable conditions for the existence of diffusing orbits. The simplification of the hypotheses allows us to perform explicitly the computations along the proof, which contribute to present in an easily understandable way the geometric mechanism of diffusion. In particular, we fully describe the construction of the scattering map and the combination of two types of dynamics on a normally hyperbolic invariant manifold.

Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.

Immunological mechanism of action and clinical profile of disease-modifying treatments in multiple sclerosis.

Multiple sclerosis (MS) is a life-long, potentially debilitating disease of the central nervous system (CNS). MS is considered to be an immune-mediated disease, and the presence of autoreactive peripheral lymphocytes in CNS compartments is believed to be critical in the process of demyelination and tissue damage in MS. Although MS is not currently a curable disease, several disease-modifying therapies (DMTs) are now available, or are in development. These DMTs are all thought to primarily suppress autoimmune activity within the CNS. Each therapy has its own mechanism of action (MoA) and, as a consequence, each has a different efficacy and safety profile. Neurologists can now select therapies on a more individual, patient-tailored basis, with the aim of maximizing potential for long-term efficacy without interruptions in treatment. The MoA and clinical profile of MS therapies are important considerations when making that choice or when switching therapies due to suboptimal disease response. This article therefore reviews the known and putative immunological MoAs alongside a summary of the clinical profile of therapies approved for relapsing forms of MS, and those in late-stage development, based on published data from pivotal randomized, controlled trials.

Indirect likelihood inference

Given a sample from a fully specified parametric model, let Zn be a given finite-dimensional statistic - for example, an initial estimator or a set of sample moments. We propose to (re-)estimate the parameters of the model by maximizing the likelihood of Zn. We call this the maximum indirect likelihood (MIL) estimator. We also propose a computationally tractable Bayesian version of the estimator which we refer to as a Bayesian Indirect Likelihood (BIL) estimator. In most cases, the density of the statistic will be of unknown form, and we develop simulated versions of the MIL and BIL estimators. We show that the indirect likelihood estimators are consistent and asymptotically normally distributed, with the same asymptotic variance as that of the corresponding efficient two-step GMM estimator based on the same statistic. However, our likelihood-based estimators, by taking into account the full finite-sample distribution of the statistic, are higher order efficient relative to GMM-type estimators. Furthermore, in many cases they enjoy a bias reduction property similar to that of the indirect inference estimator. Monte Carlo results for a number of applications including dynamic and nonlinear panel data models, a structural auction model and two DSGE models show that the proposed estimators indeed have attractive finite sample properties.

Restriction site-associated DNA sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference.

Restriction site-associated DNA sequencing (RADseq) provides researchers with the ability to record genetic polymorphism across thousands of loci for nonmodel organisms, potentially revolutionizing the field of molecular ecology. However, as with other genotyping methods, RADseq is prone to a number of sources of error that may have consequential effects for population genetic inferences, and these have received only limited attention in terms of the estimation and reporting of genotyping error rates. Here we use individual sample replicates, under the expectation of identical genotypes, to quantify genotyping error in the absence of a reference genome. We then use sample replicates to (i) optimize de novo assembly parameters within the program Stacks, by minimizing error and maximizing the retrieval of informative loci; and (ii) quantify error rates for loci, alleles and single-nucleotide polymorphisms. As an empirical example, we use a double-digest RAD data set of a nonmodel plant species, Berberis alpina, collected from high-altitude mountains in Mexico.

Improving the performance of positive selection inference by filtering unreliable alignment regions.

Errors in the inferred multiple sequence alignment may lead to false prediction of positive selection. Recently, methods for detecting unreliable alignment regions were developed and were shown to accurately identify incorrectly aligned regions. While removing unreliable alignment regions is expected to increase the accuracy of positive selection inference, such filtering may also significantly decrease the power of the test, as positively selected regions are fast evolving, and those same regions are often those that are difficult to align. Here, we used realistic simulations that mimic sequence evolution of HIV-1 genes to test the hypothesis that the performance of positive selection inference using codon models can be improved by removing unreliable alignment regions. Our study shows that the benefit of removing unreliable regions exceeds the loss of power due to the removal of some of the true positively selected sites.

Quantitative genetic modeling and inference in the presence of nonignorable missing data.

Natural selection is typically exerted at some specific life stages. If natural selection takes place before a trait can be measured, using conventional models can cause wrong inference about population parameters. When the missing data process relates to the trait of interest, a valid inference requires explicit modeling of the missing process. We propose a joint modeling approach, a shared parameter model, to account for nonrandom missing data. It consists of an animal model for the phenotypic data and a logistic model for the missing process, linked by the additive genetic effects. A Bayesian approach is taken and inference is made using integrated nested Laplace approximations. From a simulation study we find that wrongly assuming that missing data are missing at random can result in severely biased estimates of additive genetic variance. Using real data from a wild population of Swiss barn owls Tyto alba, our model indicates that the missing individuals would display large black spots; and we conclude that genes affecting this trait are already under selection before it is expressed. Our model is a tool to correctly estimate the magnitude of both natural selection and additive genetic variance.

Electron cryo-microscopy reveals mechanism of action of propranolol on artificial membranes.

The pharmacological activity of several amphiphilic drugs is often related to their ability to interact with biological membranes. Propranolol is an efficient multidrug resistance (MDR) modulator; it is a nonselective beta-blocker and is thought to reduce hypertension by decreasing the cardiac frequency and thus blood pressure. It is used in drug delivery studies in order to treat systemic hypertension. We are interested in the interaction of propranolol with artificial membranes, as liposomes of controllable size are used as biocompatible and protective structures to encapsulate labile molecules, such as proteins, nucleic acids or drugs, for pharmaceutical, cosmetic or chemical applications. We present here a study of the interaction of propranolol, a cationic surfactant, with pure egg phosphatidylcholine (EPC) vesicles. The gradual transition from liposome to micelle of EPC vesicles in the presence of propranolol was monitored by time-resolved electron cryo-microscopy (cryo-EM) under different experimental conditions. The liposome-drug interaction was studied with varying drug/lipid (D/L) ratios and different stages were captured by direct thin-film vitrification. The time-series cryo-EM data clearly illustrate the mechanism of action of propranolol on the liposome structure: the drug disrupts the lipid bilayer by perturbing the local organization of the phospholipids. This is followed by the formation of thread-like micelles, also called worm-like micelles (WLM), and ends with the formation of spherical (globular) micelles. The overall reaction is slow, with the process taking almost two hours to be completed. The effect of a monovalent salt was also investigated by repeating the lipid-surfactant interaction experiments in the presence of KCl as an additive to the lipid/drug suspension. When KCl was added in the presence of propranolol the overall reaction was the same but with slower kinetics, suggesting that this monovalent salt affects the general lipid-to-micelle transition by stabilizing the membrane, presumably by binding to the carbonyl chains of the phosphatidylcholine.

Presence of alternative lengthening of telomeres mechanism in patients with glioblastoma identifies a less aggressive tumor type with longer survival.

Patients with glioblastoma (GBM) have variable clinical courses, but the factors that underlie this heterogeneity are not understood. To determine whether the presence of the telomerase-independent alternative lengthening of telomeres (ALTs) mechanism is a significant prognostic factor for survival, we performed a retrospective analysis of 573 GBM patients. The presence of ALT was identified in paraffin sections using a combination of immunofluorescence for promyelocytic leukemia body and telomere fluorescence in situ hybridization. Alternative lengthening of telomere was present in 15% of the GBM patients. Patients with ALT had longer survival that was independent of age, surgery, and other treatments. Mutations in isocitrate dehydrogenase (IDH1mut) 1 frequently accompanied ALT, and in the presence of both molecular events, there was significantly longer overall survival. These data suggest that most ALT+ tumors may be less aggressive proneural GBMs, and the better prognosis may relate to the set of genetic changes associated with this tumor subtype. Despite improved overall survival of patients treated with the addition of chemotherapy to radiotherapy and surgery, ALT and chemotherapy independently provided a survival advantage, but these factors were not found to be additive. These results suggest a critical need for developing new therapies to target these specific GBM subtypes.

Analysis of hepatitis C virus resistance to silibinin in vitro and in vivo points to a novel mechanism involving nonstructural protein 4B.

Intravenous silibinin (SIL) is an approved therapeutic that has recently been applied to patients with chronic hepatitis C, successfully clearing hepatitis C virus (HCV) infection in some patients even in monotherapy. Previous studies suggested multiple antiviral mechanisms of SIL; however, the dominant mode of action has not been determined. We first analyzed the impact of SIL on replication of subgenomic replicons from different HCV genotypes in vitro and found a strong inhibition of RNA replication for genotype 1a and genotype 1b. In contrast, RNA replication and infection of genotype 2a were minimally affected by SIL. To identify the viral target of SIL we analyzed resistance to SIL in vitro and in vivo. Selection for drug resistance in cell culture identified a mutation in HCV nonstructural protein (NS) 4B conferring partial resistance to SIL. This was corroborated by sequence analyses of HCV from a liver transplant recipient experiencing viral breakthrough under SIL monotherapy. Again, we identified distinct mutations affecting highly conserved amino acid residues within NS4B, which mediated phenotypic SIL resistance also in vitro. Analyses of chimeric viral genomes suggest that SIL might target an interaction between NS4B and NS3/4A. Ultrastructural studies revealed changes in the morphology of viral membrane alterations upon SIL treatment of a susceptible genotype 1b isolate, but not of a resistant NS4B mutant or genotype 2a, indicating that SIL might interfere with the formation of HCV replication sites. CONCLUSION: Mutations conferring partial resistance to SIL treatment in vivo and in cell culture argue for a mechanism involving NS4B. This novel mode of action renders SIL an attractive candidate for combination therapies with other directly acting antiviral drugs, particularly in difficult-to-treat patient cohorts.

Inference of distributional parameters from compositional samples containing nondetects

Low concentrations of elements in geochemical analyses have the peculiarity of beingcompositional data and, for a given level of significance, are likely to be beyond thecapabilities of laboratories to distinguish between minute concentrations and completeabsence, thus preventing laboratories from reporting extremely low concentrations of theanalyte. Instead, what is reported is the detection limit, which is the minimumconcentration that conclusively differentiates between presence and absence of theelement. A spatially distributed exhaustive sample is employed in this study to generateunbiased sub-samples, which are further censored to observe the effect that differentdetection limits and sample sizes have on the inference of population distributionsstarting from geochemical analyses having specimens below detection limit (nondetects).The isometric logratio transformation is used to convert the compositional data in thesimplex to samples in real space, thus allowing the practitioner to properly borrow fromthe large source of statistical techniques valid only in real space. The bootstrap method isused to numerically investigate the reliability of inferring several distributionalparameters employing different forms of imputation for the censored data. The casestudy illustrates that, in general, best results are obtained when imputations are madeusing the distribution best fitting the readings above detection limit and exposes theproblems of other more widely used practices. When the sample is spatially correlated, itis necessary to combine the bootstrap with stochastic simulation