Selective Constraints on Amino Acids Estimated by a Mechanistic Codon Substitution Model with Multiple Nucleotide Changes

Empirical substitution matrices represent the average tendencies of substitutions over various protein families by sacrificing gene-level resolution. We develop a codon-based model, in which mutational tendencies of codon, a genetic code, and the strength of selective constraints against amino acid replacements can be tailored to a given gene. First, selective constraints averaged over proteins are estimated by maximizing the likelihood of each 1-PAM matrix of empirical amino acid (JTT, WAG, and LG) and codon (KHG) substitution matrices. Then, selective constraints specific to given proteins are approximated as a linear function of those estimated from the empirical substitution matrices. Akaike information criterion (AIC) values indicate that a model allowing multiple nucleotide changes fits the empirical substitution matrices significantly better. Also, the ML estimates of transition-transversion bias obtained from these empirical matrices are not so large as previously estimated. The selective constraints are characteristic of proteins rather than species. However, their relative strengths among amino acid pairs can be approximated not to depend very much on protein families but amino acid pairs, because the present model, in which selective constraints are approximated to be a linear function of those estimated from the JTT/WAG/LG/KHG matrices, can provide a good fit to other empirical substitution matrices including cpREV for chloroplast proteins and mtREV for vertebrate mitochondrial proteins. The present codon-based model with the ML estimates of selective constraints and with adjustable mutation rates of nucleotide would be useful as a simple substitution model in ML and Bayesian inferences of molecular phylogenetic trees, and enables us to obtain biologically meaningful information at both nucleotide and amino acid levels from codon and protein sequences.Comment: Table 9 in this article includes corrections for errata in the Table 9 published in 10.1371/journal.pone.0017244. Supporting information is attached at the end of the article, and a computer-readable dataset of the ML estimates of selective constraints is available from 10.1371/journal.pone.001724

Processing and characterization of chitosan microspheres to be used as templates for layer-by-layer assembly

Chitosan (Ch) microspheres have been developed by precipitation method, cross-linked with glutaraldehyde and used as a template for layer-by-layer (LBL) deposition of two natural polyelectrolytes. Using a LBL methodology, Ch microspheres were alternately coated with hyaluronic acid (HA) and Ch under mild conditions. The roughness of the Ch-based crosslinked microspheres was characterized by atomic force microscopy (AFM). Morphological characterization was performed by environmental scanning electron microscopy (ESEM), scanning electron microscopy (SEM) and stereolight microscopy. The swelling behaviour of the microspheres demonstrated that the ones with more bilayers presented the highest water uptake and the uncoated cross-linked Ch microspheres showed the lowest uptake capability. Microspheres presented spherical shape with sizes ranging from 510 to 840 lm. ESEM demonstrated that a rougher surface with voids is formed in multilayered microspheres caused by the irregular stacking of the layers. A short term mechanical stability assay was also performed, showing that the LBL procedure with more than five bilayers of HA/Ch over Ch cross-linked microspheres provide higher mechanical stability

Integrating Quantitative Knowledge into a Qualitative Gene Regulatory Network

Despite recent improvements in molecular techniques, biological knowledge remains incomplete. Any theorizing about living systems is therefore necessarily based on the use of heterogeneous and partial information. Much current research has focused successfully on the qualitative behaviors of macromolecular networks. Nonetheless, it is not capable of taking into account available quantitative information such as time-series protein concentration variations. The present work proposes a probabilistic modeling framework that integrates both kinds of information. Average case analysis methods are used in combination with Markov chains to link qualitative information about transcriptional regulations to quantitative information about protein concentrations. The approach is illustrated by modeling the carbon starvation response in Escherichia coli. It accurately predicts the quantitative time-series evolution of several protein concentrations using only knowledge of discrete gene interactions and a small number of quantitative observations on a single protein concentration. From this, the modeling technique also derives a ranking of interactions with respect to their importance during the experiment considered. Such a classification is confirmed by the literature. Therefore, our method is principally novel in that it allows (i) a hybrid model that integrates both qualitative discrete model and quantities to be built, even using a small amount of quantitative information, (ii) new quantitative predictions to be derived, (iii) the robustness and relevance of interactions with respect to phenotypic criteria to be precisely quantified, and (iv) the key features of the model to be extracted that can be used as a guidance to design future experiments

The evolutionary history of the catenin gene family during metazoan evolution

<p>Abstract</p> <p>Background</p> <p>Catenin is a gene family composed of three subfamilies; p120, beta and alpha. Beta and p120 are homologous subfamilies based on sequence and structural comparisons, and are members of the armadillo repeat protein superfamily. Alpha does not appear to be homologous to either beta or p120 based on the lack of sequence and structural similarity, and the alpha subfamily belongs to the vinculin superfamily. Catenins link the transmembrane protein cadherin to the cytoskeleton and thus function in cell-cell adhesion. To date, only the beta subfamily has been evolutionarily analyzed and experimentally studied for its functions in signaling pathways, development and human diseases such as cancer. We present a detailed evolutionary study of the whole catenin family to provide a better understanding of how this family has evolved in metazoans, and by extension, the evolution of cell-cell adhesion.</p> <p>Results</p> <p>All three catenin subfamilies have been detected in metazoans used in the present study by searching public databases and applying species-specific BLAST searches. Two monophyletic clades are formed between beta and p120 subfamilies using Bayesian phylogenetic inference. Phylogenetic analyses also reveal an array of duplication events throughout metazoan history. Furthermore, numerous annotation issues for the catenin family have been detected by our computational analyses.</p> <p>Conclusions</p> <p>Delta2/ARVCF catenin in the p120 subfamily, beta catenin in the beta subfamily, and alpha2 catenin in the alpha subfamily are present in all metazoans analyzed. This implies that the last common ancestor of metazoans had these three catenin subfamilies. However, not all members within each subfamily were detected in all metazoan species. Each subfamily has undergone duplications at different levels (species-specific, subphylum-specific or phylum-specific) and to different extents (in the case of the number of homologs). Extensive annotation problems have been resolved in each of the three catenin subfamilies. This resolution provides a more coherent description of catenin evolution.</p

Prefrontal response and frontostriatal functional connectivity to monetary reward in abstinent alcohol-dependent young adults

Although altered function in neural reward circuitry is widely proposed in models of addiction, more recent conceptual views have emphasized the role of disrupted response in prefrontal regions. Changes in regions such as the orbitofrontal cortex, medial prefrontal cortex, and dorsolateral prefrontal cortex are postulated to contribute to the compulsivity, impulsivity, and altered executive function that are central to addiction. In addition, few studies have examined function in these regions during young adulthood, when exposure is less chronic than in typical samples of alcohol-dependent adults. To address these issues, we examined neural response and functional connectivity during monetary reward in 24 adults with alcohol dependence and 24 psychiatrically healthy adults. Adults with alcohol dependence exhibited less response to the receipt of monetary reward in a set of prefrontal regions including the medial prefrontal cortex, lateral orbitofrontal cortex, and dorsolateral prefrontal cortex. Adults with alcohol dependence also exhibited greater negative correlation between function in each of these regions and that in the nucleus accumbens. Within the alcohol-dependent group, those with family history of alcohol dependence exhibited lower mPFC response, and those with more frequent drinking exhibited greater negative functional connectivity between the mPFC and the nucleus accumbens. These findings indicate that alcohol dependence is associated with less engagement of prefrontal cortical regions, suggesting weak or disrupted regulation of ventral striatal response. This pattern of prefrontal response and frontostriatal connectivity has consequences for the behavior patterns typical of addiction. Furthermore, brain-behavior findings indicate that the potential mechanisms of disruption in frontostriatal circuitry in alcohol dependence include family liability to alcohol use problems and more frequent use of alcohol. In all, these findings build on the extant literature on reward-circuit function in addiction and suggest mechanisms for disrupted function in alcohol dependence. © 2014 Forbes et al

Advantages of a Mechanistic Codon Substitution Model for Evolutionary Analysis of Protein-Coding Sequences

A mechanistic codon substitution model, in which each codon substitution rate is proportional to the product of a codon mutation rate and the average fixation probability depending on the type of amino acid replacement, has advantages over nucleotide, amino acid, and empirical codon substitution models in evolutionary analysis of protein-coding sequences. It can approximate a wide range of codon substitution processes. If no selection pressure on amino acids is taken into account, it will become equivalent to a nucleotide substitution model. If mutation rates are assumed not to depend on the codon type, then it will become essentially equivalent to an amino acid substitution model. Mutation at the nucleotide level and selection at the amino acid level can be separately evaluated.The present scheme for single nucleotide mutations is equivalent to the general time-reversible model, but multiple nucleotide changes in infinitesimal time are allowed. Selective constraints on the respective types of amino acid replacements are tailored to each gene in a linear function of a given estimate of selective constraints. Their good estimates are those calculated by maximizing the respective likelihoods of empirical amino acid or codon substitution frequency matrices. Akaike and Bayesian information criteria indicate that the present model performs far better than the other substitution models for all five phylogenetic trees of highly-divergent to highly-homologous sequences of chloroplast, mitochondrial, and nuclear genes. It is also shown that multiple nucleotide changes in infinitesimal time are significant in long branches, although they may be caused by compensatory substitutions or other mechanisms. The variation of selective constraint over sites fits the datasets significantly better than variable mutation rates, except for 10 slow-evolving nuclear genes of 10 mammals. An critical finding for phylogenetic analysis is that assuming variable mutation rates over sites lead to the overestimation of branch lengths

Biomimetic, Mild Chemical Synthesis of CdTe-GSH Quantum Dots with Improved Biocompatibility

Multiple applications of nanotechnology, especially those involving highly fluorescent nanoparticles (NPs) or quantum dots (QDs) have stimulated the research to develop simple, rapid and environmentally friendly protocols for synthesizing NPs exhibiting novel properties and increased biocompatibility

Intersection of inflammation and herbal medicine in the treatment of osteoarthritis

Herbal remedies and dietary supplements have become an important area of research and clinical practice in orthopaedics and rheumatology. Understanding the risks and benefits of using herbal medicines in the treatment of arthritis, rheumatic diseases, and musculoskeletal complaints is a key priority of physicians and their patients. This review discusses the latest advances in the use of herbal medicines for treating osteoarthritis (OA) by focusing on the most significant trends and developments. This paper sets the scene by providing a brief introduction to ethnopharmacology, Ayurvedic medicine, and nutrigenomics before discussing the scientific and mechanistic rationale for targeting inflammatory signalling pathways in OA by use of herbal medicines. Special attention is drawn to the conceptual and practical difficulties associated with translating data from in-vitro experiments to in-vivo studies. Issues relating to the low bioavailability of active ingredients in herbal medicines are discussed, as also is the need for large-scale, randomized clinical trial

Asymmetric Wolbachia Segregation during Early Brugia malayi Embryogenesis Determines Its Distribution in Adult Host Tissues

Wolbachia are required for filarial nematode survival and fertility and contribute to the immune responses associated with human filarial diseases. Here we developed whole-mount immunofluorescence techniques to characterize Wolbachia somatic and germline transmission patterns and tissue distribution in Brugia malayi, a nematode responsible for lymphatic filariasis. In the initial embryonic divisions, Wolbachia segregate asymmetrically such that they occupy only a small subset of cells in the developing embryo, facilitating their concentration in the adult hypodermal chords and female germline. Wolbachia are not found in male reproductive tissues and the absence of Wolbachia from embryonic germline precursors in half of the embryos indicates Wolbachia loss from the male germline may occur in early embryogenesis. Wolbachia rely on fusion of hypodermal cells to populate adult chords. Finally, we detect Wolbachia in the secretory canal lumen suggesting living worms may release bacteria and/or their products into their host

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior