STITCHER: Dynamic assembly of likely amyloid and prion β‐structures from secondary structure predictions

The supersecondary structure of amyloids and prions, proteins of intense clinical and biological interest, are difficult to determine by standard experimental or computational means. In addition, significant conformational heterogeneity is known or suspected to exist in many amyloid fibrils. Previous work has demonstrated that probability‐based prediction of discrete β‐strand pairs can offer insight into these structures. Here, we devise a system of energetic rules that can be used to dynamically assemble these discrete β‐strand pairs into complete amyloid β‐structures. The STITCHER algorithm progressively ‘stitches’ strand‐pairs into full β‐sheets based on a novel free‐energy model, incorporating experimentally observed amino‐acid side‐chain stacking contributions, entropic estimates, and steric restrictions for amyloidal parallel β‐sheet construction. A dynamic program computes the top 50 structures and returns both the highest scoring structure and a consensus structure taken by polling this list for common discrete elements. Putative structural heterogeneity can be inferred from sequence regions that compose poorly. Predictions show agreement with experimental models of Alzheimer's amyloid beta peptide and the Podospora anserina Het‐s prion. Predictions of the HET‐s homolog HET‐S also reflect experimental observations of poor amyloid formation. We put forward predicted structures for the yeast prion Sup35, suggesting N‐terminal structural stability enabled by tyrosine ladders, and C‐terminal heterogeneity. Predictions for the Rnq1 prion and alpha‐synuclein are also given, identifying a similar mix of homogenous and heterogeneous secondary structure elements. STITCHER provides novel insight into the energetic basis of amyloid structure, provides accurate structure predictions, and can help guide future experimental studies. Proteins 2012. © 2011 Wiley Periodicals, Inc.     

Peroxisome proliferator-activated receptor γ decouples fatty acid uptake from lipid inhibition of insulin signaling in skeletal muscle

Peroxisome proliferator-activated receptor γ (PPARγ) is expressed at low levels in skeletal muscle, where it protects against adiposity and insulin resistance via unclear mechanisms. To test the hypothesis that PPARγ directly modulates skeletal muscle metabolism, we created two models that isolate direct PPARγ actions on skeletal myocytes. PPARγ was overexpressed in murine myotubes by adenotransfection and in mouse skeletal muscle by plasmid electroporation. In cultured myotubes, PPARγ action increased fatty acid uptake and incorporation into myocellular lipids, dependent upon a 154 ± 20-fold up-regulation of CD36 expression. PPARγ overexpression more than doubled insulin-stimulated thymoma viral proto-oncogene (AKT) phosphorylation during low lipid availability. Furthermore, in myotubes exposed to palmitate levels that inhibit insulin signaling, PPARγ overexpression increased insulin-stimulated AKT phosphorylation and glycogen synthesis over 3-fold despite simultaneously increasing myocellular palmitate uptake. The insulin signaling enhancement was associated with an increase in activating phosphorylation of phosphoinositide-dependent protein kinase 1 and a normalized expression of palmitate-induced genes that antagonize AKT phosphorylation. In vivo, PPARγ overexpression more than doubled insulin-dependent AKT phosphorylation in lipid-treated mice but did not augment insulin-stimulated glucose uptake. We conclude that direct PPARγ action promotes myocellular storage of energy by increasing fatty acid uptake and esterification while simultaneously enhancing insulin signaling and glycogen formation. However, direct PPARγ action in skeletal muscle is not sufficient to account for the hypoglycemic actions of PPARγ agonists during lipotoxicity.     

Periodontitis in established rheumatoid arthritis patients: a cross-sectional clinical,microbiological and serological study

Introduction

The association between rheumatoid arthritis (RA) and periodontitis is suggested to be linked to the periodontal pathogen Porphyromonas gingivalis. Colonization of P. gingivalis in the oral cavity of RA patients has been scarcely considered. To further explore whether the association between periodontitis and RA is dependent on P. gingivalis, we compared host immune responses in RA patients with and without periodontitis in relation to presence of cultivable P. gingivalis in subgingival plaque.

Methods

In 95 RA patients, the periodontal condition was examined using the Dutch Periodontal Screening Index for treatment needs. Subgingival plaque samples were tested for presence of P. gingivalis by anaerobic culture technique. IgA, IgG and IgM antibody titers to P. gingivalis were measured by ELISA. Serum and subgingival plaque measures were compared to a matched control group of non-RA subjects.

Results

A higher prevalence of severe periodontitis was observed in RA patients in comparison to matched non-RA controls (27% versus 12%, p < 0.001). RA patients with severe periodontitis had higher DAS28 scores than RA patients with no or moderate periodontitis (p < 0.001), while no differences were seen in IgM-RF or ACPA reactivity. Furthermore, RA patients with severe periodontitis had higher IgG- and IgM-anti P. gingivalis titers than non-RA controls with severe periodontitis (p < 0.01 resp. p < 0.05), although subgingival occurrence of P. gingivalis was not different.

Conclusions

Severity of periodontitis is related to severity of RA. RA patients with severe periodontitis have a more robust antibody response against P. gingivalis than non-RA controls, but not all RA patients have cultivable P. gingivalis.     

Characterizing and predicting species distributions across environments and scales: Argentine ant occurrences in the eye of the beholder

Aim Species distribution models (SDMs) or, more specifically, ecological niche models (ENMs) are a useful and rapidly proliferating tool in ecology and global change biology. ENMs attempt to capture associations between a species and its environment and are often used to draw biological inferences, to predict potential occurrences in unoccupied regions and to forecast future distributions under environmental change. The accuracy of ENMs, however, hinges critically on the quality of occurrence data. ENMs often use haphazardly collected data rather than data collected across the full spectrum of existing environmental conditions. Moreover, it remains unclear how processes affecting ENM predictions operate at different spatial scales. The scale (i.e. grain size) of analysis may be dictated more by the sampling regime than by biologically meaningful processes. The aim of our study is to jointly quantify how issues relating to region and scale affect ENM predictions using an economically important and ecologically damaging invasive species, the Argentine ant (Linepithema humile). Location California, USA. Methods We analysed the relationship between sampling sufficiency, regional differences in environmental parameter space and cell size of analysis and resampling environmental layers using two independently collected sets of presence/absence data. Differences in variable importance were determined using model averaging and logistic regression. Model accuracy was measured with area under the curve (AUC) and Cohen's kappa. Results We first demonstrate that insufficient sampling of environmental parameter space can cause large errors in predicted distributions and biological interpretation. Models performed best when they were parametrized with data that sufficiently sampled environmental parameter space. Second, we show that altering the spatial grain of analysis changes the relative importance of different environmental variables. These changes apparently result from how environmental constraints and the sampling distributions of environmental variables change with spatial grain. Conclusions These findings have clear relevance for biological inference. Taken together, our results illustrate potentially general limitations for ENMs, especially when such models are used to predict species occurrences in novel environments. We offer basic methodological and conceptual guidelines for appropriate sampling and scale matching.     

Dominance–diversity relationships in ant communities differ with invasion

The relationship between levels of dominance and species richness is highly contentious, especially in ant communities. The dominance‐impoverishment rule states that high levels of dominance only occur in species‐poor communities, but there appear to be many cases of high levels of dominance in highly diverse communities. The extent to which dominant species limit local richness through competitive exclusion remains unclear, but such exclusion appears more apparent for non‐native rather than native dominant species. Here we perform the first global analysis of the relationship between behavioral dominance and species richness. We used data from 1,293 local assemblages of ground‐dwelling ants distributed across five continents to document the generality of the dominance‐impoverishment rule, and to identify the biotic and abiotic conditions under which it does and does not apply. We found that the behavioral dominance–diversity relationship varies greatly, and depends on whether dominant species are native or non‐native, whether dominance is considered as occurrence or relative abundance, and on variation in mean annual temperature. There were declines in diversity with increasing dominance in invaded communities, but diversity increased with increasing dominance in native communities. These patterns occur along the global temperature gradient. However, positive and negative relationships are strongest in the hottest sites. We also found that climate regulates the degree of behavioral dominance, but differently from how it shapes species richness. Our findings imply that, despite strong competitive interactions among ants, competitive exclusion is not a major driver of local richness in native ant communities. Although the dominance‐impoverishment rule applies to invaded communities, we propose an alternative dominance‐diversification rule for native communities.     

Wrap-and-Pack: a new paradigm for beta structural motif recognition with application to recognizing beta trefoils.

A method is presented that uses beta-strand interactions at both the sequence and the atomic level, to predict beta-structural motifs of protein sequences. A program called Wrap-and- Pack implements this method and is shown to recognize beta-trefoils, an important class of globular beta-structures, in the Protein Data Bank with 92% specificity and 92.3% sensitivity in cross-validation. It is demonstrated that Wrap-and-Pack learns each of the ten known SCOP beta-trefoil families, when trained primarily on beta-structures that are not beta-trefoils, together with three-dimensional structures of known beta-trefoils from outside the family. Wrap-and-Pack also predicts many proteins of unknown structure to be beta-trefoils. The computational method used here may generalize to other beta-structures for which strand topology and profiles of residue accessibility are well conserved.     

Kinetics and mechanism of oxygen-independent hydrocarbon hydroxylation by ethylbenzene dehydrogenase

Ethylbenzene dehydrogenase (EBDH) from the denitrifying bacterium Azoarcus sp. strain EbN1 (to be renamed Aromatoleum aromaticum) catalyzes the oxygen-independent, stereospecific hydroxylation of ethylbenzene to (S)-1-phenylethanol, the first known example of direct anaerobic oxidation of a nonactivated hydrocarbon. The enzyme is a trimeric molybdenum/iron-sulfur/heme protein of 155 kDa that is quickly inactivated in air in its reduced state. Enzyme activity can be coupled to ferricenium tetrafluoroborate, providing a convenient way for kinetic measurements. EBDH exhibits activity with a wide range of ethylbenzene analogues, which were analyzed for their kinetic parameters, stoichiometry, and formed products. The reactivity was correlated to the chemical structures by a quantitative structure-activity relationship (QSAR) model. On the basis of these results, quantum chemical calculations of DeltaG298 for formation of carbocations of the respective substrates were performed and used in reactivity analysis. A putative reaction mechanism is proposed on the basis of the experimental results and theoretical considerations. Finally, the enzyme reaction has been established in an electrochemical reactor, allowing sustained enzymatic reaction and potential technical applications of the enzyme.