Obesity is an important risk factor for exacerbating chronic diseases such as cardiovascular disease and cancer. High serum level of saturated free fatty acids such as palmitate is an important contributor for obesity-induced diseases. Here, we examined the contribution of inflammasome activation in vascular cells to free fatty acid-induced endothelial dysfunction and vascular injury in obesity. Our findings demonstrated that high fat diet-induced impairment of vascular integrity and enhanced vascular permeability in the myocardium in mice were significantly attenuated by Nlrp3 gene deletion. In microvascular endothelial cells (MVECs), palmitate markedly induces Nlrp3 inflammasome complex formation leading to caspase-1 activation and IL1β production. By fluorescence microscopy and flow cytometry, we observed that such palmitate-induced Nlrp3 inflammasome activated was accompanied by a reduction in inter-endothelial tight junction proteins ZO-1/ZO-2. Such palmitate-induced decrease of ZO-1/ZO-2 was also correlated with an increase in the permeability of endothelial monolayers treated with palmitates. Moreover, palmitate-induced alterations in ZO-1/ZO-2 or permeability were significantly reversed by an inflammasome activity inhibitor, YVAD, or a high mobility group box 1 (HMGB1) activity inhibitor glycyrrhizin. Lastly, blockade of cathepsin B with Ca-074Me significantly abolished palmitate-induced activation of Nlrp3 inflammasomes, down-regulation of ZO-1/ZO-2, and enhanced permeability in MVECs or their monolayers. Together, these data strongly suggest that activation of endothelial inflammasomes due to increased free fatty acids produces HMGB1, which disrupts inter-endothelial junctions and increases paracellular permeability of endothelium contributing to early onset of endothelial injury during obesity.

This paper proposes a novel method that can predict protein interaction sites in heterocomplexes using residue spatial sequence profile and evolution rate approaches. The former represents the information of multiple sequence alignments while the latter corresponds to a residueÕs evolutionary conservation score based on a phylogenetic tree. Three predictors using a support vector machines algorithm are constructed to predict whether a surface residue is a part of a protein-protein interface. The efficiency and the effectiveness of our proposed approach is verified by its better prediction performance compared with other models. The study is based on a non-redundant data set of heterodimers consisting of 69 protein chains.

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In protein-ligand binding, only a few residues contribute significantly to the ligand binding. Quantitative characterization of binding free energies of specific residues in protein-ligand binding is extremely useful in our understanding of drug resistance and rational drug design. In this paper, we present an alanine scanning approach combined with an efficient interaction entropy method to compute residue-specific protein-ligand binding free energies in protein-drug binding. In the current approach, the entropic components in the free energies of all residues binding to the ligand are explicitly computed from just a single trajectory MD simulation by using the interaction entropy method. In this approach the entropic contribution to binding free energy is determined from fluctuations of individual residue-ligand interaction energies contained in the MD trajectory. The calculated residue-specific binding free energies give relative values between those for ligand binding to the wild type protein and those to the mutants when specific results mutated to alanine. Computational study for the binding of two classes of drugs (first and second generation drugs) to target protein ALK and its mutant was performed. Important or hot spot residues with large contributions to the total binding energy are quantitatively characterized and the mutation effect for the loss of binding affinity for the first generation drug is explained. Finally, it is very interesting to note that the sum of those individual residue-specific binding free energies are in quite good agreement with the experimentally measured total binding free energies for this protein-ligand system.

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BackgroundHot spots are interface residues that contribute most binding affinity to protein-protein interaction. A compact and relevant feature subset is important for building machine learning methods to predict hot spots on protein-protein interfaces. Although different methods have been used to detect the relevant feature subset from a variety of features related to interface residues, it is still a challenge to detect the optimal feature subset for building the final model.ResultsIn this study, three different feature selection methods were compared to propose a new hybrid feature selection strategy. This new strategy was proved to effectively reduce the feature space when we were building the prediction models for identifying hotspot residues. It was tested on eighty-two features, both conventional and newly proposed. According to the strategy, combining the feature subsets selected by decision tree and mRMR (maximum Relevance Minimum Redundancy) individually, we were able to build a model with 6 features by using a PSFS (Pseudo Sequential Forward Selection) process. Compared with other state-of-art methods for the independent test set, our model had shown better or comparable predictive performances (with F-measure 0.622 and recall 0.821). Analysis of the 6 features confirmed that our newly proposed feature CNSV_REL1 was important for our model. The analysis also showed that the complementarity between features should be considered as an important aspect when conducting the feature selection.ConclusionIn this study, most important of all, a new strategy for feature selection was proposed and proved to be effective in selecting the optimal feature subset for building prediction models, which can be used to predict hot spot residues on protein-protein interfaces. Moreover, two aspects, the generalization of the single feature and the complementarity between features, were proved to be of great importance and should be considered in feature selection methods. Finally, our newly proposed feature CNSV_REL1 had been proved an alternative and effective feature in predicting hot spots by our study. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPPHOT/.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2009-5) contains supplementary material, which is available to authorized users.

Considerable controversy exists regarding the associations of dietary patterns with the risk of all-cause, CVD and stroke mortality. Therefore, a meta-analysis was conducted to elucidate the potential associations between dietary patterns and the risk of all-cause, CVD and stroke mortality. The PubMed database was searched for prospective cohort studies on the associations between dietary patterns and the risk of all-cause, CVD and stroke mortality published until February 2014. Random-effects models were used to calculate the summary relative risk estimates (SRRE) based on the highest v. the lowest category of dietary pattern scores. Stratified analyses were conducted based on sex, geographical region, follow-up duration, and adjustment/non-adjustment for energy intake. A total of thirteen prospective cohort studies involving 338 787 participants were included in the meta-analysis. There was evidence of inverse associations between the prudent/healthy dietary pattern and the risk of all-cause (SRRE ¼ 0·76, 95 % CI 0·68, 0·86) and CVD (SRRE ¼ 0·81, 95 % CI 0·75, 0·87) mortality and an absence of association between this dietary pattern and stroke mortality (SRRE ¼ 0·89, 95 % CI 0·77, 1·02). However, no significant associations were observed between the Western/unhealthy dietary pattern and the risk of all-cause (SRRE ¼ 1·07, 95 % CI 0·96, 1·20), CVD (SRRE ¼ 0·99, 95 % CI 0·91, 1·08) and stroke (SRRE ¼ 0·94, 95 % CI 0·81, 1·10) mortality. In conclusion, the findings provide evidence that greater adherence to a prudent/healthy dietary pattern is associated with a lower risk of all-cause and CVD mortality and not significantly associated with stroke mortality and that the Western/unhealthy dietary pattern is not associated with all-cause, CVD and stroke mortality. Further studies are required to confirm these findings.Key words: Dietary patterns: Mortality: All-cause mortality: Meta-analysisThe proportion of elderly people as well as the incidence of chronic diseases, such as CVD, is increasing globally. CVD remains a major public health problem and represents the leading cause of mortality, affecting millions of people in both developed and developing countries. Furthermore, it is estimated that deaths due to CVD and cancer will account for more than 50 % of total mortality cases in 2030 (1) . The incidence of CVD is rising at an alarming rate, especially among people aged $60 years, which will lead to a heavy social and economic toll worldwide (2) . Therefore, strategies to promote health and prevent deaths due to major chronic diseases, such as CVD and stroke, are required to be implemented. Dietary habits play an important role as determinants of health status. Nutrients and foods can never be eaten in isolation and have complex interactions, resulting in the masking of true associations (3) . In recent decades, the dietary pattern analysis has emerged as an alternative and complementary approach to examine the effects of overall diet on all-cause, CVD and stroke mortality instead of the assessment † Co...

Background: Auxin may have a positive effect on plants under drought stress. White clover is widely cultivated and often prone to water shortages. In the present study, we investigated the effects of exogenous indole − 3acetic acid (IAA) on growth and physiological changes of white clover under drought stress condition. The contents of endogenous IAA and other hormones including ABA, CTK, JA, GA, IAA, and SA were assayed. Moreover, expressions of auxin-responsive genes, drought-responsive genes and leaf senescence-associated genes were detected in response to exogenous IAA. Results: Compared to control, drought stress alone significantly diminished stem dry weigh, relative water content (RWC) and total chlorophyll content (Chl). Exogenous IAA treatment significantly increased RWC and Chl, whereas L-AOPP treatment drastically decreased stem dry weight, RWC and Chl under drought stress condition. Additionally, exogenous IAA treatment significantly increased ABA content and JA content, up-regulated expression of auxin responsive genes (GH3.1, GH3.9, IAA8), drought stress responsive genes (bZIP11, DREB2, MYB14, MYB48, WRKY2, WRKY56, WRKY108715 and RD22), and down-regulated expressions of auxin-responding genes (GH3.3, GH3.6, IAA27) and leaf senescence genes (SAG101 and SAG102) in the presence of PEG. Contrarily, L-AOPP treatment significantly reduced contents of ABA, GA3 and JA, down-regulated expressions of GH3.1, GH3.9, IAA8, bZIP11, DREB2, MYB14, MYB48, WRKY2, WRKY56, WRKY108715, ERD and RD22, and up-regulated SAG101 and SAG102. Conclusions: Exogenous IAA improved drought tolerance of white clover possibly due to endogenous plant hormone concentration changes and modulation of genes involving in drought stress response and leaf senescence. These results provided useful information to understand mechanisms of IAA improved drought tolerance in white clover.