Detection of 10-nm Superparamagnetic Iron Oxide Nanoparticles Using Exchange-Biased GMR Sensors in Wheatstone Bridge

published_or_final_versio

Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information

<p>Abstract</p> <p>Background</p> <p>As the number of non-synonymous single nucleotide polymorphisms (nsSNPs), also known as single amino acid polymorphisms (SAPs), increases rapidly, computational methods that can distinguish disease-causing SAPs from neutral SAPs are needed. Many methods have been developed to distinguish disease-causing SAPs based on both structural and sequence features of the mutation point. One limitation of these methods is that they are not applicable to the cases where protein structures are not available. In this study, we explore the feasibility of classifying SAPs into disease-causing and neutral mutations using only information derived from protein sequence.</p> <p>Results</p> <p>We compiled a set of 686 features that were derived from protein sequence. For each feature, the distance between the wild-type residue and mutant-type residue was computed. Then a greedy approach was used to select the features that were useful for the classification of SAPs. 10 features were selected. Using the selected features, a decision tree method can achieve 82.6% overall accuracy with 0.607 Matthews Correlation Coefficient (MCC) in cross-validation. When tested on an independent set that was not seen by the method during the training and feature selection, the decision tree method achieves 82.6% overall accuracy with 0.604 MCC. We also evaluated the proposed method on all SAPs obtained from the Swiss-Prot, the method achieves 0.42 MCC with 73.2% overall accuracy. This method allows users to make reliable predictions when protein structures are not available. Different from previous studies, in which only a small set of features were arbitrarily chosen and considered, here we used an automated method to systematically discover useful features from a large set of features well-annotated in public databases.</p> <p>Conclusion</p> <p>The proposed method is a useful tool for the classification of SAPs, especially, when the structure of the protein is not available.</p

Dislocations and the enhancement of superconductivity in odd-parity superconductor Sr2_2RuO4_4

We report observation of the enhancement of superconductivity near lattice dislocations and the absence of the strengthening of vortex pinning in odd-parity superconductor Sr$_2$RuO$_4$, both surprising results in direct contrast to the well known sensitivity of superconductivity in Sr$_2$RuO$_4$ to disorder. The enhanced superconductivity appears to be related fundamentally to the two-component nature of the superconducting order parameter, as revealed in our phenomenological theory taking into account the effect of symmetry reduction near a dislocation.Comment: 5 pages, 4 figures, submitted to Physical Review Letter

Improving the prediction of disease-related variants using protein three-dimensional structure

Background: Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability. Non-synonymous SNPs occurring in coding regions result in single amino acid polymorphisms (SAPs) that may affect protein function and lead to pathology. Several methods attempt to estimate the impact of SAPs using different sources of information. Although sequence-based predictors have shown good performance, the quality of these predictions can be further improved by introducing new features derived from three-dimensional protein structures.Results: In this paper, we present a structure-based machine learning approach for predicting disease-related SAPs. We have trained a Support Vector Machine (SVM) on a set of 3,342 disease-related mutations and 1,644 neutral polymorphisms from 784 protein chains. We use SVM input features derived from the protein's sequence, structure, and function. After dataset balancing, the structure-based method (SVM-3D) reaches an overall accuracy of 85%, a correlation coefficient of 0.70, and an area under the receiving operating characteristic curve (AUC) of 0.92. When compared with a similar sequence-based predictor, SVM-3D results in an increase of the overall accuracy and AUC by 3%, and correlation coefficient by 0.06. The robustness of this improvement has been tested on different datasets and in all the cases SVM-3D performs better than previously developed methods even when compared with PolyPhen2, which explicitly considers in input protein structure information.Conclusion: This work demonstrates that structural information can increase the accuracy of disease-related SAPs identification. Our results also quantify the magnitude of improvement on a large dataset. This improvement is in agreement with previously observed results, where structure information enhanced the prediction of protein stability changes upon mutation. Although the structural information contained in the Protein Data Bank is limiting the application and the performance of our structure-based method, we expect that SVM-3D will result in higher accuracy when more structural date become available. \ua9 2011 Capriotti; licensee BioMed Central Ltd

Incidence, mechanism and prognostic value of activated AKT in pancreas cancer

When activated, the serine/threonine kinase AKT mediates an antiapoptotic signal implicated in chemoresistance of various cancers. The mechanism(s) of AKT activation are unknown, though overexpression of HER-2/neu has been implicated in breast cancer. Therefore, we determined the incidence of activated AKT in human pancreatic cancer, whether HER-2/neu is involved in AKT activation, and if AKT activation is associated with biologic behaviour. HER-2/neu expression and AKT activation were examined in seven pancreatic cancer cell lines by Western blotting. The in vitro effect of HER-2/neu inhibition on AKT activation was similarly determined. Finally, 78 pancreatic cancer specimens were examined for AKT activation and HER-2/neu overexpression, and correlated with the clinical prognostic variable of histologic grade. HER-2/neu was overexpressed in two of seven cell lines; these two cell lines demonstrated the highest level of AKT activation. Inhibition of HER-2/neu reduced AKT activation in vitro. AKT was activated in 46 out of 78 (59%) of the pancreatic cancers; HER-2/neu overexpression correlated with AKT activation (P=0.015). Furthermore, AKT activation was correlated with higher histologic tumour grade (P=0.047). Thus, it is concluded that AKT is frequently activated in pancreatic cancer; this antiapoptotic signal may be mediated by HER-2/neu overexpression. AKT activation is associated with tumour grade, an important prognostic factor

Prediction of Deleterious Non-Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties

Non-synonymous SNPs (nsSNPs), also known as Single Amino acid Polymorphisms (SAPs) account for the majority of human inherited diseases. It is important to distinguish the deleterious SAPs from neutral ones. Most traditional computational methods to classify SAPs are based on sequential or structural features. However, these features cannot fully explain the association between a SAP and the observed pathophysiological phenotype. We believe the better rationale for deleterious SAP prediction should be: If a SAP lies in the protein with important functions and it can change the protein sequence and structure severely, it is more likely related to disease. So we established a method to predict deleterious SAPs based on both protein interaction network and traditional hybrid properties. Each SAP is represented by 472 features that include sequential features, structural features and network features. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) were applied to obtain the optimal feature set and the prediction model was Nearest Neighbor Algorithm (NNA). In jackknife cross-validation, 83.27% of SAPs were correctly predicted when the optimized 263 features were used. The optimized predictor with 263 features was also tested in an independent dataset and the accuracy was still 80.00%. In contrast, SIFT, a widely used predictor of deleterious SAPs based on sequential features, has a prediction accuracy of 71.05% on the same dataset. In our study, network features were found to be most important for accurate prediction and can significantly improve the prediction performance. Our results suggest that the protein interaction context could provide important clues to help better illustrate SAP's functional association. This research will facilitate the post genome-wide association studies

Overexpression of a Common Wheat Gene TaSnRK2.8 Enhances Tolerance to Drought, Salt and Low Temperature in Arabidopsis

Drought, salinity and low temperatures are major factors limiting crop productivity and quality. Sucrose non-fermenting1-related protein kinase 2 (SnRK2) plays a key role in abiotic stress signaling in plants. In this study, TaSnRK2.8, a SnRK2 member in wheat, was cloned and its functions under multi-stress conditions were characterized. Subcellular localization showed the presence of TaSnRK2.8 in the cell membrane, cytoplasm and nucleus. Expression pattern analyses in wheat revealed that TaSnRK2.8 was involved in response to PEG, NaCl and cold stresses, and possibly participates in ABA-dependent signal transduction pathways. To investigate its role under various environmental stresses, TaSnRK2.8 was transferred to Arabidopsis under control of the CaMV-35S promoter. Overexpression of TaSnRK2.8 resulted in enhanced tolerance to drought, salt and cold stresses, further confirmed by longer primary roots and various physiological characteristics, including higher relative water content, strengthened cell membrane stability, significantly lower osmotic potential, more chlorophyll content, and enhanced PSII activity. Meanwhile, TaSnRK2.8 plants had significantly lower total soluble sugar levels under normal growing conditions, suggesting that TaSnRK2.8 might be involved in carbohydrate metabolism. Moreover, the transcript levels of ABA biosynthesis (ABA1, ABA2), ABA signaling (ABI3, ABI4, ABI5), stress-responsive genes, including two ABA-dependent genes (RD20A, RD29B) and three ABA-independent genes (CBF1, CBF2, CBF3), were generally higher in TaSnRK2.8 plants than in WT/GFP controls under normal/stress conditions. Our results suggest that TaSnRK2.8 may act as a regulatory factor involved in a multiple stress response pathways

Predicting disease-associated substitution of a single amino acid by analyzing residue interactions

<p>Abstract</p> <p>Background</p> <p>The rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues.</p> <p>Results</p> <p>We found that SAPs can be well characterized by network topological features. Mutations are probably disease-associated when they occur at a site with a high centrality value and/or high degree value in a protein structure network. We also discovered that study of the neighboring residues around a mutation site can help to determine whether the mutation is disease-related or not. We compiled a dataset from the Swiss-Prot variant pages and constructed a model to predict disease-associated SAPs based on the random forest algorithm. The values of total accuracy and MCC were 83.0% and 0.64, respectively, as determined by 5-fold cross-validation. With an independent dataset, our model achieved a total accuracy of 80.8% and MCC of 0.59, respectively.</p> <p>Conclusions</p> <p>The satisfactory performance suggests that network topological features can be used as quantification measures to determine the importance of a site on a protein, and this approach can complement existing methods for prediction of disease-associated SAPs. Moreover, the use of this method in SAP studies would help to determine the underlying linkage between SAPs and diseases through extensive investigation of mutual interactions between residues.</p

Role of Fractalkine/CX3CR1 Interaction in Light-Induced Photoreceptor Degeneration through Regulating Retinal Microglial Activation and Migration

Background: Excessive exposure to light enhances the progression and severity of some human retinal degenerative diseases. While retinal microglia are likely to be important in neuron damage associated with these diseases, the relationship between photoreceptor damage and microglial activation remains poorly understood. Some recent studies have indicated that the chemokine fractalkine is involved in the pathogenesis of many neurodegenerative diseases. The present study was performed to investigate the cross-talk between injured photoreceptors and activated retinal microglia, focusing on the role of fractalkine and its receptor CX3CR1 in light-induced photoreceptor degeneration. Methodology/Principal Findings: Both in vivo and in vitro experiments were involved in the research. In vivo, Sprague– Dawley rats were exposed to blue light for 24 hours. In vitro, the co-culture of primary retinal microglia and a photoreceptor cell line (661W cell) was exposed to blue light for five hours. Some cultures were pretreated by the addition of anti-CX3CR1 neutralizing antibody or recombinant fractalkine. Expression of fractalkine/CX3CR1 and inflammatory cytokines was detected by immunofluorescence, real-time PCR, Western immunoblot analysis, and ELISA assay. TUNEL method was used to detect cell apoptosis. In addition, chemotaxis assay was performed to evaluate the impact of soluble fractalkine on microglial migration. Our results showed that the expression of fractalkine that was significantly upregulated after exposure to light, located mainly at the photoreceptors. The extent of photoreceptor degeneration and microglial migratio

Bioinformatics in China: A Personal Perspective

Biochemical Research MethodsMathematical &amp; Computational BiologySCI(E)PubMed3EDITORIAL MATERIAL4e1000020