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Previous studies have demonstrated the genetic correlations between type 2 diabetes, obesity and dyslipidemia, and indicated that many genes have pleiotropic effects on them. However, these pleiotropic genes have not been well-defined. It is essential to identify pleiotropic genes using systematic approaches because systematically analyzing correlated traits is an effective way to enhance their statistical power. To identify potential pleiotropic genes for these three disorders, we performed a systematic analysis by incorporating GWAS (genome-wide associated study) datasets of six correlated traits related to type 2 diabetes, obesity and dyslipidemia using Meta-CCA (meta-analysis using canonical correlation analysis). Meta-CCA is an emerging method to systematically identify potential pleiotropic genes using GWAS summary statistics of multiple correlated traits. 2,720 genes were identified as significant genes after multiple testing (Bonferroni corrected p value < 0.05). Further, to refine the identified genes, we tested their relationship to the six correlated traits using VEGAS-2 (versatile gene-based association study-2). Only the genes significantly associated (Bonferroni corrected p value < 0.05) with more than one trait were kept. Finally, 25 genes (including two confirmed pleiotropic genes and eleven novel pleiotropic genes) were identified as potential pleiotropic genes. They were enriched in 5 pathways including the statin pathway and the PPAR (peroxisome proliferator-activated receptor) Alpha pathway. In summary, our study identified potential pleiotropic genes and pathways of type 2 diabetes, obesity and dyslipidemia, which may shed light on the common biological etiology and pathogenesis of these three disorders and provide promising insights for new therapies.

Background and aim Many studies have reported that genetic variants correlate with higher risk for coronary artery disease (CAD) or in-stent restenosis (ISR) after bare metal stent (BMS) implantation. However, there is limited data assessing the impact of these variants on ISR in patients treated with drug-eluting stent (DES). The purpose of this study was to investigate the effects of genetic risk factors on ISR in Chinese Han patients treated with DES. Methods A total of 425 patients with a diagnosis of CAD who underwent successful revascularization in native coronary arteries with DES were included in this retrospective study. Genotyping was performed on six single nucleotide polymorphisms (SNPs) in the endothelial nitric oxide synthase gene (eNOS), the angiotensin converting enzyme gene (ACE), the angiotensin II type 1 receptor gene (AT1R), the transforming growth factor beta gene (TGF-β), and the vascular endothelial growth factor gene (VEGF). Quantitative coronary angiography (QCA) was performed during the follow-up period to detect ISR. Logistic regression models were used to test for association. Results Fifty-four patients (12.7%) developed ISR during the follow-up period. Of the six analyzed SNPs, the frequency of the C allele of T786C polymorphism in eNOS was significantly higher in the ISR group (22.2%) compared to the non-ISR group (12.7%) (p

CIP2A, cancerous inhibitor of protein phosphatase 2A, was initially recognized as an oncoprotein. Recently several studies revealed that CIP2A could function as a prognosis biomarker, however, the result remained not comprehensive, partly due to small number of patients included individually. Here we carried out a meta-analysis of published studies to assess the prognostic significance of CIP2A in solid tumors. All eligible studies were identified through searching PubMed, Embase and Web of Science database. In this meta-analysis, 22 studies involving 4,579 participants were included, and we verified that CIP2A over-expression was significantly related with poor overall survival (pooled HR = 1.844, 95% CI = 1.528-2.225, P

[This corrects the article DOI: 10.1371/journal.pone.0182866.].

There are co-morbidity between osteoporosis (OP) and rheumatoid arthritis (RA). Some genetic risk factors have been identified for these two phenotypes respectively in previous research; however, they accounted for only a small portion of the underlying total genetic variances. Here, we sought to identify additional common genetic loci associated with OP and/or RA. The conditional false discovery rate (cFDR) approach allows detection of additional genetic factors (those respective ones as well as common pleiotropic ones) for the two associated phenotypes. We collected and analyzed summary statistics provided by large, multi-center GWAS studies of FNK (femoral neck) BMD (a major risk factor for osteoporosis) (n = 53,236) and RA (n = 80,799). The conditional quantile-quantile (Q-Q) plots can assess the enrichment of SNPs related to FNK BMD and RA, respectively. Furthermore, we identified shared loci between FNK BMD and RA using conjunction cFDR (ccFDR). We found strong enrichment of p-values in FNK BMD when conditional Q-Q was done on RA and vice versa. We identified 30 novel OP-RA associated pleiotropic loci that have not been reported in previous OP or RA GWAS, 18 of which located in the MHC (major histocompatibility complex) region previously reported to play an important role in immune system and bone health. We identified some specific novel polygenic factors for OP and RA respectively, and identified 30 novel OP-RA associated pleiotropic loci. These discovery findings may offer novel pathobiological insights, and suggest new targets and pathways for drug development in OP and RA patients.

In this paper, based on our previous multi-pattern uniform resource locator (URL) binary-matching algorithm called HEM, we propose an improved multi-pattern matching algorithm called MH that is based on hash tables and binary tables. The MH algorithm can be applied to the fields of network security, data analysis, load balancing, cloud robotic communications, and so on-all of which require string matching from a fixed starting position. Our approach effectively solves the performance problems of the classical multi-pattern matching algorithms. This paper explores ways to improve string matching performance under the HTTP protocol by using a hash method combined with a binary method that transforms the symbol-space matching problem into a digital-space numerical-size comparison and hashing problem. The MH approach has a fast matching speed, requires little memory, performs better than both the classical algorithms and HEM for matching fields in an HTTP stream, and it has great promise for use in real-world applications.

Background The zinc finger protein A20 is an important negative regulator of inflammation; polymorphisms in the corresponding gene, TNFAIP3, have been reported to be associated with several inflammation diseases. However, only a few studies have focused on the relationship between TNFAIP3 polymorphisms and acute pancreatitis (AP). Methods We enrolled 201 healthy controls and 190 acute pancreatitis patients (including 47 systemic inflammatory response syndrome patients) for this study and used DNA sequencing to investigate polymorphisms in the TNFAIP3 promoter. The functional effects of these variants on transcriptional activity, A20 expression, NF-κB activity, and TNF-α and IL-1β levels, after in vitro lipopolysaccharide stimulation, were assessed. Results Two SNPs (rs59693083 and rs5029924) in the TNFAIP3 promoter were selected based on bioinformatic analysis. Neither of these SNPs was associated with susceptibility to AP; however, acute pancreatitis patients who possessed the T allele of rs5029924 were more likely to experience systemic inflammatory response syndrome. Moreover, rs5029924 was found to affect TNFAIP3 promoter activity. After lipopolysaccharide stimulation, the expression of A20 protein significantly decreased, while the activity of NF-κB and the production of TNF-α and IL-1β significantly increased in whole blood leukocytes from subjects with the T allele. Conclusion The rs5029924 polymorphism in the TNFAIP3 promoter may alter the risk of systemic inflammatory response syndrome in acute pancreatitis patients by influencing the expression of A20 protein.

This paper mainly utilizes likelihood-based tests to detect rare variants associated with a continuous phenotype under the framework of kernel machine learning. Both the likelihood ratio test (LRT) and the restricted likelihood ratio test (ReLRT) are investigated. The relationship between the kernel machine learning and the mixed effects model is discussed. By using the eigenvalue representation of LRT and ReLRT, their exact finite sample distributions are obtained in a simulation manner. Numerical studies are performed to evaluate the performance of the proposed approaches under the contexts of standard mixed effects model and kernel machine learning. The results have shown that the LRT and ReLRT can control the type I error correctly at the given α level. The LRT and ReLRT consistently outperform the SKAT, regardless of the sample size and the proportion of the negative causal rare variants, and suffer from fewer power reductions compared to the SKAT when both positive and negative effects of rare variants are present. The LRT and ReLRT performed under the context of kernel machine learning have slightly higher powers than those performed under the context of standard mixed effects model. We use the Genetic Analysis Workshop 17 exome sequencing SNP data as an illustrative example. Some interesting results are observed from the analysis. Finally, we give the discussion.

OBJECTIVE: To establish equations for the estimation of glomerular filtration rates (eGFRs) based on serum creatinine (SCr) and/or serum cystatin C (SCysC) in Chinese patients with chronic kidney disease (CKD), and to compare the new equations with both the reference GFR (rGFR) and the literature equations to evaluate their applicability. METHODS: The 788 Chinese CKD patients were randomly divided into two groups, the training group and the testing group, to establish new eGFR-formulas based on serum CysC and to validate the established formulas, respectively. (99m)Tc-DTPA clearance (as the rGFR), serum Cr, and serum CysC were determined for all patients, and GFR was calculated using the Cockcroft-Gault equation (eGFR1), the MDRD formula (eGFR2), the CKD-EPI formulas (eGFR3, eGFR4), and the Chinese eGFR Investigation Collaboration formulas (eGFR5, eGFR6). The accuracy of each eGFR was compared with the rGFR. RESULTS: The training and testing groups' mean GFRs were 50.84±31.36 mL/min/1.73 m(2) and 54.16±29.45 mL/min/1.73 m(2), respectively. The two newly developed eGFR formulas were fitted using iterative computation: [Formula: see text] and [Formula: see text]. Significant correlation was observed between each eGFR and the rGFR. However, proportional errors and constant errors were observed between rGFR and eGFR1, eGFR2, eGFR4, eGFR5 or eGFR6, and constant errors were observed between eGFR3 and rGFR, as revealed by the Passing & Bablok plot analysis. The Bland-Altman analysis illustrated that the 95% limits of agreement of all equations exceeded the previously accepted limits of

A novel approach to reveal intramolecular signal transduction network is proposed in this work. To this end, a new algorithm of network construction is developed, which is based on a new protein dynamics model of energy dissipation. A key feature of this approach is that direction information is specified after inferring protein residue-residue interaction network involved in the process of signal transduction. This enables fundamental analysis of the regulation hierarchy and identification of regulation hubs of the signaling network. A well-studied allosteric enzyme, E. coli aspartokinase III, is used as a model system to demonstrate the new method. Comparison with experimental results shows that the new approach is able to predict all the sites that have been experimentally proved to desensitize allosteric regulation of the enzyme. In addition, the signal transduction network shows a clear preference for specific structural regions, secondary structural types and residue conservation. Occurrence of super-hubs in the network indicates that allosteric regulation tends to gather residues with high connection ability to collectively facilitate the signaling process. Furthermore, a new parameter of propagation coefficient is defined to determine the propagation capability of residues within a signal transduction network. In conclusion, the new approach is useful for fundamental understanding of the process of intramolecular signal transduction and thus has significant impact on rational design of novel allosteric proteins. © 2012 Ma et al.

Background Rheumatoid arthritis (RA) is an inflammatory articular disease with cartilage and bone damage due to hyperplasic synoviocyte invasion and subsequent matrix protease digestion. Although monoclonal antibodies against tumor necrosis factor alpha (TNFα) have been approved for clinical use in patients with RA, desired therapeutic regimens suitable for non-responders are still unavailable because etiological initiators leading to RA remain enigmatic and unidentified. Methodology/Principal Findings Bacteria-induced arthritis (BIA) that simulates collagen-induced arthritis (CIA) is developed in mice upon daily live bacterial feeding. The morphological lesions of paw erythema and edema together with the histological alterations of synovial hyperplasia and lymphocytic infiltration emerge as the early-phase manifestations of BIA and CIA. Bacteria- or collagen-mediated global upregulation of pro-inflammatory cytokines is accompanied by the burst of nitric oxide (NO). Elevation of the serum NO level is correlated with decline of the blood oxygen saturation percentage (SpO2), reflecting a hypoxic consequence during development towards arthritis. NO-driven hypoxia is further evident from a positive relationship between NO and lactic acid (LA), an end product from glycolysis. Upregulation of hypoxia inducible factor 1 alpha (HIF-1α) and vascular endothelial growth factor (VEGF) validates hypoxia-induced angiogenesis in the inflamed synovium of modeling mice. Administration of the NO donor compound sodium nitroprusside (SNP) causes articular inflammation by inducing synovial hypoxia. Anti-bacteria by the antibiotic cefotaxime and/or the immunosuppressant rapamycin or artesunate that also inhibits nitric oxide synthase (NOS) can abrogate NO production, mitigate hypoxia, and considerably ameliorate or even completely abort synovitis, hence highlighting that NO may serve as an initiator of inflammatory arthritis. Conclusions/Significance Like collagen, bacteria also enable synovial lesions via upregulating pro-inflammatory cytokines, triggering NO production, driving hypoxic responses, and inducing synovial angiogenesis and hyperplasia, suggesting that sustained infection might be, in part, responsible for the onset of synovitis and arthritis in mice.

In the present study, we aim to elucidate the roles of caveolin-1(Cav-1), a 22 kDa protein in plasma membrane invaginations, in modulating neuronal differentiation of neural progenitor cells (NPCs). In the hippocampal dentate gyrus, we found that Cav-1 knockout mice revealed remarkably higher levels of vascular endothelial growth factor (VEGF) and the more abundant formation of newborn neurons than wild type mice. We then studied the potential mechanisms of Cav-1 in modulating VEGF signaling and neuronal differentiation in isolated cultured NPCs under normoxic and hypoxic conditions. Hypoxic embryonic rat NPCs were exposed to 1% O 2 for 24 h and then switched to 21% O 2 for 1, 3, 7 and 14 days whereas normoxic NPCs were continuously cultured with 21% O 2. Compared with normoxic NPCs, hypoxic NPCs had down-regulated expression of Cav-1 and up-regulated VEGF expression and p44/42MAPK phosphorylation, and enhanced neuronal differentiation. We further studied the roles of Cav-1 in inhibiting neuronal differentiation by using Cav-1 scaffolding domain peptide and Cav-1-specific small interfering RNA. In both normoxic and hypoxic NPCs, Cav-1 peptide markedly down-regulated the expressions of VEGF and flk1, decreased the phosphorylations of p44/42MAPK, Akt and Stat3, and inhibited neuronal differentiation, whereas the knockdown of Cav-1 promoted the expression of VEGF, phosphorylations of p44/42MAPK, Akt and Stat3, and stimulated neuronal differentiation. Moreover, the enhanced phosphorylations of p44/42MAPK, Akt and Stat3, and neuronal differentiation were abolished by co-treatment of VEGF inhibitor V1. These results provide strong evidence to prove that Cav-1 can inhibit neuronal differentiation via down-regulations of VEGF, p44/42MAPK, Akt and Stat3 signaling pathways, and that VEGF signaling is a crucial target of Cav-1. The hypoxia-induced down-regulation of Cav-1 contributes to enhanced neuronal differentiation in NPCs. © 2011 Li et al., published_or_final_version

AIMS: To develop a risk assessment model for persons at risk from type 2 diabetes in Chinese. MATERIALS AND METHODS: The model was generated from the cross-sectional data of 16246 persons aged from 20 years old and over. C4.5 algorithm and multivariate logistic regression were used for variable selection. Relative risk value combined with expert decision constructed a comprehensive risk assessment for evaluating the individual risk category. The validity of the model was tested by cross validation and a survey performed six years later with some participants. RESULTS: Nine variables were selected as risk variables. A mathematical model was established to calculate the average probability of diabetes in each cluster's group divided by sex and age. A series of criteria combined with relative RR value (2.2) and level of risk variables stratified individuals into four risk groups (non, low, medium and high risk). The overall accuracy reached 90.99% evaluated by cross-validation inside the model population. The incidence of diabetes for each risk group increased from 1.5 (non-risk group) to 28.2(high-risk group) per one thousand persons per year with six years follow-up. DISCUSSION: The model could determine the individual risk for type 2 diabetes by four risk degrees. This model could be used as a technique tool not only to support screening persons at different risk, but also to evaluate the result of the intervention.

DNase I hypersensitive (DHS) sites are important for understanding cis regulation of gene expression. However, existing methods for detecting DHS sites in small numbers of cells can lead to ambiguous results. Here we describe a simple new method, in which DNA fragments with ends generated by DNase I digestion are isolated and used as templates for two PCR reactions. In the first PCR, primers are derived from sequences up- and down-stream of the DHS site. If the DHS site exists in the cells, the first PCR will not produce PCR products due to the cuts of the templates by DNase I between the primer sequences. In the second PCR, one primer is derived from sequence outside the DHS site and the other from the adaptor. This will produce a smear of PCR products of different sizes due to cuts by DNase I at different positions at the DHS site. With this design, we detected a DHS site at the CD4 gene in two CD4 T cell populations using as few as 2×10(4) cells. We further validated this method by detecting a DHS site of the IL-4 gene that is specifically present in type 2 but not type 1 T helper cells. Overall, this method overcomes the interference by genomic DNA not cut by DNase I at the DHS site, thereby offering unambiguous detection of DHS sites in the cells.

Protein dynamics is essential for its function, especially for intramolecular signal transduction. In this work we propose a new concept, energy dissipation model, to systematically reveal protein dynamics upon effector binding and energy perturbation. The concept is applied to better understand the intramolecular signal transduction during allostery of enzymes. The E. coli allosteric enzyme, aspartokinase III, is used as a model system and special molecular dynamics simulations are designed and carried out. Computational results indicate that the number of residues affected by external energy perturbation (i.e. caused by a ligand binding) during the energy dissipation process shows a sigmoid pattern. Using the two-state Boltzmann equation, we define two parameters, the half response time and the dissipation rate constant, which can be used to well characterize the energy dissipation process. For the allostery of aspartokinase III, the residue response time indicates that besides the ACT2 signal transduction pathway, there is another pathway between the regulatory site and the catalytic site, which is suggested to be the β15-αK loop of ACT1. We further introduce the term "protein dynamical modules" based on the residue response time. Different from the protein structural modules which merely provide information about the structural stability of proteins, protein dynamical modules could reveal protein characteristics from the perspective of dynamics. Finally, the energy dissipation model is applied to investigate E. coli aspartokinase III mutations to better understand the desensitization of product feedback inhibition via allostery. In conclusion, the new concept proposed in this paper gives a novel holistic view of protein dynamics, a key question in biology with high impacts for both biotechnology and biomedicine.