Two novel mutations of Wiskott–Aldrich syndrome: the molecular prediction of interaction between the mutated WASP L101P with WASP-interacting protein by molecular modeling

AbstractWiskott–Aldrich syndrome (WAS) is an X-linked disorder characterized by eczema, thrombocytopenia and increased susceptibility of infections, with mutations of the WAS gene being responsible for WAS and X-linked thrombocytopenia. Herein, two novel mutations of WAS at T336C on exon 3, and at 1326–1329, a G deletion on exon 10, resulting in L101P missense mutation and frameshift mutation 444 stop, respectively, are reported. The affected patients with either mutation showed severe suppression of WAS protein (WASP) levels, T cell proliferation, and CFSE-labeled T cells division. Because WASP L101 have not shown direct nuclear Overhauser effect (NOE) contact with the WASP-interacting protein (WIP) in NMR spectroscopy, molecular modeling was performed to evaluate the molecular effect of WASP P101 to WIP peptide. It is presumed that P101 induced a conformational change in the Q99 residue of WASP and made the side chain of Q99 move away from the WIP peptide, resulting in disruption of the hydrogen bond between Q99 WASP and Y475 WIP. A possible model for the molecular pathogenesis of WAS has been proposed by analyzing the interactions of WASP and WIP using a molecular modeling study

Identification of Novel Reference Genes Using Multiplatform Expression Data and Their Validation for Quantitative Gene Expression Analysis

Normalization of mRNA levels using endogenous reference genes (ERGs) is critical for an accurate comparison of gene expression between different samples. Despite the popularity of traditional ERGs (tERGs) such as GAPDH and ACTB, their expression variability in different tissues or disease status has been reported. Here, we first selected candidate housekeeping genes (HKGs) using human gene expression data from different platforms including EST, SAGE, and microarray, and 13 novel ERGs (nERGs) (ARL8B, CTBP1, CUL1, DIMT1L, FBXW2, GPBP1, LUC7L2, OAZ1, PAPOLA, SPG21, TRIM27, UBQLN1, ZNF207) were further identified from these HKGs. The mean coefficient variation (CV) values of nERGs were significantly lower than those of tERGs and the expression level of most nERGs was relatively lower than high expressing tERGs in all dataset. The higher expression stability and lower expression levels of most nERGs were validated in 108 human samples including formalin-fixed paraffin-embedded (FFPE) tissues, frozen tissues and cell lines, through quantitative real-time RT-PCR (qRT-PCR). Furthermore, the optimal number of nERGs required for accurate normalization was as few as two, while four genes were required when using tERGs in FFPE tissues. Most nERGs identified in this study should be better reference genes than tERGs, based on their higher expression stability and fewer numbers needed for normalization when multiple ERGs are required

The Homocysteine and Metabolic Syndrome: A Mendelian Randomization Study

Homocysteine (Hcy) is well known to be increased in the metabolic syndrome (MetS) incidence. However, it remains unclear whether the relationship is causal or not. Recently, Mendelian Randomization (MR) has been popularly used to assess the causal influence. In this study, we adopted MR to investigate the causal influence of Hcy on MetS in adults using three independent cohorts. We considered one-sample MR and two-sample MR. We analyzed one-sample MR in 5902 individuals (2090 MetS cases and 3812 controls) from the KARE and two-sample MR from the HEXA (676 cases and 3017 controls) and CAVAS (1052 cases and 764 controls) datasets to evaluate whether genetically increased Hcy level influences the risk of MetS. In observation studies, the odds of MetS increased with higher Hcy concentrations (odds ratio (OR) 1.17, 95%CI 1.12–1.22, p < 0.01). One-sample MR was performed using two-stage least-squares regression, with an MTHFR C677T and weighted Hcy generic risk score as an instrument. Two-sample MR was performed with five genetic variants (rs12567136, rs1801133, rs2336377, rs1624230, and rs1836883) by GWAS data as the instrumental variables. For sensitivity analysis, weighted median and MR–Egger regression were used. Using one-sample MR, we found an increased risk of MetS (OR 2.07 per 1-SD Hcy increase). Two-sample MR supported that increased Hcy was significantly associated with increased MetS risk by using the inverse variance weighted (IVW) method (beta 0.723, SE 0.119, and p < 0.001), the weighted median regression method (beta 0.734, SE 0.097, and p < 0.001), and the MR–Egger method (beta 2.073, SE 0.843, and p = 0.014) in meta-analysis. The MR–Egger slope showed no evidence of pleiotropic effects (intercept −0.097, p = 0.107). In conclusion, this study represented the MR approach and elucidates the significant relationship between Hcy and the risk of MetS in the Korean population

Analysis of pharmacogenomic variants associated with population differentiation.

In the present study, we systematically investigated population differentiation of drug-related (DR) genes in order to identify common genetic features underlying population-specific responses to drugs. To do so, we used the International HapMap project release 27 Data and Pharmacogenomics Knowledge Base (PharmGKB) database. First, we compared four measures for assessing population differentiation: the chi-square test, the analysis of variance (ANOVA) F-test, Fst, and Nearest Shrunken Centroid Method (NSCM). Fst showed high sensitivity with stable specificity among varying sample sizes; thus, we selected Fst for determining population differentiation. Second, we divided DR genes from PharmGKB into two groups based on the degree of population differentiation as assessed by Fst: genes with a high level of differentiation (HD gene group) and genes with a low level of differentiation (LD gene group). Last, we conducted a gene ontology (GO) analysis and pathway analysis. Using all genes in the human genome as the background, the GO analysis and pathway analysis of the HD genes identified terms related to cell communication. "Cell communication" and "cell-cell signaling" had the lowest Benjamini-Hochberg's q-values (0.0002 and 0.0006, respectively), and "drug binding" was highly enriched (16.51) despite its relatively high q-value (0.0142). Among the 17 genes related to cell communication identified in the HD gene group, five genes (STX4, PPARD, DCK, GRIK4, and DRD3) contained single nucleotide polymorphisms with Fst values greater than 0.5. Specifically, the Fst values for rs10871454, rs6922548, rs3775289, rs1954787, and rs167771 were 0.682, 0.620, 0.573, 0.531, and 0.510, respectively. In the analysis using DR genes as the background, the HD gene group contained six significant terms. Five were related to reproduction, and one was "Wnt signaling pathway," which has been implicated in cancer. Our analysis suggests that the HD gene group from PharmGKB is associated with cell communication and drug binding

iTurboGraph: Scaling and Automating Incremental Graph Analytics

With the rise of streaming data for dynamic graphs, large-scale graph analytics meets a new requirement of Incremental Computation because the larger the graph, the higher the cost for updating the analytics results by re-execution. A dynamic graph consists of an initial graph G and graph mutation updates ∆G of edge insertions or deletions. Given a query Q, its results Q(G), and updates for ∆G to G, incremental graph analytics computes updates ∆Q such that Q(G ∪ ∆G) = Q(G) ∪ ∆Q where ∪ is a union operator. In this paper, we consider the problem of large-scale incremental neighbor-centric graph analytics (NGA). We solve the limitations of previous systems: lack of usability due to the difficulties in programming incremental algorithms for NGA and limited scalability and efficiency due to the overheads in maintaining intermediate results for graph traversals in NGA. First, we propose a domainspecific language, LN GA, and develop its compiler for intuitive programming of NGA, automatic query incrementalization, and query optimizations. Second, we define Graph Streaming Algebra as a theoretical foundation for scalable processing of incremental NGA. We introduce a concept of Nested Graph Windows and model graph traversals as the generation of walk streams. Lastly, we present a system iTurboGraph, which efficiently processes incremental NGA for large graphs. Comprehensive experiments show that it effectively avoids costly re-executions and efficiently updates the analytics results with reduced IO and computations.1

Comprehensive Metabolomic Search for Biomarkers to Differentiate Early Stage Hepatocellular Carcinoma from Cirrhosis

The established biomarker for hepatocellular carcinoma (HCC), serum α-fetoprotein (AFP), has suboptimal performance in early disease stages. This study aimed to develop a metabolite panel to differentiate early-stage HCC from cirrhosis. Cross-sectional metabolomic analyses of serum samples were performed for 53 and 47 patients with early HCC and cirrhosis, respectively, and 50 matched healthy controls. Results were validated in 82 and 80 patients with early HCC and cirrhosis, respectively. To retain a broad spectrum of metabolites, technically distinct analyses (global metabolomic profiling using gas chromatography time-of-flight mass spectrometry and targeted analyses using liquid chromatography with tandem mass spectrometry) were employed. Multivariate analyses classified distinct metabolites; logistic regression was employed to construct a prediction model for HCC diagnosis. Five metabolites (methionine, proline, ornithine, pimelylcarnitine, and octanoylcarnitine) were selected in a panel. The panel distinguished HCC from cirrhosis and normal controls, with an area under the receiver operating curve (AUC) of 0.82; this was significantly better than that of AFP (AUC: 0.75). During validation, the panel demonstrated significantly better predictability (AUC: 0.94) than did AFP (AUC: 0.78). Defects in ammonia recycling, the urea cycle, and amino acid metabolism, demonstrated on enrichment pathway analysis, may reliably distinguish HCC from cirrhosis. Compared with AFP alone, the metabolite panel substantially improved early-stage HCC detection

Summary of each SNP group.

<p>Summary of each SNP group.</p

Boxplots representing four measures of simulation data with an increase in <i>d</i>.

<p><b>A.</b> Variation of distributions due to increase in sample sizes (Case I). <b>B.</b> Variation of distributions due to bias of sample sizes (Case II). For both cases, the <i>x</i>-axis denotes the distance <i>d</i>, and the <i>y</i>-axis and denotes the following measures: -log₁₀<i>Pvalue</i> for chi-square test and ANOVA F-test; Weir and Cockerham’s F_st estimates for F_st; </p><p></p><p></p><p></p><p></p><p><mo>∑</mo></p><mo>​</mo><p></p><p><mi>d</mi><mi>i</mi><mn>2</mn></p><p></p><p></p><p></p> for NSCM.<p></p

Specificities (%) of each measure from simulation data under H₀:<i>d</i> = 0 due to an increase in sample size (Scenario I).

<p>The chi-square test and ANOVA F-test are similar, and F_st and SS_d from NSCM are nearly identical. Blue line: chi-square test; red line: F_st; black dotted line: ANOVA F-test; green dotted line: SS_d from NSCM.</p

Histogram of sample sizes from 654 drug-related SNPs.

<p><b>A</b>. Total sample sizes of SNPs. <b>B</b>. Sample size of each population of SNPs. CHB and JPT are plotted separately according to the format of the original HapMap Data. SNPs with larger sample sizes are included in Phase III, and SNPs with smaller sample sizes are included in Phase II.</p