56 research outputs found

    Transcriptome-Wide Analysis of UTRs in Non-Small Cell Lung Cancer Reveals Cancer-Related Genes with SNV-Induced Changes on RNA Secondary Structure and miRNA Target Sites

    Get PDF
    <div><p>Traditional mutation assessment methods generally focus on predicting disruptive changes in protein-coding regions rather than non-coding regulatory regions like untranslated regions (UTRs) of mRNAs. The UTRs, however, are known to have many sequence and structural motifs that can regulate translational and transcriptional efficiency and stability of mRNAs through interaction with RNA-binding proteins and other non-coding RNAs like microRNAs (miRNAs). In a recent study, transcriptomes of tumor cells harboring mutant and wild-type <i>KRAS</i> (V-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog) genes in patients with non-small cell lung cancer (NSCLC) have been sequenced to identify single nucleotide variations (SNVs). About 40% of the total SNVs (73,717) identified were mapped to UTRs, but omitted in the previous analysis. To meet this obvious demand for analysis of the UTRs, we designed a comprehensive pipeline to predict the effect of SNVs on two major regulatory elements, secondary structure and miRNA target sites. Out of 29,290 SNVs in 6462 genes, we predict 472 SNVs (in 408 genes) affecting local RNA secondary structure, 490 SNVs (in 447 genes) affecting miRNA target sites and 48 that do both. Together these disruptive SNVs were present in 803 different genes, out of which 188 (23.4%) were previously known to be cancer-associated. Notably, this ratio is significantly higher (one-sided Fisher's exact test p-value = 0.032) than the ratio (20.8%) of known cancer-associated genes (n = 1347) in our initial data set (n = 6462). Network analysis shows that the genes harboring disruptive SNVs were involved in molecular mechanisms of cancer, and the signaling pathways of LPS-stimulated MAPK, IL-6, iNOS, EIF2 and mTOR. In conclusion, we have found hundreds of SNVs which are highly disruptive with respect to changes in the secondary structure and miRNA target sites within UTRs. These changes hold the potential to alter the expression of known cancer genes or genes linked to cancer-associated pathways.</p></div

    Results of SNV U1552G predicted to cause significant local secondary structure changes in 3′ UTR of GPX3 mRNA.

    No full text
    <p>The dot plot from RNAsnp web server <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082699#pone.0082699-Sabarinathan2" target="_blank">[67]</a> shows the base pair probabilities corresponds to the local region predicted with significant difference (<i>d<sub>max</sub></i> p-value: 0.0474) between wild-type and mutant. The upper triangle represents the base pair probabilities for the wild-type (green) and the lower triangle for the mutant (red). On the sides, the minimum free energy (MFE) structure of the wild-type and mutants are displayed in planar graphic representation. The SECIS region is highlighted in blue circle and the SNV position is indicated with arrow mark.</p

    Summary of pathway analysis results using Ingenuity pathway analysis software.

    No full text
    <p>The numbers at the end of each cell represent the p-values, but for the top networks it is the p-score (−log<sub>10</sub><i>p-value</i>).</p

    List of target predictions of <i>NCSLC-associated miRNAs</i> derived from the microRNA body map [45].

    No full text
    <p>List of target predictions of <i>NCSLC-associated miRNAs</i> derived from the microRNA body map <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082699#pone.0082699-Mestdagh1" target="_blank">[45]</a>.</p

    Network Analysis of genes predicted to have SNVs' effect on UTRs.

    No full text
    <p>The networks represent the interaction between genes that were predicted to have SNVs' effect on UTRs from miRNA and RNAsnp analysis (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082699#pone-0082699-t007" target="_blank">Table 7</a>, column 5). The gene nodes were colored to differentiate the known (orange) and unknown (green) cancer-associated genes, and the color outside the node indicates whether the gene comes from miRNA (yellow) or RNAsnp (blue) or both.</p

    List of genes which have more than one disruptive SNV (combined high-confidence and medium-confidence candidates) in the UTRs.

    No full text
    <p><sup>a</sup> SNVs that were predicted by both d<sub>max</sub> and r<sub>min</sub> measures are highlighted with †.</p><p><sup>b</sup> The p-value corresponding to the r<sub>min</sub> measure is highlighted with *.</p><p><sup>c</sup> The conserved RNA secondary structure predicted by CMfinder and RNAz program (through our in-house pipeline <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082699#pone.0082699-Seemann1" target="_blank">[39]</a>) are highlighted with the symbols <sup>#</sup> and <sup>$</sup>, respectively.</p

    Effect of particle gradation on the properties of Mg(OH)<sub>2</sub> slurry: viscosity, stability, and rheology

    No full text
    In this article, multipeak Mg(OH)2 slurries, that is, unimodal, bimodal, and trimodal, were prepared by blending Mg(OH)2 of distinct particle sizes (d50 of 1, 3, 7, 10, and 20 μm). The effects of particle gradation on the properties of Mg(OH)2 slurry, such as viscosity, stability, and rheological behavior, were investigated. Also, the packing efficiency was analyzed by the compartment packing model. The results revealed that viscosity and stability decrease with particle size or larger particle mixing in unimodal and bimodal schemes. However, trimodal slurry viscosity did not significantly change with particle size ratio. The packing efficiency calculated by the compartment packing model has the opposite trend of viscosity, but this trend is not so strictly consistent in the three-peak gradation scheme. Among the unimodal, bimodal, and trimodal slurries with better viscosity and stability (10, 3 + 10 (3:7), and 1 + 7 + 10 μm (3:1:6)), the trimodal slurry had the lowest viscosity and the highest stability. Its highest yield stress (4.66 ± 0.23 Pa) and flow stress (7.67 ± 0.38 Pa) indicated its structural stability, and it showed good structural recovery capability, reestablishing about 87% in 60 seconds. This might be explained by the fine particles forming a bridge between the coarse particles, resulting in a stable and networked structure.</p
    corecore