Alterations to glycan and glycogene expression during embryogenesis.

<p>(A) Lectin microarray data were mean-normalized and analyzed by Student's <i>t</i>-test. Nine lectins with significantly different signals (<i>p</i><0.01) between the early-stage group (2-cell stage, 16-cell stage, stage 8, and stage 9) and the late-stage group (stages 15–40) were categorized into 5 groups based on the glycan-binding specificities of lectins (indicated above). <i>t</i>-Values of lectins with higher signals in the early-stage group than in the late-stage group (indicated as Early>Late) are displayed as positive values, and <i>t</i>-values of lectins with lower signals in the early-stage group (indicated in EarlyTable S1. (B) Changes in the expression of glycogenes related to lectins with significantly different signals in early- and late-stage groups. Fold changes in 11 DNA microarray probes for glycogenes and glycans that may be regulated by these glycogenes are shown. All gene data are deposited in the GEO database as GSE40620. (C–J) Temporal changes in lectin signals and glycogene expression during embryogenesis. Normalized intensity values of glycogene probes are represented as lines in the primary Y-axis (left side). Mean-normalized signal intensities of lectins are represented as bars in the secondary Y-axis (right side). All lectin data are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056581#pone.0056581.s003" target="_blank">Table S3</a>. (C) <i>fut1</i> and rGC2, (D) <i>abo</i> and rGC2, (E) rDiscoidin I, (F) UDA, (G) <i>mgat1</i> and NPA, (H) <i>galnt3</i> and HPA, (I) <i>galnt7</i> and HPA, (J) <i>galnt11</i> and HPA.</p

Synthesis of Chiral Tritylpyrrolidine Derivatives and Their Application to Asymmetric Benzoyloxylation

An efficient synthesis of novel chiral tritylpyrrolidine derivatives has been developed. Single stereoisomers of various tritylpyrrolidine derivatives can be readily obtained through diastereomer separation by simple silica gel column chromatography. Representative compounds of this class have been shown to be efficient amine organocatalysts for asymmetric benzoyloxylation

A Lectin-Based Glycomic Approach to Identify Characteristic Features of <em>Xenopus</em> Embryogenesis

<div><p>Cell surface glycans show dynamic changes during cell differentiation. Several glycans are useful biomarkers of tumors, stem cells, and embryogenesis. Glycomic studies have been performed using liquid chromatography and mass spectrometry, which are powerful tools for glycan structural analysis but are difficult to use for small sample sizes. Recently, a lectin microarray system was developed for profiling cell surface glycome changes to terminal carbohydrate chains and branch types, using sample sizes of a few micrograms. In this study, we used the lectin microarray system for the first time to investigate stage-specific glycomes in <em>Xenopus laevis</em> embryos. Unsupervised cluster analysis of lectin microarray data indicated that glycan profiles changed sequentially during development. Nine lectin probes showed significantly different signals between early and the late-stage embryos: 4 showed higher signals in the early stages, and 5 exhibited higher signals in the late stages. The gene expression profiles of relevant glycosyltransferase genes support the lectin microarray data. Therefore, we have shown that lectin microarray is an effective tool for high-throughput glycan analysis in <em>Xenopus</em> embryogenesis, allowing glycan profiling of early embryos and small biopsy specimens.</p> </div

Global comparisons of total and glycogene transcriptomes during <i>Xenopus</i> embryogenesis.

<p>Heat map of the correlation coefficient matrix. The color of each square indicates the difference at a specified developmental stage. By definition, diagonal squares have zero divergence. Color scales indicate the correlation coefficient. (A) Global genes; (B) “<i>Xenopus</i> glycogenes.” Egg, unfertilized egg; st., stage.</p

Lectin microarray data of <i>Xenopus</i> embryogenesis.

<p>(A–D) Lectin microarray data for the 4 indicated lectins, DBAI (A), ASA (B), GNA (C), and DBA (D). Mean-normalized signal intensities of lectins are shown. All lectin data are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056581#pone.0056581.s003" target="_blank">Table S3</a>. (E) A summary of 96 lectin signals. Unsupervised hierarchical cluster was generated using mean-normalized and log-transformed signal intensities. Levels of lectin signaling are indicated by a color change from blue (low) to yellow (high). Egg, unfertilized egg; st., stage.</p

Additional file 1: Figure S1. of A prediction model for the grade of liver fibrosis using magnetic resonance elastography

ROC analysis for fibrosis score in relation to fibrosis grade. (a) Fibrosis grade I vs II/III. The AUC of the ROC was 0.930. (b) Fibrosis grade I/II vs III. The AUC of the ROC was 0.925. (PPTX 64 kb

SNP profiling discriminates histology-related subgroups based on chromosomal instability status.

<p>Chromosomal instability status (CIN) according to the number of allele-specific copy number alterations (CNAs) and copy number neutral loss of heterozygosity (CNN LOH), using a human mapping 250K single nucleotide polymorphism (SNP) array with paired tumor DNA and normal DNA. CNAs were divided into three subgroups: CIN-high (≥9 arms with CNAs), CIN-low (1–8 arms with CNAs), and CIN-negative (0 CNAs). (A) Details of number of chromosomal arms with CNAs in each tumor of three histological subtypes (serous carcinomas, SC; clear cell carcinomas, CCC; endometrioid carcinomas, EC). Stage I/II and stage III/IV are colored differently. (B) Correlation between CIN status and histological subtypes. (C) Overview of CNAs by running SNP arrays with 57 ovarian cancer samples. Hierarchical clustering based on the Euclidean distance for dissimilarities is shown. The type A cluster includes tumors with a broad range and low frequency of CNAs, whereas the type B cluster includes tumors with a focal range and high frequency of CNAs. C, E, and S indicate clear cell carcinoma, endometrioid carcinoma, and serous carcinoma, respectively.</p

Integrated analyses reveal a poor-prognostic clear cell signature associated with high chromosomal instability.

<p>Comparison of clear cell carcinoma (CCC) clusters CCC-1 and CCC-2 by gene expression profiling. (A) <i>UGT1A6</i> and <i>UGT1A10</i> were significantly upregulated in CCC-1 compared with CCC-2. (B) The cluster of genes upregulated in CCC-1 compared with CCC-2. The cluster includes <i>STAT3</i> and <i>HIF2A</i>.</p

Clustering by expression arrays and clinicopathological characteristics.

<p>Sensitive*: Complete response or Partial response.</p><p>Resistant**: Stable disease or Progressive disease.</p><p>p-value (by Fisher's exact test).</p><p>Clustering by expression arrays and clinicopathological characteristics.</p

Correlation between clustering by expression arrays, genetic alterations and clinicopathological characteristics in CCC.

<p>Sensitive*: Complete response or Partial response.</p><p>Resistant**: Stable disease or Progressive disease.</p><p>Correlation between clustering by expression arrays, genetic alterations and clinicopathological characteristics in CCC.</p