Sequence composition influences eIF4E responsiveness.

<p>Top row: median fold change of four groups of sequences corresponding to the four possible nucleotides at each position in the alignment around a) cap region (nt 1–20), b) start region (positions −19…20 with position 1 being the first nt of the coding region), c) stop region (positions −19…20 with position 1 being the first nt of the 3′UTR). Blue: A, red: C, green: G, black: U. Bottom row: Negative decadic logarithm of the Kruskal-Wallis test p-value as a function of the sequence position. The statistical test is applied at each alignment column to the fold change values of the four groups mention above. Note that because the Kruskal-Wallis test is not defined for completely conserved alignment columns, the start and stop codon regions are skipped (Figures a)–c)). The eIF4E overexpression data set consisting of 11387 mRNAs was used to generate the plots (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004868#s2" target="_blank">Materials and Methods</a>).</p

A few upregulated mRNAs show positive selection for miRNA binding sites.

<p>List of miRNAs with positive selection (accumulation of binding sites) among 40 highly eIF4E upregulated mRNAs (fold change greater 4.0) and 1200 nonregulated mRNAs (fold change between 1.05 and 1.0/1.05). See caption of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004868#pone-0004868-t005" target="_blank">Table 5</a> for an explanation of table columns.</p

Probability of base pairing is greater for upregulated mRNAs at the regions just upstream of the start codon and flanking the stop codon.

<p>Secondary structure profiles of mRNA regions. Red, black, green: average secondary structure probability for upregulated, un-regulated or downregulated mRNAs respectively; blue: p-value of the Wilcoxon-Mann-Whitney two-sample rank sum test using logarithmic scale shown on the right y-axis. a) start codon (positions −19…20 with position 1 being the first nt of the coding region), b) stop codon (positions −19…20 with position 1 being the first nt of the 3′UTR).The positions of start and stop codon are indicated in red. The used subsets of the eIF4E overexpression data consists of 1835 upregulated mRNAs, 679 downregulated mRNAs and 3814 nonregulated mRNAs (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004868#s2" target="_blank">Material and Methods</a>).</p

The support vector machine shows high correlation for combinations of total length with 3′UTR length and/or G+C content.

<p>Result of support vector machine. Shown is the Spearman correlation coefficient as well as the Matthews correlation coefficient of the predicted fold change versus the actual fold change using different feature combinations. LT: total length; L3: length of 3′UTR region; GC: G+C content.</p

Support vector machine classifier effectively predicts fold change.

<p>Log-log plot of the eIF4E dataset fold change plotted with the corresponding support vector machine classifier results. The used eIF4E overexpression dataset consists of 4000 mRNAs for training and 5629 mRNAs for testing the classifier (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004868#s2" target="_blank">Materials and Methods</a>).</p

Highly upregulated mRNAs show mostly negative selection (site avoidance) for miRNA binding sites.

<p>List of miRNAs with negative selection (avoidance of binding sites) among 40 highly eIF4E upregulated mRNAs (fold change greater 4.0) and 1200 nonregulated mRNAs (fold change between 1.05 and 1.0/1.05). upreg⁺: number of upregulated mRNAs that show positive selection with respect to the specified miRNA, upreg⁻: number of upregulated mRNAs with negative selection, nonreg⁺: number of nonregulated mRNAs with positive selection of miRNA-target binding sites; nonreg⁻: number of nonregulated mRNAs with negative selection. P-values are computed according using a Fisher exact test, all entries with a p-value smaller than 0.02 are listed. Ratio: (upreg⁺/(upreg⁺+upreg⁻))/(nonreg⁺/(nonreg⁺+nonreg⁻)).</p

G+C content shows correlation with polysome shift for total mRNA, coding and 3′UTR but not for 5′UTR sequence.

<p>Table shows G+C content of mRNA (total mRNA or 5′UTR, coding, 3′UTR) as a function of fold change. P-values and correlation coefficients are computed according to the Spearman correlation for the eIF4E overexpression data set (11387 mRNAs) and the AKT activation data set (7496 mRNAs).</p

Confusion matrix of support vector machine for combination of total length with 3′UTR length and G+C content.

<p>Results of the support vector machine (corresponding to first row in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004868#pone-0004868-t003" target="_blank">Table 3</a>) applied as a two-class predictor. The used eIF4E overexpression dataset consists of 4000 mRNAs for training and 5629 mRNAs for testing the classifier (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0004868#s2" target="_blank">Materials and Methods</a>).</p

Length correlates with fold shift for total, coding and 3′UTR but not 5′UTR of target mRNAs.

<p>Table of correlations between length of mRNA (total mRNA, 5′UTR, coding, 3′UTR) and the eIF4E fold change for the eIF4E dataset. P-values and correlation coefficient are computed according to the Spearman correlation for the eIF4E overexpression data set (11387 mRNAs) and the AKT activation data set (7496 mRNAs).</p

Microwave-Based Reaction Screening: Tandem Retro-Diels–Alder/Diels–Alder Cycloadditions of <i>o</i>-Quinol Dimers

We have accomplished a parallel screen of cycloaddition partners for <i>o</i>-quinols utilizing a plate-based microwave system. Microwave irradiation improves the efficiency of retro-Diels–Alder/Diels–Alder cascades of <i>o-</i>quinol dimers which generally proceed in a diastereoselective fashion. Computational studies indicate that asynchronous transition states are favored in Diels–Alder cycloadditions of <i>o</i>-quinols. Subsequent biological evaluation of a collection of cycloadducts has identified an inhibitor of activator protein-1 (AP-1), an oncogenic transcription factor