The X chromosome inactivation network.

<p>(A) <i>Xist</i> expression is controlled by counteracting activators (red) and repressors (blue). Stem cell factors (blue ovals) might repress <i>Xist</i> directly or indirectly via activating the repressive transcript Tsix or repressing the activator Rnf12. Rnf12 is the only known activator, and may function by targeting the <i>Xist</i> promoter directly and/or by inducing degradation of an unknown <i>Xist</i> repressor (blue squares). The existence of additional X-linked activators (red triangles) and long-range control elements such as <i>Xpr</i>, <i>Xce</i>, <i>Xite</i>, and others (red box) has been suggested <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002002#pgen.1002002-Nora1" target="_blank">[10]</a>. (B) The time window when XCI can be initiated (grey) could be controlled by the down-regulation of <i>Xist</i> repressors such as stem cell factors (blue) and up-regulation of <i>Xist</i> activators like Rnf12 (red). (C) Different cell lines might require Rnf12 (ESC line B) or not (ESC line A), depending on the expression kinetics of other X-linked activators (dotted red line).</p

Deep-Sequencing Protocols Influence the Results Obtained in Small-RNA Sequencing

<div><p>Second-generation sequencing is a powerful method for identifying and quantifying small-RNA components of cells. However, little attention has been paid to the effects of the choice of sequencing platform and library preparation protocol on the results obtained. We present a thorough comparison of small-RNA sequencing libraries generated from the same embryonic stem cell lines, using different sequencing platforms, which represent the three major second-generation sequencing technologies, and protocols. We have analysed and compared the expression of microRNAs, as well as populations of small RNAs derived from repetitive elements. Despite the fact that different libraries display a good correlation between sequencing platforms, qualitative and quantitative variations in the results were found, depending on the protocol used. Thus, when comparing libraries from different biological samples, it is strongly recommended to use the same sequencing platform and protocol in order to ensure the biological relevance of the comparisons.</p> </div

Categories of all aligned reads for the libraries investigated.

<p>The feature type of reads overlapping multiple genomic features was assigned by prioritising the feature types in the order: microRNA>other ncRNA>pseudogene>exon>gene>LINE>other repeat.</p

Description of the libraries investigated.

<p>The ten samples differ in size, in the employed sequencing technology, in the version of the machine that they were generated with and whether a barcode or index had been used for parallel sequencing with other libraries.</p

Comparison of miRNA expression levels.

<p>Scatter plots comparing the normalised miRNA expression levels (on a generalised logarithmic scale) between pairs of libraries. The libraries are named as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032724#pone-0032724-t001" target="_blank">Table 1</a>. A. Libraries were generated from the same cell line (E14 ES XY) but using different sequencing platforms (Solexa vs. SOLiDv4). B. Libraries were generated from different cell lines (XY vs. XX), but both using the Solexa sequencing platform. CC: Spearman correlation coefficient.</p

Read length distributions after adapter removal.

<p>The upper panel shows the E14 XY libraries while the lower panel displays the PGK XX libraries. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032724#pone-0032724-t001" target="_blank">Table 1</a> for details about the libraries.</p

Correlation of miRNAs expression.

<p>Heatmap showing the pair-wise Spearman rank correlation between the miRNA read counts of the 10 libraries. The colour key at the bottom indicates which colour represents which correlation coefficient range.</p

Annotation of repeats for the libraries investigated.

<p><b>A.</b> Coverage of all repeats classes (in proportion). <b>B.</b> The three main repeats classes (RNA, rRNA, tRNA) were discarded to highlight the annotation for the other classes. <b>C.</b> Size distribution of reads aligned on all the repeats classes. <b>D.</b> Size distribution of the reads aligned on repeats classes excluding RNA, rRNA and tRNA.</p

Correction: RNAi-Dependent and Independent Control of LINE1 Accumulation and Mobility in Mouse Embryonic Stem Cells

<p>Correction: RNAi-Dependent and Independent Control of LINE1 Accumulation and Mobility in Mouse Embryonic Stem Cells</p

RNAi-Dependent and Independent Control of LINE1 Accumulation and Mobility in Mouse Embryonic Stem Cells

<div><p>In most mouse tissues, long-interspersed elements-1 (L1s) are silenced <i>via</i> methylation of their 5′-untranslated regions (5′-UTR). A gradual loss-of-methylation in pre-implantation embryos coincides with L1 retrotransposition in blastocysts, generating potentially harmful mutations. Here, we show that Dicer- and Ago2-dependent RNAi restricts L1 accumulation and retrotransposition in undifferentiated mouse embryonic stem cells (mESCs), derived from blastocysts. RNAi correlates with production of Dicer-dependent 22-nt small RNAs mapping to overlapping sense/antisense transcripts produced from the L1 5′-UTR. However, RNA-surveillance pathways simultaneously degrade these transcripts and, consequently, confound the anti-L1 RNAi response. In <i>Dicer^−/−</i> mESC complementation experiments involving ectopic Dicer expression, L1 silencing was rescued in cells in which microRNAs remained strongly depleted. Furthermore, these cells proliferated and differentiated normally, unlike their non-complemented counterparts. These results shed new light on L1 biology, uncover defensive, in addition to regulatory roles for RNAi, and raise questions on the differentiation defects of <i>Dicer^−/−</i> mESCs.</p></div