Chromosomal Rearrangements and Chromothripsis: The Alternative End Generation Model

Chromothripsis defines a genetic phenomenon where up to hundreds of clustered chromosomal rearrangements can arise in a single catastrophic event. The phenomenon is associated with cancer and congenital diseases. Most current models on the origin of chromothripsis suggest that prior to chromatin reshuffling numerous DNA double-strand breaks (DSBs) have to exist, i.e., chromosomal shattering precedes rearrangements. However, the preference of a DNA end to rearrange in a proximal accessible region led us to propose chromothripsis as the reaction product of successive chromatin rearrangements. We previously coined this process Alternative End Generation (AEG), where a single DSB with a repair-blocking end initiates a domino effect of rearrangements. Accordingly, chromothripsis is the end product of this domino reaction taking place in a single catastrophic event

Differential Programming of B Cells in AID Deficient Mice

<div><p>The <i>Aicda</i> locus encodes the activation induced cytidine deaminase (AID) and is highly expressed in germinal center (GC) B cells to initiate somatic hypermutation (SHM) and class switch recombination (CSR) of immunoglobulin (Ig) genes. Besides these Ig specific activities in B cells, AID has been implicated in active DNA demethylation in non-B cell systems. We here determined a potential role of AID as an epigenetic eraser and transcriptional regulator in B cells. RNA-Seq on different B cell subsets revealed that <i>Aicda^−/−</i> B cells are developmentally affected. However as shown by RNA-Seq, MethylCap-Seq, and SNP analysis these transcriptome alterations may not relate to AID, but alternatively to a CBA mouse strain derived region around the targeted <i>Aicda</i> locus. These unexpected confounding parameters provide alternative, AID-independent interpretations on genotype-phenotype correlations previously reported in numerous studies on AID using the <i>Aicda^−/−</i> mouse strain.</p></div

Transcriptome comparisons of GC B cells and <i>in vitro</i> activated B cells from <i>Aicda^+/+</i> and <i>Aicda^−/−</i> mice.

<p>A) Box-plot of previously defined gene groups which are differentially expressed between <i>Aicda^+/+</i> and <i>Aicda^−/−</i> GC (left panel) and activated (right panel) B cells: CON, control group; LZS light zone signature genes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069815#pone.0069815-Victora1" target="_blank">[29]</a>, DZS, dark zone signature genes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069815#pone.0069815-Victora1" target="_blank">[29]</a>; NVS, naïve B cell signature genes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069815#pone.0069815-Klein2" target="_blank">[28]</a>, CBS, centroblast signature genes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069815#pone.0069815-Klein2" target="_blank">[28]</a>. For statistical analysis the sign test was applied. B) Volcano-plot of genes differentially expressed between <i>Aicda^+/+</i> and <i>Aicda^−/−</i> GC (left panel) and activated (right panel) B cells. The <i>Ighv</i> genes are shown in red. For statistical analysis the sign test was applied.</p

Assessment of AID-dependent DNA demethylation.

<p>A) Comparisons of CpG methylation load between <i>Aicda^+/+</i> and <i>Aicda^−/−</i> GC B cells in defined genomic elements. TSS, transcription start sides (TSS); EXN, exons; INT, introns; ING, intergenic regions are shown. Various statistical tests revealed no differences. B) Correlation plot of MethylCap data obtained from <i>Aicda^+/+</i> and <i>Aicda^−/−</i> GC B cells. The correlation coefficient, <i>r</i> was determined applying the Persons’ test.</p

Naïve B cells are pre-activated.

<p>A) Box-plot of previously defined gene groups differentially expressed between <i>Aicda^+/+</i> and <i>Aicda^−/−</i> naive B cells: CON, control group; LZS light zone signature genes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069815#pone.0069815-Victora1" target="_blank">[29]</a>, DZS, dark zone signature genes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069815#pone.0069815-Victora1" target="_blank">[29]</a>; NVS, naïve B cell signature genes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069815#pone.0069815-Klein2" target="_blank">[28]</a>, CBS, centroblast signature genes <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069815#pone.0069815-Klein2" target="_blank">[28]</a>. B) Volcano-plot of genes differentially expressed between <i>Aicda^+/+</i> and <i>Aicda^−/−</i> naïve B cells. The DZS genes are shown in red.</p

<i>Aicda^−/−</i> mice are not congenic and <i>Aicda^−/−</i> B cells are transcriptionally deregulated.

<p>A) CBA derived SNPs reside in chromosome 6. B) Number of variants per 50kb window is plotted against the position on chromosome 6, where <i>Aicda</i> (indicated in red) is located. CBA derived SNPs accumulate in a specific region. C) Contribution of each chromosome to the top 100 of differentially expressed genes between <i>Aicda^+/+</i> and <i>Aicda^−/−</i> GC B cells. The <i>chi</i> square test revealed that differential expressed genes located on chromosome 6 are significantly (p<2.2e-16) enriched in the region were CBA SNPs were found (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069815#pone-0069815-g004" target="_blank">figure 4B</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069815#pone.0069815.s005" target="_blank">Table S1</a>). D) As ‘C’, but now on activated <i>Aicda^+/+</i> and <i>Aicda^−/−</i> B cells. The <i>chi</i> square test revealed that differential expressed genes located on chromosome 6 are significantly (p<2.2e−16) enriched in the region were CBA SNPs were found (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069815#pone-0069815-g004" target="_blank">figure 4B</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069815#pone.0069815.s005" target="_blank">Table S1</a>).</p

Mortality prediction models for pediatric intensive care:comparison of overall and subgroup specific performance

<p>To validate paediatric index of mortality (PIM) and pediatric risk of mortality (PRISM) models within the overall population as well as in specific subgroups in pediatric intensive care units (PICUs).</p><p>Variants of PIM and PRISM prediction models were compared with respect to calibration (agreement between predicted risks and observed mortality) and discrimination (area under the receiver operating characteristic curve, AUC). We considered performance in the overall study population and in subgroups, defined by diagnoses, age and urgency at admission, and length of stay (LoS) at the PICU. We analyzed data from consecutive patients younger than 16 years admitted to the eight PICUs in the Netherlands between February 2006 and October 2009. Patients referred to another ICU or deceased within 2 h after admission were excluded.</p><p>A total of 12,040 admissions were included, with 412 deaths. Variants of PIM2 were best calibrated. All models discriminated well, also in patients <28 days of age (neonates), with overall higher AUC for PRISM variants (PIM = 0.83, PIM2 = 0.85, PIM2-ANZ06 = 0.86, PIM2-ANZ08 = 0.85, PRISM = 0.88, PRISM3-24 = 0.90). Best discrimination for PRISM3-24 was confirmed in 13 out of 14 subgroup categories. After recalibration PRISM3-24 predicted accurately in most (12 out of 14) categories. Discrimination was poorer for all models (AUC <0.73) after LoS of > 6 days at the PICU.</p><p>All models discriminated well, also in most subgroups including neonates, but had difficulties predicting mortality for patients > 6 days at the PICU. In a western European setting both the PIM2(-ANZ06) or a recalibrated version of PRISM3-24 are suited for overall individualized risk prediction.</p>