Additional file 2: Figure S2. of A donor splice site mutation in CISD2 generates multiple truncated, non-functional isoforms in Wolfram syndrome type 2 patients

Chromatograms of the 400 nt-5′-RACE products not subjected to subcloning. (TIFF 155 kb

Delineating the Cytogenomic and Epigenomic Landscapes of Glioma Stem Cell Lines

<div><p>Glioblastoma multiforme (GBM), the most common and malignant type of glioma, is characterized by a poor prognosis and the lack of an effective treatment, which are due to a small sub-population of cells with stem-like properties, termed glioma stem cells (GSCs). The term “multiforme” describes the histological features of this tumor, that is, the cellular and morphological heterogeneity. At the molecular level multiple layers of alterations may reflect this heterogeneity providing together the driving force for tumor initiation and development. In order to decipher the common “signature” of the ancestral GSC population, we examined six already characterized GSC lines evaluating their cytogenomic and epigenomic profiles through a multilevel approach (conventional cytogenetic, FISH, aCGH, MeDIP-Chip and functional bioinformatic analysis). We found several canonical cytogenetic alterations associated with GBM and a common minimal deleted region (MDR) at 1p36.31, including CAMTA1 gene, a putative tumor suppressor gene, specific for the GSC population. Therefore, on one hand our data confirm a role of driver mutations for copy number alterations (CNAs) included in the GBM genomic-signature (gain of chromosome 7- EGFR gene, loss of chromosome 13- RB1 gene, loss of chromosome 10-PTEN gene); on the other, it is not obvious that the new identified CNAs are passenger mutations, as they may be necessary for tumor progression specific for the individual patient. Through our approach, we were able to demonstrate that not only individual genes into a pathway can be perturbed through multiple mechanisms and at different levels, but also that different combinations of perturbed genes can incapacitate functional modules within a cellular networks. Therefore, beyond the differences that can create apparent heterogeneity of alterations among GSC lines, there’s a sort of selective force acting on them in order to converge towards the impairment of cell development and differentiation processes. This new overview could have a huge importance in therapy.</p> </div

Cytogenomic profiles of GSCs.

<p>(A) Frequency of gains and losses of whole chromosomes in the six GSC lines analyzed by QFQ-banding. The frequencies of numerical aberrations specific for each chromosome were calculated from the total of the analyzed metaphases of the six cell lines and represented as mean values. (B) Composite array CGH profiles of GSC lines. (C) Detailed 1p LOH mapping of GSC lines. A common region of LOH was identified in all the six GSC lines, involving D1S214 microsatellite, located at 1p36.31 and highlighted by the square box.</p

The GSCs’ methylation profiles evidence the functional impairment of cell development and differentiation processes.

<p>(A) Functional annotation analysis of commonly methylated or unmethylated gene promoters in all the three GSC lines (GBM2, G144 and G166), performed using GOstat software. The graph shows the percentage (y-axis) of each category compared to totally annotated genes. (B) Top 10 pathways influenced by DNA methylation pattern in GSCs. A p-value (calculated by the Ingenuity Pathway Analysis, IPA, software) is associated to each pathway; this value represents the probability that such association could have occurred by chance.</p

Methylation profiles.

<p>(A) Frequency of methylation and unmethylation of CGIs for each sample. The methylation status for each chromosome is reported and global genomic methylation percentages are displayed as the mean values of all chromosomes values. GSCs vs. CB660SP *p<0.05, **p<0.01, GSCs vs. GBM FFPE tissues §p<0.01, Chi-square test. Abbreviation: Met, methylation; Unmet, unmethylation. (B) Distribution of methylated and unmethylated CGIs among the different functional genomic regions.</p

GSC epigenetic signature.

<p>Epigenetic comparison between GSC, NSC and GBM FFPE tissue methylation profiles. The inner circle shows the 378 shared methylated genes in GSC lines, while the middle circle points out 37/378 genes that were unmethylated in NSC lines. The external circle displays the methylation status in GBM FFPE tissues. Note that 10 of the 37 specifically methylated genes in the GSC lines and unmethylated in foetal NSC lines were unmethylated in GBM FFPE tissues, representing the GSC epigenetic signature. The asterisk identifies 27 “cancer <i>de novo</i> methylated genes” in GSC and GBM FFPE tissues vs. foetal NSCs (see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0057462#pone.0057462.s016" target="_blank">Table S9A</a>).</p

Chromosomal aberrations identified in the six GSC lines through QFQ-banding and FISH analysis.

<p>The aberrations identified and/or supported by FISH analysis are marked in red. -: loss of whole chromosome; +: gain of whole chromosome; ++: more than one sovrannumerary copy; –: loss of at least two copies of a chromosome;/: lack of alterations. All the numerical alterations are referred to the modal number of chromosomes for each cell line. Numbers in square brackets refer to percentages of each abnormality. From a minimum of 12 to a maximum of 34 metaphases were evaluated for each GSC line and the analysis was performed on at least 3 different passages.</p