The poly(A)-binding protein nuclear 1 (PABPN1) is a ubiquitously expressed protein that is thought to function during mRNA poly(A) tail synthesis in the nucleus. Despite the predicted role of PABPN1 in mRNA polyadenylation, little is known about the impact of PABPN1 deficiency on human gene expression. Specifically, it remains unclear whether PABPN1 is required for general mRNA expression or for the regulation of specific transcripts. Using RNA sequencing (RNA–seq), we show here that the large majority of protein-coding genes express normal levels of mRNA in PABPN1–deficient cells, arguing that PABPN1 may not be required for the bulk of mRNA expression. Unexpectedly, and contrary to the view that PABPN1 functions exclusively at protein-coding genes, we identified a class of PABPN1–sensitive long noncoding RNAs (lncRNAs), the majority of which accumulated in conditions of PABPN1 deficiency. Using the spliced transcript produced from a snoRNA host gene as a model lncRNA, we show that PABPN1 promotes lncRNA turnover via a polyadenylation-dependent mechanism. PABPN1–sensitive lncRNAs are targeted by the exosome and the RNA helicase MTR4/SKIV2L2; yet, the polyadenylation activity of TRF4-2, a putative human TRAMP subunit, appears to be dispensable for PABPN1–dependent regulation. In addition to identifying a novel function for PABPN1 in lncRNA turnover, our results provide new insights into the post-transcriptional regulation of human lncRNAs. PABPN1 gene, is caused by defective expression of noncoding RNAs.In eukaryotic cells, protein-coding genes are transcribed to produce pre-messenger RNAs (pre–mRNAs) that are processed at the 3′ end by the addition of a sequence of poly-adenosine. This 3′ end poly(A) tail normally confers positive roles to the mRNA life cycle by stimulating nuclear export and translation. The fundamental role of mRNA polyadenylation is generally mediated by the activity of poly(A)-binding proteins (PABPs) that bind to the 3′ poly(A) tail of eukaryotic mRNAs. In the nucleus, the evolutionarily conserved poly(A)-binding protein PABPN1 is thought to be important for gene expression, as it stimulates mRNA polyadenylation in biochemical assays. Using a high-throughput sequencing approach that quantitatively measures the level of RNA expressed from all genes, we addressed the global impact of a PABPN1 deficiency on human gene expression. Notably, we found that most mRNAs were normally expressed in PABPN1–deficient cells, a result inconsistent with a role for PABPN1 in general mRNA metabolism. Surprisingly, our genome-wide analysis unveiled a new function for PABPN1 in a polyadenylation-dependent pathway of RNA decay that targets non-protein coding genes. Our discovery that PABPN1 functions in the regulation of noncoding RNAs raises the possibility that oculopharyngeal muscular dystrophy, a disease associated with mutations in the  PABPN1 gene was originally identified as a factor that stimulates the synthesis of RNA poly(A) tails in vitroin vitro polyadenylation assays have led to a model in which PABPN1 stimulates processive poly(A) synthesis by direct and simultaneous interactions with the growing mRNA poly(A) tail and the poly(A) polymerase Drosophila, specific pabp2 mutants also result in shorter poly(A) tails pab2, which encodes the PABPN1 ortholog, revealed that the expression of most protein-coding genes was unaffected, indicating that Pab2 does not function as a general mRNA maturation factor in this organism C. elegans, as this protein can be depleted from larvae without significant effects on mRNA levels and global translation Poly(A)-binding proteins (PABPs) play essential roles in eukaryotic gene expression. Normally, PABPs bind the poly(A) tract of mRNAs to confer positive roles in the mRNA life cycle, such as stability and translational activity. Two evolutionarily conserved, yet structurally different PABPs bind the poly(A) tract of mRNAs in most eukaryotic cells: PABPC1 in the cytoplasm and PABPN1/PABP2 in the nucleus. The product of the PABPN1 gene. OPMD usually appears between the fourth and sixth decade of life and is primarily associated with drooping eyelids, swallowing difficulties, and proximal limb weakness PABPN1 gene, which produce proteins with a stretch of 12–17 alanines PABPN1 mutations cause OPMD is still unclear.A detailed understanding of the role of PABPN1 in gene expression is significant, as the human genetic disorder oculopharyngeal muscular dystrophy (OPMD) is linked to mutations in the et al. showed that poly(A) tail length is not affected in myoblasts from individuals with OPMD cis elements within meiotic mRNAs Because OPMD is mainly restricted to ocular and pharyngeal muscles, it has been difficult to reconcile how defects in a cellular process as fundamental as mRNA polyadenylation could result in such a tissue-specific disease. In addition, Calado SHG60), we found a mechanism of RNA decay that requires lncRNA polyadenylation and the RNA exosome complex. Our findings thus define a novel function for PABPN1 in the regulation of human lncRNAs.To investigate the global effect of a PABPN1 deficiency on human gene expression, we performed a transcriptome-wide analysis of PABPN1–depleted cells using RNA sequencing. We find that the expression of most protein-coding genes is not affected by the loss of PABPN1, with less than 5% of the expressed genes showing misregulation at the mRNA level. Unexpectedly, a deficiency of PABPN1 resulted in the accumulation of a significant number of long noncoding RNAs (lncRNAs). By studying how PABPN1 promotes the turnover of the lncRNA encoded from the SNORD60-Host Gene  tail lengths and cleavage site decisions were similar between PABPN1–depleted and control cells . We also measured the impact of PABPN1 deficiency on mRNAs exclusively (using a dataset that excluded loci for which protein-coding and noncoding RNA genes overlapped) and obtained similar results as with the more comprehensive coding gene dataset: ∼4% of the protein-coding genes showed changes greater than 2-fold  and obtained >190 millions of 100-bp paired-end reads for both PABPN1–depleted and control HeLa cells. Sequence reads were mapped to the human genome with a spliced read aligner n 2-fold .+ transcripts, we do not expect to see reads mapping to known nonpolyadenylated ncRNAs  unless they are generated from a polyadenylated precursor. Notably, we found that a class of noncoding transcripts whose expression was substantially affected by PABPN1 deficiency was long noncoding (lnc) RNAs, defined as non-protein coding transcripts larger than 200 nucleotides p-value <1.5e-08, Student's t-test): it is assymetrical to the right in this case . The greater impact of a PABPN1 deficiency on lncRNA expression relative to mRNA expression is also observed by comparing the cumulative distribution of mRNAs and lncRNAs . Although most of the lncRNAs misregulated in PABPN1–depleted cells are still uncharacterized  RNAs. It should be noted that because the sequenced libraries were prepared using poly(A)his case , showingcterized , a few hcterized . In humacterized . 5′ and cterized . Consistp-value <3.4e-06, Chi-squared test) and protein-coding genes , we decided to concentrate our analysis on the functional relationship between PABPN1 and the upregulated lncRNAs. We first independently validated the results obtained by RNA-seq. Northern blot and quantitative RT-PCR analyses were used to confirm the accumulation of transcripts that belong to different classes of lncRNAs, including snoRNA- and scaRNA-host transcripts (As the proportion of lncRNAs showing increased expression (13%) in conditions of PABPN1 deficiency was significantly greater than for downregulated lncRNAs . Based on these findings, we used strand information obtained from the gene annotations to examine whether lncRNAs upregulated in PABPN1–depleted cells were sense or antisense relative to the mRNA produced from the downstream-positioned genes. We found that the lncRNAs negatively controlled by PABPN1 are preferentially antisense relative to their downstream protein-coding genes: of the 24 lncRNAs located ≤5.0-Kb upstream of a transcription start site, 20 are in the antisense orientation. In addition, the fold changes in lncRNA expression induced by the loss of PABPN1 and the fold changes in the expression of their neighboring protein-coding gene are not correlated  of neighboring protein-coding genes . Signifirrelated , suggestrrelated .Altogether, our transcriptome analysis indicates that a deficiency in PABPN1 does not globally affect mRNA expression, but revealed an important role for PABPN1 in the negative control of lncRNAs.The observation that a PABPN1 deficiency caused the upregulation of spliced, but not of unspliced lncRNAs  suggesteWe therefore examined the effect of PABPN1 deficiency on lncRNA stability. Actinomycin D was used to inhibit transcription in PABPN1–depleted and controls cells, and RNA decay was followed over time using the GAPDH mRNA as a control. Depletion of PABPN1 clearly stabilized the spliced SHG60 lncRNA . A similp-value <0.02, Student's t-test) proportion of SHG60 lncRNA is bound by PABPN1 relative to the GAPDH mRNA population.To address whether PABPN1 directly regulates the expression of lncRNAs, RNA co-immunoprecipitation (IP) experiments were used to examine whether PABPN1 can bind to lncRNAs. As can be seen in H2A histone gene, which encodes a non-polyadenylated mRNA (Gene regulation by a nuclear poly(A)-binding protein at the level of lncRNA decay predicts a polyadenylation-dependent mechanism. To test this prediction, we first analyzed the effect of cordycepin, which causes premature termination of poly(A) tail synthesis by inhibiting the poly(A) polymerase cis elements normally required during mRNA 3′ end processing. Accordingly, we tested whether the AAUAAA hexamer found 16-nt upstream of the SHG60 lncRNA poly(A) site was required for SHG60 expression by transfecting HeLa cells with DNA constructs in which the AAUAAA hexamer was mutated to AAGAAA and AGUACU  signal is required for proper 3′ end processing and suggest that PABPN1–induced lncRNA destabilization is coupled to poly(A) signal-mediated 3′ end processing.We also examined whether 3′ end processing of the SHG60 lncRNA involves d AGUACU . As was The observation that the accumulation of lncRNAs in PABPN1–depleted cells was associated with RNA stabilization  suggesteRRP40 mRNA depleted ∼80% of the total hRRP40 protein , as a consequence of their stabilization. Our findings thus elucidate a novel function for PABPN1 in the post-transcriptional control of noncoding RNAs.The poly(A)-binding protein nuclear 1 (PABPN1) is required for efficient and processive poly(A) tail synthesis C. elegansDrosophila pabp2 mutants in vitro, namely stimulation of poly(A) polymerase processivity and poly(A) tail length control, may not necessarily apply to all genes and all cell types, suggesting that the mechanism underlying nuclear poly(A) tail synthesis is likely to be more complex than previously anticipated.The biochemical characterization of mammalian PABPN1 has led to a model in which PABPN1 is critical for mRNA polyadenylation, and therefore gene expression Our transcriptome analysis revealed that only ∼4% of the expressed protein-coding genes were misregulated in PABPN1–depleted cells. While the mechanism by which PABPN1 controls – directly or indirectly – the expression of specific protein-coding genes remains to be determined, such mRNA-specific regulation is apparently not associated to the recently described role of PABPN1 as a regulator of polyadenylation site decision A surprising finding from the analysis of our RNA sequencing results was the impact of PABPN1 deficiency on the human noncoding transcriptome. Accordingly, our analysis revealed that a significant proportion (∼16%) of the expressed long noncoding (lnc) RNA genes were misregulated in PABPN1–deficient cells. Notably, the majority (78%) of lncRNAs misregulated in PABPN1–deficient cells showed increased expression, a result that supports a more prominent role for PABPN1 in the negative control of lncRNA expression. The past few years have seen increasing interest in the identification and characterization of mammalian lncRNAs S. pombe. Accordingly, Pab2 promotes TRAMP-independent, exosome-mediated decay of select transcripts via the polyadenylation activity of the canonical poly(A) polymerase Our data support the involvement of the nuclear exosome as a degradation machinery that catalyzes the turnover of PABPN1–sensitive lncRNAs . It is nAlthough the exact features that distinguish PABPN1–sensitive lncRNAs from other polyadenylated noncoding transcripts await further investigation, we found that lncRNAs stabilized in PABPN1–depleted cells were frequently located near and upstream of transcription start sites of neighboring coding genes. The existence of noncoding transcripts produced in the vicinity of transcription start sites of protein-coding genes has previously been observed in metazoans PABPN1 gene.Expanding numbers of human disease-associated lncRNAs have been reported in the past few years In sum, we have performed the first transcriptome-wide analysis of PABPN1–dependent gene expression in human cells. Our results are not consistent with a general role for PABPN1 in mRNA maturation, as the majority of protein-coding genes expressed normal mRNA levels in cells deficient for PABPN1. Importantly, our findings unveiled a new function for PABPN1 in the control of lncRNA expression and provide evidence for the evolutionarily conserved role of PABPN1 in polyadenylation-dependent RNA decay.HEK293T and HeLa cell lines were grown in DMEM supplemented with 10% FBS. siRNAs were transfected with lipofectamine 2000 at a final concentration of 25 nM for 72 hrs. Cordycepin 5′-triphosphate (Sigma Aldrich C9137) was dissolved in water and added to the cell culture medium at a concentration of 20 µM.GGAACGGCCUGGAGUCUGAUU), siPABPN1#6 (AGUCAACCGUGUUACCAUAUU), siUPF1 (GGAACCACCUGCUGAACUATT); sihRRP40 (CACGCACAGUACUAGGUCAdTdT), siMTR4 (C A A U U A A G G C U C U G A G U A A U U), sihTRF4-2 (G C G C U G A C G U C C A G A U A U U U U), siPARN (G G A A G A A G A A A G A C A G U U A U U), and siXRN2 (G A G U A C A G A U G A U C A U G U U U U).siRNAs are from Dharmacon/Thermo Scientific. Control siRNAs (ON-TARGETplus Non-targeting siRNA #1 and #4), siPABPN1#5 (SGH60 gene (exon1-intron-exon2) plus 1-kb of downstream sequence was PCR-amplified using genomic DNA and cloned into pcDNA5.1. The SGH60-ribozyme and SGH60-H2A constructs were generated by inserting a variant of the hepatitis d ribozyme and terminator sequences from the histone H2A gene, respectively, 2-nt upstream of the SGH60 polyadenylation site. The ribozyme and H2A-terminator sequences used were previously described GAAAAA and SGH60-GTACUA poly(A) signal variants were generated by site-directed mutagenesis using the wild-type SHG60 construct. All of the constructs used in this study were verified by automated sequencing.pEGFP-PABPN1 was previously described The rabbit monoclonal antibodies specific to PABPN1 and Actin were obtained from Epitomics Inc.. A rabbit polyclonal anti-PABPN1 was also used in this study . The rabbit polyclonal antibody specific for hRRP6/EXOSC10 was from Abcam. The monoclonal antibody specific to RNA polymerase II (8WG16) was from Covance and the anti-RNA Pol II antibody specific for Serine-2 phosphorylated CTD repeats was from Abcam (Ab5095). The mouse monoclonal GFP and Flag antibodies were from Roche and Sigma, respectively.http://hannonlab.cshl.edu/fastx_toolkit/index.html), discarding short reads (<32 nt) after trimming. Tophat Libraries were prepared from total human RNA and sequenced using the Illumina HiSeq technology. We obtained 196,998,210 and 192,880,594 100-bp paired-end reads for control and PABPN1–depleted cells, respectively . Reads cA comprehensive set of noncoding RNA genes was compiled by combining annotations from different sources, which are specified in Neighboring gene expression analysis: BEDTools and customs scripts were used to find, for each lncRNA gene, the closest transcription start site (TSS) of a protein-coding gene expressed above a minimum level (RPKM>1 in at least one of the samples). Strand information from current gene annotations was used to define transcripts as sense  or antisense.The Sequence Read Archive (SRA) number for the deep sequencing data reported in this paper is SRP015926.32P-labeled gene-specific probes. Signals were detected and quantified using a Typhoon Trio instrument. Real-time quantitative RT-PCR analysis were as previously described Northern blot were hybridized using HeLa cells that were previously transfected with siRNAs against PABPN1/Control for 72 h, were washed and cross-linked for 10 min at 37°C by addition of formaldehyde at a final concentration of 1%. Cells were washed in PBS, resuspended in 100 µl of ChIP lysis buffer , and sonicated with a Branson Sonifier 450 at 20% amplitude with 10-sec pulses at 3 min cycle. The chromatin solution was diluted 10-fold in ChIP dilution buffer . 5% of the chromatin extracts were used for purification of total DNA by ChIP. Each sample was precleared by incubating with 40 µl salmon sperm DNA/protein A-agarose 50% gel slurry for 2 hrs at 4°C. An aliquot of 4 µl of anti-RNA Polymerase II monoclonal Antibody (8WG16) was added and immunoprecipitated at 4°C overnight. The immunoprecipitates were collected using salmon sperm DNA/protein A-agarose and washed once with the following buffers in sequence: low-salt wash buffer ; high-salt wash buffer ; LiCl wash buffer ; and 3 times with TE . DNA-protein cross-links were reversed by incubation at 65°C overnight followed by proteinase K treatment. DNA was recovered by purification with the Qiaquik PCR purification column (QIAGEN). Quantitative real-time PCR was performed using total DNA as control, and immunoprecipitated DNA. Fold enrichments of RNA Pol II are relative to control ChIPs performed without antibody, as we found identical results regardless of using IgG control (abcam ab46540) or no antibody.2, and 1% NP-40) for RNA coimmunoprecipitations and buffer B  for protein coimmunoprecipitations. Total cell lysates were subjected to immunoprecipitation using specific antibodies and the antibody-coated beads were washed four times in lysis buffer before analyses by Western blot and RT-PCR. For the quantitative RT-PCR analysis of RNA IPs, fold changes were calculated relative to the control actin immunoprecipitation and normalized to the SRP RNA, as previously described Immunoprecipitation experiments were as previously described Figure S1A) the 11,572 protein-coding genes and (B) the 469 lncRNA-coding genes detected RPKM >1 in at least one sample. The density distribution of these histograms was analyzed using Kernel density estimation statistics, yielding the smoothed distribution shown in lncRNAs are more affected than mRNAs by a deficiency in PABPN1. Histograms of log ratios/fold-change (FC) in expression levels  showing the distribution of ((DOC)Click here for additional data file.Figure S2The expression of lncRNA genes is more significantly affected by a deficiency in PABPN1 than protein-coding genes. Cumulative distribution of RPKM fold changes after depleting PABPN1. Plotted are distributions for the protein-coding (red) and lncRNA (blue) genes with RPKM >1. The number of genes in each category is indicated in parentheses. The two distributions are significantly different .(DOC)Click here for additional data file.Figure S3Genomic organization of the SNORD60 Host Gene (SHG60). The blue rectangular boxes represent the 2 exons of human SHG60; the green rectangle represents the SNORD60 snoRNA present in the SHG60 intron. Numbers above and below the gene are the lengths of exons and intron, respectively, as determined by 5′ and 3′ RACE experiments. Transcription of SHG60 produces a 555-nt-long unspliced precursor transcript. Splicing of the SHG60 precursor yields two products: (i) an excised intron that will be processed into the mature C/D box SNORD60 snoRNA, and (ii) a 335-nt-long polyadenylated RNA with limited coding potential (24-nt ORF shown in red).(DOC)Click here for additional data file.Figure S4lncRNA genes that are negatively regulated by PABPN1 are frequently located upstream and near the transcription start site of a neighboring protein-coding gene. Histogram showing the distribution of lncRNA genes up-regulated in PABPN1–depleted cells according to the distance from their nearest expressed protein-coding gene. Bin size: 10 Kb.(DOC)Click here for additional data file.Figure S5A, in nucleotides, X-axis) or normalized expression levels  versus the fold change in expression in PABPN1–depleted cells relative to control cells (Y-axis), for the 469 lncRNAs expressed >1 RPKM in at least one sample. Dots above the red horizontal line represent the 60 lncRNAs induced >2-fold in PABPN1–deficient cells.Negative regulation of lncRNA expression by PABPN1 is not correlated with RNA size and expression level. Scatter plots of RNA length ((DOC)Click here for additional data file.Figure S6GAPDH gene (A) and SHG60 gene (B). ChIP data are presented as percentage of input normalized to control purifications. Values represent the means of at least three independent experiments and bars correspond to standard deviations.PABPN1 depletion does not affect GAPDH and SHG60 transcription. ChIP assays were performed on extracts prepared from cells treated with control (white bars) and PABPN1–specific (black bars) siRNAs using (A) a monoclonal antibody (8WG16) specific to RNA Pol II and (B) an anti-RNA Pol II antibody specific for Serine-2 phosphorylated CTD repeats. The coprecipitating DNA was quantified by real-time PCR using gene-specific primer pairs located along the (DOC)Click here for additional data file.Figure S7siRNA–mediated depletion of human RRP40. Western blot analysis of total extracts prepared from HeLa cells treated with RRP40-specific (lane 2) and control (lane 1) siRNAs for 72 hrs.(DOC)Click here for additional data file.Figure S8XRN2 and PARN are not required for the control of SHG60 lncRNA expression. Quantitative RT-PCR analysis of RNA prepared from HeLa cells treated with control siRNAs as well as with siRNAs specific to PABPN1, XRN2, and PARN. Fold increases in SHG60 lncRNA levels are relative to control siRNA and normalized to GAPDH mRNA. The data and error bars represent the average and standard deviation from at least three independent experiments.(DOC)Click here for additional data file.Figure S9Individual depletion of PABPN1 does not affect the expression of a promoter upstream transcript (PROMPT). Quantitative RT-PCR analysis of a representative PROMPT (40-33) region. HeLa cells were treated with the indicated combinations of siRNAs. Fold increases in 40-33 PROMPT levels are relative to the control/control siRNA mix and normalized to GAPDH mRNA. The data and error bars represent the average and standard deviation from at least three independent experiments.(DOC)Click here for additional data file.Figure S10A PABPN1–dependent switch in polyadenylation site usage does not necessarily induce a change in mRNA level. (A) Quantification of the fold changes in proximal/distal poly(A) site usage between PABPN1–depleted and control cells. Proximal/Distal ratios were calculated for the indicated genes using a previously described approach (DOC)Click here for additional data file.Figure S11Loss of PABPN1 does not result in the accumulation of promoter upstream transcripts (PROMPTs). (A–D) Integrated Genome Viewer screenshots showing the transcriptional start sites of the C21orf63 (A), EXT1 (B), HMGN1 (C), and RBM39 (D) genes that were previously associated with PROMPT activity (DOC)Click here for additional data file.Table S1Table showing all protein-coding genes with RPKM >1 in a least one sample. UCSC genes , with a protein ID , after removal of redundant genes. Coding genes up-regulated and down-regulated >2-fold are shaded in green and orange, respectively.(XLS)Click here for additional data file.Table S2Table showing all protein-coding genes with RPKM >1 in a least one sample, not overlapping ncRNA genes, displaying more than 2 fold change in expression: UCSC genes , with a protein ID , after removal of redundant genes.(XLS)Click here for additional data file.Table S3Table showing lncRNA genes with RPKM >1 in a least one sample. RNA genes >200 nucleotides, annotated in the Human lincRNA catalog, the lncRNA database, and the Noncode database, combined to eliminate redundancies. lncRNA genes up-regulated and down-regulated >2-fold are shaded in green and orange, respectively.(XLS)Click here for additional data file.Table S4Table showing the expressed lncRNAs and their closest expressed protein-coding gene. Expression cutoff was defined as RPKM >1 in at least one sample.(XLS)Click here for additional data file.Table S5Table showing the summary of statistics for the RNA sequencing data.(DOC)Click here for additional data file.Table S6List of publicly available annotations used to generate the coding and noncoding RNA datasets in this study.(DOC)Click here for additional data file.


Drosophila genome that are bound by HSF. We have also performed a transcript analysis of heat shocked Kc167 cells and third instar larvae and compared them to HSF binding sites. The heat-induced transcription profiles were quite different between cells and larvae and surprisingly only about 10% of the genes associated with HSF binding sites show changed transcription. There were also genes that showed changes in transcript levels that did not appear to correlate with HSF binding sites. Analysis of the locations of the HSF binding sites revealed that 57% were contained within genes with approximately 2/3rds of these sites being in introns. We also found that the insulator protein, BEAF, has enriched binding prior to HS to promoters of genes that are bound by HSF upon HS but that are not transcriptionally induced during HS. When the genes associated with HSF binding sites in promoters were analyzed for gene ontology terms, categories such as stress response and transferase activity were enriched whereas analysis of genes having HSF binding sites in introns identified those categories plus ones related to developmental processes and reproduction. These results suggest that Drosophila HSF may be regulating many genes besides the known HS genes and that some of these genes may be regulated during non-stress conditions.During heat shock (HS) and other stresses, HS gene transcription in eukaryotes is up-regulated by the transcription factor heat shock factor (HSF). While the identities of the major HS genes have been known for more than 30 years, it has been suspected that HSF binds to numerous other genes and potentially regulates their transcription. In this study, we have used a chromatin immunoprecipitation and microarray (ChIP-chip) approach to identify 434 regions in the  Drosophila decondensed or “puffed” when the larvae were exposed to heat or other forms of stress such as oxidative stress, inhibitors of respiration and certain metals More than four decades ago Ritossa described a phenomenon where specific loci on the polytene chromosomes from third instar larvae of Drosophila), and frogs, and multiple homologous but distinct HSF genes have been cloned in chickens, mice, and humans. The HSF that is primarily involved in responding to heat and other stress agents has been designated HSF1 in most species with multiple HSFs . HSF recognizes and binds to a specific DNA sequence in the promoter of HS genes known as the HS element (HSE) Drosophila) even under non-stress conditions. In the case of Drosophila, death was found to occur between the first and second larval instar stages in null mutants suggesting a critical role for HSF even under non-stress conditions −/−Hsf1  female mice also produce defective oocytes that, when fertilized, do not develop very far into embryogenesis −/−Hsf2 mice show embryonic brain defects that persist with adults displaying enlarged ventricles and a decrease in hippocampus size and striatum and cortex width HSF is an essential gene in those species that have a single HSF gene (e.g. yeast and C. elegansDrosophila embryos Drosophila adults Drosophila embryos using a 5400 element cDNA array and 3000 element tiling array Drosophila S2 cells using ChIP and next generation DNA sequencing (ChIP-seq) There have been a few genome-wide screens using DNA microarrays to characterize the eukaryotic transcriptional response to HS in Drosophila 3rd instar larvae were stained with anti-HSF antibodies, HSF was found to be localized to more than 200 loci Drosophila HSF might also play a role in the transcriptional repression of certain other genes that are known to be repressed during HS. Supporting this hypothesis, HSF1 in human cells has been shown to be a repressor of cytokine genes Drosophila genome. We have also performed transcription analysis of heat shocked Kc167 cells and 3rd instar larvae in an attempt to correlate HSF binding events with induction of gene transcription.When the polytene chromosomes from heat-shocked Drosophila genome (data available in GEO under GSE19744). HSF binding should reach a maximum level following a 30-minute heat-shock (HS) at 36.5°C Drosophila polytene chromosomes by indirect immunofluorescence Hsp26 promoter DNA by PCR and qPCR . Furthermore, this segment is contained in one of the loci (88E) bound by HSF on polytene chromosomes Agilent's Feature Extraction software quantified images of the arrays and Agilent's ChIP Analytics software identified probes corresponding to regions or segments of chromatin that were bound by HSF. We compared the anti-HSF and mock ChIP segments to determine if any of the anti-HSF ChIP segments were non-specific. Only two ChIP segments exhibited any degree of overlap to mock ChIP segments: the first  only partially overlaps with a mock ChIP segment and exhibits a much stronger min P[Xbar] on the ChIP array .Hsp23 because it is incorporated into the neighboring segment due to its proximity  closest to the peak of each binding site is listed in The HSF bound segments identified in our analysis span several oligonuclotide probes from the tiling array and average 1400 bp in length. Thus, for each HSF bound segment we assigned a “peak” as the center of probe in the segment with the lowest P[Xbar] . An example of how a peak identified in this way compares to the HSF-bound segments and probes identified by ChIP Analytics is presented in roximity . Using Proximity . A X2 te−4 to 1×10−5, right columns of −30, right column of Because the canonical HSE is composed of 3 repeats of the 5 bp motif nGAAn we sought to determine if other orientations of this motif were enriched in the peak region relative to the local background. We used Patser to determine the number of matches to each PWM for all possible 3-way combinations of the 5 bp motif represented in TRANSFAC and plotted a histogram to depict the distribution of matches . Two of Because the previous analysis of HSF binding events on polytene chromosomes was not of sufficient resolution to determine which genes HSF associated with at heat shock temperatures on a global scale, we set out to determine what genes may be affected by HSF binding by identifying the genes closest to each HSF binding site. In a first attempt to identify genes that may be regulated by HSF binding, we identified the nearest transcription start site (TSS) to the peak of each HSF bound segment. The result of this analysis is included in jumu. Roughly 1/4 of sites in transcribed regions were, however, also located in the proximity of gene promoters (which we are considering to be a region surrounding 1250 bp from the transcription start site). By our definition, 14% of the genome falls in gene promoters, however, of the 43% of sites that are intergenic, over half were found in promoter regions representing 27% of all HSF binding sites  relative to those that were not (intergenic) . In totang sites . It shouGiven this distribution we were interested in determining if HSF is targeting a specific class of genes when binding to promoters so we used the online resource DAVID To further this analysis, we investigated the enrichment in functional categories among genes that contained at least one HSF binding site within their transcribed region. In this case we considered only those 35% of sites that were within introns and greater than 1250 bp away from the transcription start site of any gene/isoform. This conservative estimate of the number of HSF binding sites found in introns still represents a 2-fold enrichment over the background distribution since only 17.1% of euchromatin is intronic. Our findings here were largely unexpected; there was a strong enrichment for genes involved in biological regulation and more specifically the regulation of transcription and metabolic processes as well as for genes involved in reproduction and development such as gamete generation and anatomical structure development (−2) to PWMs representing 111 different DNA binding proteins from two databases, Transfac and the Drosophila DNase I Footprint Database. As expected, the PWM representing the 15 bp HSE composed of inverted repeats of nGAAn (2>40) and show the same level of significance in a chi square test  as the PWM representing the canonical HSE /DNA binding protein(s) were associated with these sites. To identify possible candidates, we used Patser to scan HSF-bound promoters and introns for matches  relocalizes from several hundred discrete loci on polytene chromosomes to a far smaller number of loci with a large amount of pol II accumulation at the HS puff sites Hsp70, Hsp68, Hsp27, Hsp26, Hsp23, and Hsp22. In addition, seven other genes exhibited fold changes comparable to the small HSPs: CG32850, CG12507, SP555, Gr63a, CG8086, CG7509, and Ir93a. Aside from CG7509, which is repressed in response to both oxidative stress and ER stress in DrosophilaDrosophila. In general, most genes we identified changing in response to heat shock in Kc cells were up-regulated, and showed only a modest change in transcript levels (less than 4-fold) . RNA isolated from HS and untreated cells was reverse transcribed, labeled and hybridized to NimbleGen expression microarrays (data available in GEO under GSE19745). We identified 211 genes that showed at least a 2-fold change in expression due to heat shock with a FDR corrected p-value less than 0.01 . Not sur 4-fold) .DAVID analysis of genes differentially regulated in cells revealed an enrichment in many of the same categories enriched among genes whose promoters were associated with HSF following HS (Hsp70Bbb (the most highly induced gene in Kc cells) likely due to the highly repetitive nature of this sequence .Because many novel heat responsive genes were identified in this genome-wide screen, we wanted to determine how far away the nearest HSF binding site was relative to transcription start sites of these genes. sequence . Insteadrd instar larvae. As with the cells, the larvae were subjected to a 30 min heat shock at 36.5°C to match the conditions used for HSF binding site identification (data available in GEO under GSE19745). Overall, 237 genes exhibited a 2-fold or greater change in expression and a FDR corrected p-value of less than 0.01 , DnaJ-1, and 22 other genes, of which 10 have been previously associated with at least one other stress in Drosophila , although not a classical HSP, is induced in response to several stresses including oxidative and ER stress, aging, starvation and HS To further investigate possible HSF association with genes regulated during heat shock we compared the lists of genes exhibiting a 2-fold or greater change in expression in response to heat shock in either cells or larvae to the 471 genes that either contained an HSF binding site within their coding region or were located 1250 bp downstream of the peak of an HSF bound region . Only niBecause the FDR corrected p-value cutoff we applied in the identification of transcriptionally regulated genes of 0.01 is relatively stringent, it is possible that other genes associated with HSF-binding sites were transcriptionally regulated during HS but were not identified in our expression analysis. To investigate this possibility we generated a cluster of the expression profiles of all genes that were associated with an HSF binding site and/or were identified in our expression analysis of HS cells and larvae and aligned a heat map depicting the relative enrichment of the nearest HSF bound segment identified in our ChIP-chip analysis  . This an−4) ) and an insulator associated protein, CP190, for which ChIP-chip data is available in Kc cells Of the 11 HSF-bound and HS-induced genes identified in Kc cells  all are −4) . Convers−4)  and that4Hsf mutant 3rd instar larvae using the same approach described above (data available in GEO under GSE22332). These larvae have a temperature sensitive mutation in the HSF DNA binding domain which prevent HSF from binding to HSEs and inducing HSP gene transcription at non-permissive (i.e. heat shock) temperatures 4Hsf larvae and the degree of induction was generally far less than what was seen in the wild type larvae  this distribution of HSF-binding sites appears to exhibit a strong bias for the extreme 3′ end of the introns close to the 5′ ends of alternate isoforms. In addition, all five HSF binding sites overlap with binding sites identified in Kc167 cells for the ecdysone receptor complex (EcR-C) Eip75B, Eip74EF, and brSince HSF binds a large number of introns of genes that do not appear to be transcriptionally induced during HS, we decided to examine a subset of HSF-bound introns more closely. The functional enrichment analysis presented in the previous section , indicatithin br . InteresEip75B, Eip74EF, and brIt has been previously shown that the developmentally regulated chromosomal puffs like the ecdysone induced puffs regress during HS HSF in metazoans is activated upon stress to trimerize and bind HSEs that are found in the promoters of HSP genes. HSF binding leads to the release of stalled RNA polymerase II as well as the recruitment of new polymerase complexes Drosophila melanogaster using ChIP-chip methodology on Drosophila genomic tiling arrays. In total we identified 434 HSF bound chromatin segments in heat shocked Kc167 cells with the transcription start site (TSS) of 270 genes mapping to within 1250 bp of an HSF binding site. A comparison of our binding data to an earlier study that utilized heat shocked Drosophila embryos and cDNA arrays for the ChIP-chip shows that approximately 33% of the HSF-bound genes in their study (i.e. 62 out of 188) correlate with our binding sites  show very few changes in transcript levels. For these few genes, differential levels of these transcripts might be regulated post-transcriptionally, a phenomenon reported for certain HSP genes in 4Hsf flies We attempted to correlate the HSF binding events with changes in gene transcription using standard expression microarray analysis of heat shocked Kc cells and ifferent . For exaWe were also interested in determining if any other transcription factor(s)/DNA binding protein(s) were associated with these HSF sites. We did find that a number of HSF bound promoters (and not introns) also contained binding sites for the BEAF transcription factor (known to be important for insulating enhancers) and this finding is consistent with a recent report that BEAF-binding sites tend to be associated with the 5′ UTRs and regions immediately upstream of the transcription start site of genes Drosophila. Overall, they observed a correlation of histone acetylation, H3K4 trimethylation, RNA polymerase II and coactivators such as GAGA factor with HSEs that ultimately are bound by HSF after heat shock compared to HSEs that are not bound by HSF. These chromatin modifications and proteins are hallmarks of transcriptionally active chromatin and the authors argue that the modifications are requirements for HSF to bind to HSEs prior to transcriptional induction as opposed to a consequence of transcription A recent paper by Guertin and Lis rd instar larvae lends support to this hypothesis. That is, the transcriptional response to an active transcription factor is likely dependent on the cellular/nuclear environment and/or chromatin state that exists in a given cell type. As discussed above, this could include the possibility that HSF might be acting as a specific transcriptional repressor of certain developmentally regulated genes whose puffs on polytene chromosome regress during heat shock Drosophila metamorphosis What is the function of HSF binding to so many different places in the genome if it is not to regulate the heat shock genes during stress? There is the possibility that there are some genes that are being transcriptionally induced by HSF during HS in other developmental stages and/or tissue types. The different transcriptional response to heat by Kc cells and 3Drosophila genome that fall into this category Another possibility is that HSEs appear in the genome with a certain frequency and have no biological consequence. It has been suggested that there are a large number of cis-regulatory modules (CRMs) in the Drosophila show developmental arrest (i.e. at 1st and 2nd instar) as well as defects in oogenesis under non-HS conditions Drosophila that have a single HSF, HSF may be performing numerous roles under both HS and non-HS conditions. It is also possible that during Drosophila development that other forms of stress are occurring that induce HSF transiently. As animals evolved, gene duplication and divergence resulted in multiple HSFs that distributed some of the important functions to specific and/or multiple HSFs. As previously mentioned, mice lacking HSF1 display growth retardation and female infertility due to defective oocytes −/−Hsf1  oocytes exhibit a delay and blockage of meiotic maturation and this defect at least in part can be related to a decrease in Hsp90α transcript levels and Hsp90 protein activity −/−Hsf2 mice show embryonic brain defects with the defect in cerebral cortex formation being attributed to the reduced expression of an HSF2 regulated gene, p35−/−Hsf2 mice showing a more severe defect resulting in a reduced testis size and the disruption of spermatogenesis characterized by degenerating cells, the absence of differentiating spermatids and spermatocytes, vacuolization of the tubules and reduced sperm count rd of the 105 genes known to exist on the Y chromosome Yet another possibility is that HSF has functions during non-HS conditions and that the ChIP-chip analysis is revealing many of those gene targets. Clearly HSF deficient Drosophila showed enrichment for a number of developmental processes as well as gamete formation including oogenesis  were grown to confluence in Schneider's media (Invitrogen) supplemented with 5% heat-inactivated FBS (Sigma) and 20 µg/ml gentamicin (Sigma) in tissue culture flasks at 22°C. Prior to heat-treatment, cells were transferred to Erlenmeyer flasks and aerated for 4 hrs at 22°C by gentle shaking (∼180 rpm). Following aeration, half of the cells were heat-shocked by submersing the flask in a 36.5°C circulating water bath (Neslab) for 30 minutes. The remaining cells were maintained as room temperature controls. For the cycloheximide experiments, 118 µM cycloheximide was added 10 minutes prior to initiating the heat shock treatment Cell cross-linking, lysis and chromatin shearing were all performed as reported in Hsp26 promoter and on DNA from unamplified and amplified HS anti-HSF and mock ChIPs with primers designed against regions upstream of the following genes: Hsrω, Hsp70Ab, Hsp83, CG11267, stv, CG5290, Tom34, DnaJ-1, CG10077, Taf7, and GstD2. Cycling conditions used are as follows: 95°C for 3′ followed by 35 cycles of 95°C for 30”, 58°C for 45” and 72°C for 30”. Quantitative PCR was performed on the same DNA samples with the same primers plus additional primers designed to amplify the region of chromosomal DNA 1200 bp downstream of the Hsp26 promoter. Our qPCR reactions were performed with Brilliant SYBR Green (Stratagene) in a MX4000 lightcycler (Stratgene) under the same conditions as above (only for 40 cycles instead of 35). See Standard PCR was performed on DNA from HS and RT, anti-HSF and mock ChIPs, and WCE with primers designed against the The immunoprecipitated sheared chromatin was repaired as described in Drosophila Microarray Centre web site (www.flyarrays.com). Labeled DNA was mixed with control nucleic acids  and then added to hybridization buffer  heated to 95°C for 3 min and then incubated at 40°C for 15 min. Labeled DNA was hybridized to Agilent genome-tiling arrays containing approximately 475,000 60mer probes (i.e a probe every 233 nucleotides) according to manufacturer's directions. After 20 hours of hybridization, the slides were washed for 5 min with 6x SSPE, 0.005% ultrapure N-lauroylsarcosine, again for 5 min, 0.6x SSPE, dipped in acetonitrile and washed for 30 s in Agilent's Wash III. Dried slides were scanned with Agilent's microarray scanner and the resulting images were quantified with Agilent's Feature Extraction software.A second round of PCR similar to that performed for LM-PCR was used to incorporate amino-allyl modified nucleotides into the amplified material. Following amplification, DNA clean-up, fluorescent dye conjugation and probe clean-up and precipitation was performed as described on the Canadian Drosophila genome. The Whitehead Error Model v1.0 and Whitehead Per-Array Neighborhood Model v1.0 were used with the default settings for error modeling and for peak detection/evaluation, respectively with a false discovery rate of 11%.Data from Feature Extraction was normalized with Agilent's ChIP Analytics software. Blank subtraction normalization, inter-array median normalization and intra-array (dye-bias) median normalization were all applied. Probes were mapped to release 4.2 of the −4 and 1×10−5 in 50 bp windows relative to the segment peak and generated a frequency histogram. To determine if there is a significant difference in the number of matches in the region immediately surrounding the peak (−500 bp to +500 bp) relative to the local background (−1000 bp to −550 bp and +550 bp to +1000 bp) we performed a X2 test. We repeated these steps to identify matches to all other unique PWMs from TRANSFAC and from the Drosophila DNase I Footprint Database this time counting matches to each matrix in HSF-bound promoters and HSF-bound introns with Bonferroni corrected p-values <5.6×10−2. To control for base composition bias of the test sequence, for any matrix exhibiting an enrichment of binding sites in the peak region relative to the local background comparable to the enrichment seen for the canonical HSE, we repeated the test with a scrambled version of the matrix and then threw out any matrix still showing enrichment. For any remaining matrices, we performed a X2 test to determine if there is a significant difference in the number of sites matching the matrix in the HSF-bound promoters relative to HSF-bound introns.We used the pattern matching program Patser dp cn bw and 4 cn bwHsf) Late third instar larvae (Drosophila 385 k NimbleGen expression microarrays (Roche) was carried out as described in the manufacture's protocol with the exception that HS and RT samples were differentially labeled and hybridized to a single array. For each treatment, three independent biological replicates were performed. For the ecdysone plus heat shock experiments, a cDNA based microarray was used and the microarray experiments and analysis were carried out following the methods of Neal and co-workers Total RNA was extracted from both cells and larvae using TRIzol reagent (Invitrogen) according to the manufacture's protocol. Quality and quantity of RNA was verified by measuring the absorbance and the A260/A280 ratios were always above 1.8. cDNA synthesis, labeling and hybridization to Images acquired after scanning slides with GenePix 4000B microarray scanner (Molecular Devices) were quantified and RMA normalized with NimbleScan (Roche). ArrayStar (DNASTAR) was used to analyze the resulting data files and identify genes with an average fold change across all biological replicates of 2 fold or greater and FDR corrected p-values less than 0.01. Log-converted expression ratios were clustered in the microarray data analysis software MeV Functional enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery Bioinformatics Resources (DAVID) Table S1HSF binding ratios of segments bound under hs and non-hs conditions.(XLS)Click here for additional data file.Table S2Complete list of sites and associated genes bound by HSF.(XLS)Click here for additional data file.Table S3Complete list of genes exhibiting a 2 fold or greater change in response to heat shock in either Kc cells, wild type (dp), and Hsf mutant (4Hsf) larvae. The position of the nearest HSF binding site to each gene is also given.(XLS)Click here for additional data file.Table S4Primer sequences used for gene specific PCR.(XLS)Click here for additional data file.


The Notch1 signalling pathway has been shown to control neural stem cell fate through lateral inhibition of mash1, a key promoter of neuronal differentiation. Interaction between the Delta1 ligand of a differentiating cell and the Notch1 protein of a neighbouring cell results in cleavage of the trans-membrane protein, releasing the intracellular domain (NICD) leading to the up regulation of hes1. Hes1 homodimerisation leads to down regulation of mash1. Most mathematical models currently represent this pathway up to the formation of the HES1 dimer. Herein, we present a detailed model ranging from the cleavage of the NICD and how this signal propagates through the Delta1/Notch1 pathway to repress the expression of the proneural genes. Consistent with the current literature, we assume that cells at the self renewal state are represented by a stable limit cycle and through in silico experimentation we conclude that a drastic change in the main pathway is required in order for the transition from self-renewal to differentiation to take place. Specifically, a model analysis based approach is utilised in order to generate hypotheses regarding potential mediators of this change. Through this process of model based hypotheses generation and testing, the degradation rates of Hes1 and Mash1 mRNA and the dissociation constant of Mash1-E47 heterodimers are identified as the most potent mediators of the transition towards neural differentiation. E box binding activates the transcription of genes that promote differentiation towards neural cells. The repressor type bHLH genes, which include members of the Hairy/Enhancer of split family , inhibit commitment of neural progenitor cells either by intersecting the formation of E box binding heterodimers and/or by forming homodimers that bind with high affinity to specific DNA sequences (CACNAG) termed N boxes. N box binding represses the transcription of the bearing gene. Though a number of genes have been associated with the Delta1/Notch1 signalling pathway, the most dominant ones are the repressor genes hes1, notch1, rbpj and the activator gene mash1The correct timing and distribution of differentiation in neural stem cells is critical for the integrity, shape, and size of the developing brain and the proper functioning of the central nervous system in mammals hes1 activity has been shown to decrease the more a cell commits towards a specific cell type while Mash1 is up-regulated Both the activator and repressor type genes are active from the very early stages of development The oscillatory behaviour of the Notch signalling pathway has been the focus of a number of modelling studies following publication of experimental findings hes1 transcription is repressed via its own protein product is not adequate to reproduce sustained oscillations. They successfully reproduce the experimental results via the inclusion of an “unknown” factor which they speculate is either a chemical or environmental parameter. Jensen et al. Hirata et al. mash1, hes6, dll1).Agrawal et al. in silico experimentation and model based hypothesis generation. The need for a signal that instigates the behavioural change that leads to neural differentiation is demonstrated and potential mediators of this signal are explored through model analysis techniques.Herein, we present the formulation of a detailed mathematical model that is used to qualitatively investigate the dynamic characteristics of the Delta1/Notch1 pathway which ultimately determines the fate of neural stem cells. Based on the assumption that cells at the self renewal state are represented by a stable limit cycle, we present a detailed, up-to-date mathematical description of the Delta1/Notch1 signalling pathway and demonstrate the behaviour of the system not only during the oscillatory phase but also during the transition towards specific cell lineages and at the differentiated state. It is plausible that the signal that instigates the behavioural change leading to the down-regulation of Hes1 is either the result of cross-talk with a different signalling pathway Zeiser et al. All genes were modelled based on a modified version of the model originally presented by Goodwin i,x and pj,z denote the concentrations of the x-th mRNA instance of gene i and the z-th instance of protein j, respectively. Cj denotes the specific translation rate of protein j from its respective mRNA transcripts of gene i. kd,i and kd,j denote the degradation rates of the mRNA molecules of gene i and protein j, respectively, while amRNA and bprot are the respective transfer constants between the various compartments. The term dimerf was added in order to account for the depletion of the proteins that form heterodimers.Where mi denotes the transcription rate of the thi gene and is formulated as a maximum theoretical transcription level ki,0 modified by the nuclear concentrations of its activators and its repressors as shown below by equation (2):kif as introduced by i, we have generalised function if, as shown by equation (3):Function n was introduced in order to differentiate between repressors binding only as dimers and repressors that have the ability to bind as single molecules. When repressor j binds on gene i as a dimer, then parameter n is equal to 2; if the repressor binds as a single molecule, parameter n is equal to 1. The Hill coefficient h, takes its value according to the number of binding sites for repressor j present in gene i. The resulting exponents are high and similar to the ones observed by other studies Parameter hes1 has been shown to have 2 adjacent RBPJ binding sites. RBPJ is known to function both as a repressor and as an activator when NICD is present hes6, has been shown mash1 transcription. Hes6 can form heterodimers with Hes1, thus sequestering Hes1 molecules and prohibiting the formation of Hes1 homodimers that repress mash1 transcription. Based on these observations equations (4) were formulated in order to describe the dynamics of the transcription of Hes1 mRNA, the formation of Hes1 proteins and the formation of Hes1-Hes6 heterodimers.Hes1 has been shown to oscillate with a 2 h period and is known to inhibit its own transcription elp is a binary variable used to denote the existence or absence of a delta signal from a neighbouring cell. NRB denotes the concentration of the NICD-RBP complex in the presence of a delta signal and the equations describing its formation are presented along with the equations describing the cleavage of the NICD further below. The multiplier preceding the concentration of the NRB complex equals the number of RBP binding sites on the respective gene.dRbpj gene has been reported to be ubiquitously expressed Drosophila. However, based on sequence motif recognition software, rbpj itself is repressed by the Hes1 homodimers containing 3 N box binding sites and 3 RBPJ binding sites. Herein we will follow the assumption made by hes1 gene, we have modelled the rbpj transcription to be inhibited by RBPJ in the absence of NICD, yet to be promoted by the NICD-RBPJ complex. As NICD concentration increases, so does the amount of NICD-RBPJ complexes being formed resulting in a smaller pool of “free” RBPJ proteins.notch1 gene has been shown to have one putative N box site and two putative RBPJ sites Similarly, the notch1, hes1, rbpj) form a complex loop of negative regulation applied on each other . Mash1 is a bHLH protein that has been shown to induce the expression of genes that promote neuronal differentiation E proteins, like E-47, which bind on the E-boxes of the neuronal differentiation inducing genes and enhance transcription of these genes mash1 contains one N box site These 3 genes . This complex binds on the RBPJ sites of Arabidopsis thaliana. In their work they conclude that any form of quantitative comparison between their genetic network model and any set of experimental mRNA traces would be inappropriate due to the sparsity and inherently noisy nature of such data. Instead they formulate an empirical cost function which they evaluate over a large number of random points, accounting for varying parameter values, and declare the set that minimises the value of the cost function as the optimal parameter set. The formulation of the cost function was a sum of terms that quantify the agreement between the model output and an experimentally observed qualitative feature. Following this approach consisted of running the model initially with parameter values taken from relevant literature where available, or estimates of the appropriate order of magnitude where not available; in order to verify that it satisfactorily captures the behaviour of the modelled system.Due to the limited availability of dynamic experimental measurements of the modelled variables, the nature of the model is qualitative rather than quantitative. Nevertheless the choice of parameter values is not trivial as it ultimately defines the behaviour of the model. According to 14 randomly generated points each representing a vector of all parameter values. The random points were generated using the Sobol NICD, KNICD, KRBP-J, kd,NICD, k0,NICD-RBP, kd,NRBk) were excluded from this process as they require the presence of an active delta signal which results in a deviation from the oscillatory resting state for which experimental data are available. Furthermore, appropriating the same transfer constants  for all modelled variables is a simplification of the model which was done in an attempt to reduce the size of the parameter vector and hence the dimensionality of the parameter space that needs to be sampled. The values used herein have been taken from the work of Following the approach of 12 points. The computational cost of increasing the number of sampled points beyond 214 is prohibitive and counterintuitive when taking into consideration the limited benefits of doing so. Thus the optimal parameter set as identified by this search of the parameter space can be found in This resulted in a vector of 31 parameters which were randomly varied within ±90% of their initial value in search of the optimal parameter set. The model was simulated for 3,000min allowing enough time for a resting state to be reached and the cost function was evaluated over the last 1,000min in order to ensure that the resting state has been reached. Details on the formulation of the cost function can be found in The model was simulated with the parameter values presented in hes1, results in the overall repression of the neuronal differentiation inducing genes . Therefore we applied a delta signal for varying amounts of time and studied the response of the key components of the pathway. As mentioned previously, under the effect of a Delta signal from a neighbouring cell, the upregulation of the main effecter of the Delta/Notch pathway, Subsequently the robustness of the gene network was tested by applying variably sized pulses for a fixed amount of time (980min) to various components of the pathway and studying the response. The model proved robust to pulses in the concentrations of Hes1, Mash1 and Hes6, as shown in A model based approach was employed in order to identify components of the Delta/Notch pathway as viable candidates able to mediate the required behavioural change which results in neuronal differentiation. In the context of our assumption that cells in the self renewal state are in a stable limit cycle, differentiation would be equivalent to a new non-oscillating resting state. As shown in previous studies in silico experiments to investigate whether these parameters can mediate a behavioural change towards neural differentiation, when affected one-at-a-time or in pairs of two. The parameters of Derivative Based Global Sensitivity Measures hes1 silencing at the post-transcriptional level. They further comment that the presence of this mRNA regulator is crucial for the neuronal differentiation of NT2 cells. Since our model doesn't include such a regulatory mechanism this could be implemented by an increase in the value of Hes1 mRNA degradation accounting for the function of this mi-RNA. In support of our model-driven hypothesis, A number of experimental studies have reported that a potential increase in the stability of Mash1 mRNA can be crucial in mediating neuronal differentiation. More specifically, hes1 gene and its protein product have already confirmed the significance of the degradation rate of Hes1 protein in the presence or absence of oscillations Many of the mathematical studies of the oscillatory behaviour of the Apart from its role in the sequestration of Hes1 monomers, Hes6 has been linked to cell cycle regulation BMP-2 induces a post-transcriptional decrease in Mash1 levels through enhanced degradation. While studying Mash1 stability under the effects of BMP-2 over expression The parameters  identifid,hes – kd,h6rnk  further highlighting the crucial effect these parameters have on the behaviour of the modelled gene network. Our model based hypotheses generation approach presented herein, has initially highlighted the need for a conformational change within the Delta/Notch pathway in order to reach a resting state resembling differentiated cells and more importantly through model analysis has identified the parameters that could mediate such an effect. kd,h6rn . When va kd,h6rn  had the The Delta1/Notch1 signalling pathway has drawn scientific attention due to its significance during embryogenesis and development. A number of studies have modelled parts of the pathway and its main characteristic, which is an oscillation with a period of roughly 2 h. Herein, we have introduced a mathematical formulation that involves all the significant elements participating in the pathway, facilitating a more holistic description of the dynamic behaviour of the pathway. By studying the response of the pathway to disturbances introduced in some of its key components, namely Hes1, Mash1 and Hes6 protein concentrations we conclude that a functional change is required in order for the pathway's behaviour to shift towards neural differentiation, instigated perhaps through cross-talk with other pathways.in silico experiments we identified three parameters Click here for additional data file.Figure S1Phase planes of Hes1 (A–D), Mash1(E–H) and Hes6 (I–L) mRNA transcripts versus respective protein expression as a response to the application of a delta signal from a differentiating neighbouring cell for varying time periods. : Steady state ; : 120 min application; : 240 min application; : 480 min application.(3.11 MB TIF)Click here for additional data file.Figure S2Phase planes of Hes1 (A–D), Mash1(E–H) and Hes6 (I–L) mRNA transcripts versus respective protein expression as a response to the application of variably sized pulses in the concentration of Hes1 for 960 min: : Steady state (no pulse); : 1× pulse; : 5× pulse; : 10× pulse.(3.13 MB TIF)Click here for additional data file.Figure S3Phase planes of Hes1 (A–D), Mash1(E–H) and Hes6 (I–L) mRNA transcripts versus respective protein expression as a response to the application of variably sized pulses in the concentration of Mash1 for 960 min: : Steady state (no pulse); : 1× pulse; : 5× pulse; : 10× pulse.(3.16 MB TIF)Click here for additional data file.Figure S4Phase planes of Hes1 (A–D), Mash1(E–H) and Hes6 (I–L) mRNA transcripts versus respective protein expression as a response to the application of variably sized pulses in the concentration of Hes6 for 960 min: : Steady state (no pulse); : 1× pulse; : 5× pulse; : 10× pulse.(3.13 MB TIF)Click here for additional data file.


The ability of retroviruses and transposons to insert their genetic material into host DNA makes them widely used tools in molecular biology, cancer research and gene therapy. However, these systems have biases that may strongly affect research outcomes. To address this issue, we generated very large datasets consisting of  Retroviruses and transposons are widely used in cancer research and gene therapy. However, these systems show integration biases that may strongly affect results. To address this issue, we generated very large datasets consisting of  DNA integrating elements, such as transposons and retroviruses, are an important tool in many areas of molecular biology, e.g. gene therapy Three of the main integrating elements currently used in the fields mentioned above are the Sleeping Beauty transposon (SB), the piggyBac transposon (PB), and the mouse mammary tumor virus (MMTV). During the last decade, some studies have reported on integration biases in the mouse genome for one or more of these systems. SB does not integrate randomly on a micro-scale, since it is dependent on local DNA deformability, and the presence of a TA dinucleotide at the site of integration While the studies mentioned above have provided valuable insights into retroviral and transposon target site selection for SB, PB and MMTV in the mouse genome, they do have some limitations with respect to gaining insight into de novo integration target site selection. These limitations can be subdivided into three categories. First, there are limitations regarding the individual integration datasets. For example, some datasets were generated using cells that were enriched with a selectable marker, e.g. To address these questions, we generated large datasets of SB and PB integrations in mouse embryonic stem cells (mESC). In order to directly compare the two transposons, they were mobilized from the same construct containing inverted repeats (IRs) for both PB and SB. This eliminates any other possible cause than the IR (or the transposon-specific transposase) for the observed differences between the two systems. In addition, we generated a large dataset of MMTV integrations in normal murine mammary gland epithelial (NMuMG) cells. All three datasets were generated under minimal selective pressure, and are henceforward referred to as unselected integration profiles. They are considerably larger than previously published datasets of unselected integrations, We associated the three integration profiles with a large number of genomic and epigenomic features. In particular, for SB and PB, the recent explosive growth in publicly available ChIP-seq datasets Additionally, the impact of selective pressure on an unselected integration profile, which is important in using IM for cancer gene discovery, has never been addressed extensively. Previous work can be classified as either knowledge-based or data-driven. The knowledge-based approaches use modeling of previously described integration biases to avoid CIS calls that can be explained by these biases, such as SB TA sequence specificity Taken together, this allows us to present the most extensive analysis of SB, PB and MMTV target site selection to date, the results of which include previously undetected biases and differences between selected and unselected integration profiles. Another focal point of the analysis is the influence of scale. By analyzing differences between small-scale  and 94% (SB) of the integrations . The remThe bias of an integrating element with respect to genes is of particular interest in IM and gene therapy. In IM screens, more integrations near genes are desired whereas in gene therapy integrations proximal to genes pose a potential threat to the patient, since such integrations may give rise to cancer. We, therefore, compared the integration density of the three systems in and around genes, by aligning all genes . PB showp-values associated with these statistical tests. It confirms the strong bias of PB for TSSs, and indicates that PB prefers integrating with its transcription unit oriented towards the TSS. When landing within genes, MMTV prefers a sense orientation relative to the host gene, and SB and PB an antisense orientation. In general, MMTV shows the least biased profile, with weak but significant biases for TSSs, and against genic regions. PB integrations are mainly enriched in the 5′UTR, weakly enriched in exons and the 3′UTR, and biased against introns. This pattern is highly similar to that observed for singleton MuLV integrations , and three groups associated with repression . The remaining Cluster 5 is more mixed .Another characterization of the resulting clusters is into groups of features for which the behavior is either fairly similar , or groups for which SB and PB behave very differently . Especially striking when observing the differences is that PB is positively associated  with far more features than SB (Cluster 7). Since for both PB and SB, association with the many gene-related features in Cluster 3 is mostly positive, this indicates that SB does prefer gene-rich regions and active genes over heterochromatin, but in these regions, compared to PB, generally avoids regulatory units such as histone modifications and transcription factor bound regions. Interestingly, the single cluster positively associating with SB but negatively with PB (Cluster 7) contains mostly repressive features. Combined, these observations suggest that PB is much more biased to active chromatin than SB, whereas SB is also partly biased towards more repressed chromatin.The scale-based approach reveals that the sign of association can change across different scales. For example, SB shows negative association with some of the features in Cluster 3 on a small scale, but a positive association on larger scales. This implies that SB has a bias for larger scale domains containing these features such as Ctr9, H3K79me2 and 5hmC, but within these domains integration sites will generally avoid overlap with these marks.Conversely, association changing from positive to negative for increasing scales is seen for example for PB and Stat3 in Cluster 5. This indicates that PB prefers domains relatively devoid of these features. However, given a PB integration in such a domain, it will be mildly biased towards Stat3.The above observations suggest a hierarchy in target site selection, which is further illustrated by the fact that some features, for both SB and PB, are consistently non-significant at smaller scales . For exaBiases of unselected integration profiles with respect to the macrofeatures are similar across the systems and scales. This suggests that on a large scale, integration bias is regulated in similar ways for both systems, and that this large scale bias is mainly determined by the macrofeatures. However, within a distance of To address this question, we needed to take into account that the features in BANJO Interestingly, the results show that seven macrofeatures are consistently of great importance, i.e. of high-confidence and strongly associating, in both systems . These fInsertional mutagenesis (IM) using retroviruses and transposons is an important tool in the discovery of new putative cancer genes. These elements mutate the genome by inserting into the host DNA. Mutations providing cells with a proliferative and/or survival advantage can cause tumors. Because the integration loci can be retrieved using sequencing, these mutations can act as cancer gene tags, allowing discovery of novel cancer genes, e.g. p-values). Additionally, biases of unselected integrations for intergenic CIS regions . Combined, these observations show that in regions far from genes, unselected integration profiles correlate relatively strongly with CIS profiles, compared to regions close to genes. Therefore, to avoid calling spurious CISs in IM screens, higher statistical stringency should be required for CISs found far from genes.Generally, the orientation biases of CIS integrations for genes and transcripts are much stronger than those of the unselected integrations . This inCombined, these observations suggest that CISs found far away from genes are more likely to be spurious, i.e. arise from the integration bias of the system. Conversely, the observations suggest that true CISs, i.e. CISs arising from selective pressure, are more often found in the vicinity of genes than would be expected based on an unselected integration profile.p-value, the potentially spurious CISs consisted mainly of lower ranking CISs  due to the absence of oncogenes in their genome The main differences between the three systems regarding integration bias are summarized in Recent availability of data describing the three-dimensional architecture of the genome The two transposons were found to have very different integration profiles. Generally, it is unknown to what extent certain sequences cloned into an IM construct affect the integration bias of that construct, which complicates the interpretation of differences observed between systems. However, the construct that was used in this study contained both the SB and PB IRs. Therefore, any difference between the two profiles can only be explained by the IR or the transposon-specific transposase, indicating that the IR and transposase are major defining elements of integration bias.Although the SB and PB profiles are very different, they were shown to share a bias for activating macrofeatures and a bias against repressive macrofeatures. Analysis of a subset of macrofeatures for MMTV suggested that these may also operate as macrofeatures in a wider range of systems. Differences between SB and PB were mostly seen for the microfeatures. Together, these observations support a model where integration sites are selected at two levels . On largFor the current study, the mESC model system was selected because it is the most thoroughly studied model system, with the broadest availability of (epi)genomic datasets. While limited cell type specificities have been demonstrated for retroviral integration profiles In large scale IM screens, identification of overwhelming numbers of CISs is a serious impediment in distinguishing true CISs from spurious ones. In such screens, a true CIS arises through tumorigenic selection, whereas a spurious CIS is defined by the a priori integration bias of the IM system that was used. Our large datasets of unselected integrations can be used as a valuable resource for prioritizing candidate cancer genes emerging from IM screens. As a proof of principle, we showed that a substantial number of CIS regions in three recent IM screens do not contain more integrations than would be expected based on the unselected integration profiles. Although the cell types between PB and SB tumor screens and their corresponding unselected profiles are different, this nevertheless indicates that these CISs can potentially be explained by an a priori integration bias, and therefore likely represent passenger mutations.In conclusion, the large numbers of integrations for three of the main systems used in IM, unselected integrations from cell lines and selected integrations from tumor screens, as well as the wide range of publicly available datasets, has enabled us to assess integration bias at unprecedented resolution, and assess its relation to CIS designation.http://mutapedia.nki.nl.All integration data generated in this study are made available on GTTCCCATGGTACTACTCATATAATACGACTCACTATAGG) and Sau-3A-1 LS (GATCCCTATAGTGAGTCGTATTATAATTTTTTTTTCAAAAAAA) oligos. The ligation reactions were amplified in two (SB) or three (PB) rounds of PCR to generate libraries for high throughput sequencing (for details see mES cells EBRTcH3 expressing the tetracycline-controlled transactivator (tTA) from the endogenous ROSA26 promoter Sequencing was done on an Illumina HiSeq 2000 instrument to obtain single 100 bp reads. The reads contained ends of IRs and the neighboring genomic DNA. The genomic DNA sequences were extracted from sequencing reads, and aligned against mouse genome assembly mm9 using Bowtie 2 CCTATAGTGAGTCGTATTATAATTTTTTTTTCAAAAAAA) and Nla-III US (GTTCCCATGGTACTACTCATATAATACGACTCACTATAGGCATG) oligos. The ligated DNA was cut with Dra-I (New England Biolabs). The ligation reactions were amplified in two rounds of PCR to generate high throughput sequencing libraries (for details see Mm5MT (MMTV producing cells) and NM-Pbabe/2 (NMuMG cells harboring PuroR transgene) were cultured in DMEM/F-12+GlutaMAX-I medium supplemented with serum (10%) and Insulin , each integration in that dataset was matched to 10 random controls. These random controls were subject to a number of criteria. First, specifically for SB and PB, matched controls were restricted to loci containing the system-specific integration motif (TA and TTAA respectively). Second, the distance of the matched control to the nearest restriction site upstream of the integration was required to be the same as that of the integration itself. Third, matched controls were not allowed to fall within ‘unmappable’ regions. Here, unmappable regions were defined in a dataset-dependent manner. Given an integration dataset , the sequence read length To maximize the comparability of the ChIP-seq datasets, they were processed from the sequence read archives as obtained from GEO Bisulfite sequencing reads The RNA-seq reads were processed using Cufflinks The H3K9me2, late replication timing data, and LaminB1 data were downloaded from GEO and processed as in the corresponding publications Gene locations were retrieved from the Ensembl database (release 66). Partially overlapping genes were removed (40%). In case of complete overlap, the larger gene was retained. The remaining genes (For genes (Ensembl release 66), integrations and controls (see above) were counted within genic regions, TSS upstream regions (defined as the union of those regions within 1 kb upstream of a TSS), TSS downstream regions (defined as the union of those regions within 1 kb downstream of a TSS), and everything else. Note that these classes can overlap. Then, the ratio was computed of integration counts versus control counts. This ratio was normalized by multiplication with the ratio of control dataset size and integration dataset size. Then the base 2 logarithm was taken. A similar approach was taken for the transcript-related classes 5′UTR, 3′UTR, exon, and intron.Genes were divided into five quantiles, based on their expression level. For SB and PB, Group 1 consisted of all genes with an FPKM of zero in the RNA-Seq dataset. The remaining genes were divided across four equal quantiles. The NMuMG microarray expression dataset for MMTV was divided according to the same expression quantiles. Subsequently, the same approach as above was taken for determining the numbers of integrations within each of these subsets of genes.Domain definitions were adopted from http://chromosome.sdsc.edu/mouse/hi-c/mESC.norm.tar.gz). For each locus, defined by an integration and a scale, average contact frequencies as a function of distance from that locus, were calculated within a window of 400 kb. The Hi-C t-score, For each feature and integration, a feature score was computed at exponentially increasing distances  from that integration. For all deep sequencing based features, this was done by taking the mean normalized read count within a 200 bp window, from the genome-wide binned read count profiles computed as outlined above. For CpG islands, genes, and TSSs, this score was calculated by taking the negative log2 transformed distance (+1) to the nearest CpG island, gene, or TSS. For the microarray features, this score represented the value of the nearest probe. The Hi-C score was computed as follows. Normalized Hi-C contact frequency matrices (20 kb bins) were downloaded from . Then, all data was discretized into three quantiles. To identify conditional independencies in the discretized data we took the approach of Bayesian networks p-value threshold. CIS regions were defined as those regions where the Gaussian smoothing kernel exceeded the significance threshold, extended on either side with 30 kb . CIS integrations were defined as those integrations from the tumor screen that fell within a CIS region, and log2 ratios of unselected integrations and CIS integrations were calculated as described above, replacing control loci with CIS integrations.For each of the three systems, CISs were called on the tumor screens, using the approach outlined in CISs were called as outlined above. A CIS was defined to be an activating CIS if its peak location was either not within a gene, or within a gene and orientation-wise homogeneous, requiring 90% of integrations falling within a CIS to be of the same orientation.p-values were calculated based on a binomial test. This procedure outlined for genic regions was repeated for the whole genome, and for intergenic regions.For the union of genic regions (+/−100 kb) it was counted how many unselected integrations were found within CIS regions, how many outside CIS regions, and the corresponding numbers of expected unselected integrations in those regions; p-values correspond to true (spurious) CISs. The probability of success for each binomial test was defined as For all CISs, it was determined how many CIS integrations and unselected integrations fell within the corresponding CIS region. CIS regions were defined as above. To determine whether a CIS contained significantly more CIS integrations than unselected integrations, a one-sided binomial test was performed, testing the significance of CISs were annotated with the name of the gene of nearest TSS, where the TSSs were restricted to Ensembl IDs that had corresponding UCSC, EntrezGene, MGI, and UniGene IDs. To determine whether the non-significant CISs tended to be farther away from genes, the genome-wide TSS density was estimated using a Gaussian smoothing kernel (standard deviation 1 Mb), and sampled at 1 kb intervals. Integrations were then mapped to the nearest sampled density estimation point, and a Mann-Whitney U test was performed on selected integrations within CIS regions versus unselected integrations within CIS regions.Figure S1Distribution of non-canonical recognition sequences for PB and SB. The non-canonical recognition sequences are generally relatively similar to the primary motifs and often differ by only one nucleotide, such as CTAA, TTAG, TAAA for PB and TG, CA, and AA for SB.(EPS)Click here for additional data file.Figure S2Sequence logos for all integration datasets, and de novo motif search near integration sites. For a dataset, all integrations were aligned with respect to locus and orientation, and sequence logos were derived for the set of sequences spanning a distance from 15 bp upstream of integrations to 15 bp downstream of integrations. As expected, PB and SB strongly prefer TTAA and TA respectively. This preference is however not exclusive since these motifs account for 93%  and 94%  of the total number of integrations. For SB, in addition to the recognition sequence TA centered around the integrations, a preference for A and T exactly 4 bp upstream and 4 bp downstream can be observed. MMTV is the least sequence specific of the three systems. Also, below each sequence logo are shown the top 10 motifs found within 50 bp upstream and downstream of the integration, as determined using the HOMER software for motif discovery (EPS)Click here for additional data file.Figure S3Gene alignment plots for all systems. For all systems, the blue and red lines refer to unselected integrations. The black lines refer to singleton integrations for all profiles, the black dotted lines to CIS integrations. Note that no unselected MuLV data was available. Therefore, the set of singleton MuLV integrations was used as a proxy for an unselected integration profile.(EPS)Click here for additional data file.Figure S4Distribution of integrations across genes and transcripts. Distribution of A) unselected integrations, and B) CIS integrations across genes and transcripts. For genes, we distinguish between integrations within genes (genic), within 2 kb upstream of the TSS (TSS_upstream), within 2 kb downstream of the TSS (TSS_downstream), and other integrations (other). For transcripts, we distinguish between integrations in 5′UTR, 3′UTR, exons and introns. The color scale blue-gray-red represents increasing numbers of integrations, relative to expected (left), or the integration orientation bias relative to genes, from antisense to sense (right). Associations that are not significant Click here for additional data file.Figure S5t-score , computed on rank-normalized feature values, visualized on a blue-gray-red scale from negative to positive t-scores. A positive (negative) t-score for a certain feature Orientation bias of integrations with respect to various genome-wide features. Orientation bias with respect to various (epi)genomic features for A) all integrations, B) integrations in sense orientation relative to the nearest TSS, and C) integrations in antisense orientation relative to the nearest TSS. Measure of association is a normalized (EPS)Click here for additional data file.Figure S6t-scores is calculated, and 0.5 is subtracted. The resulting score represents the overall orientation bias of the system with respect to the features, positive (negative) values implying inserting generally oriented away (toward) regions of high signal. Reported p-values are based on two-sided binomial tests and FDR-corrected. This shows that the general preference for inserting oriented towards regions of high signal can not be explained by an orientation bias of the integrations relative to genes in the case of SB, and only partly in the case of PB.Overall orientation bias of integrations with respect to (epi)genomic features. For each heatmap and system in the previous figure, the fraction of positive feature (EPS)Click here for additional data file.Figure S7t-score, computed on rank-normalized feature values, visualized on a blue-gray-red scale from negative to positive t-scores. Associations that are not significant  t-score for a certain scale MMTV macrofeatures. Association of the unselected MMTV integrations with a limited set of macrofeatures across different scales, showing that this set of features also behave as macrofeatures not only in SB and PB, but also in MMTV, an unrelated species. Measure of association is the (EPS)Click here for additional data file.Figure S8t-scores were computed for feature scores at integration loci vs. feature scores at control loci, and plotted in the t-scores in the Comparing SB and PB integration profiles to epigenetic features in mEFs and mESCs. mEF data was obtained from (EPS)Click here for additional data file.Figure S9Experimental design. Generation of unselected integration datasets for transposons and MMTV. A) A construct with IRs for both SB and PB and the coding sequence for eGFP driven by CMV promoter was co-transfected in mouse ES cells with either SB or PB transposase. After 48 hr of transfection, (EPS)Click here for additional data file.Table S1List of statistical procedures used in the main text.(XLS)Click here for additional data file.Table S2p-values associated with statistical tests performed for FDR-corrected (XLS)Click here for additional data file.Table S3Macrofeatures and microfeatures.(XLS)Click here for additional data file.Table S4p-values in ‘pvalue’ were calculated by a one-sided binomial test of ‘nC’ successes in ‘nC’+‘nU’ trials, and corrected for multiple testing (FDR). Low (high) p-values correspond to true (spurious) CISs. The probability of success for each binomial test was defined as SB true and spurious CISs. Column name ‘nU’ refers to the number of unselected integrations within the CIS region on chromosome ‘chr’, starting at ‘start’ and ending at ‘end’. ‘nC’ refers to the corresponding number of CIS integrations. The (XLS)Click here for additional data file.Table S5p-values in ‘pvalue’ were calculated by a one-sided binomial test of ‘nC’ successes in ‘nC’+‘nU’ trials, and corrected for multiple testing (FDR). Low (high) p-values correspond to true (spurious) CISs. The probability of success for each binomial test was defined as PB true and spurious CISs. Column name ‘nU’ refers to the number of unselected integrations within the CIS region on chromosome ‘chr’, starting at ‘start’ and ending at ‘end’. ‘nC’ refers to the corresponding number of CIS integrations. The (XLS)Click here for additional data file.Table S6p-values in ‘pvalue’ were calculated by a one-sided binomial test of ‘nC’ successes in ‘nC’+‘nU’ trials, and corrected for multiple testing (FDR). Low (high) p-values correspond to true (spurious) CISs. The probability of success for each binomial test was defined as MMTV true and spurious CISs. Column name ‘nU’ refers to the number of unselected integrations within the CIS region on chromosome ‘chr’, starting at ‘start’ and ending at ‘end’. ‘nC’ refers to the corresponding number of CIS integrations. The (XLS)Click here for additional data file.Table S7Amplification of samples for mapping integrations by high throughput Illumina sequencing. For PCR-1 Thermo-Start Taq DNA polymerase  was used whereas for all other PCR reactions Phusion High-Fidelity DNA Polymerase  was used. (DOC)Click here for additional data file.