FUS gene (Fused in Sarcoma) are known to cause Amyotrophic Lateral Sclerosis (ALS), a neurodegenerative disease affecting upper and lower motoneurons. The FUS gene codes for a multifunctional RNA/DNA-binding protein that is primarily localized in the nucleus and is involved in cellular processes such as splicing, translation, mRNA transport and DNA damage response. In this study, we analyzed pathophysiological alterations associated with ALS related FUS mutations (mFUS) in human induced pluripotent stem cells (hiPSCs) and hiPSC derived motoneurons. To that end, we compared cells carrying a mild or severe mFUS in physiological- and/or stress conditions as well as after induced DNA damage. Following hyperosmolar stress or irradiation, mFUS hiPS cells recruited significantly more cytoplasmatic FUS into stress granules accompanied by impaired DNA-damage repair. In motoneurons wild-type FUS was localized in the nucleus but also deposited as small punctae within neurites. In motoneurons expressing mFUS the protein was additionally detected in the cytoplasm and a significantly increased number of large, densely packed FUS positive stress granules were seen along neurites. The amount of FUS mislocalization correlated positively with both the onset of the human disease  and the maturation status of the motoneurons. Moreover, even in non-stressed post-mitotic mFUS motoneurons clear signs of DNA-damage could be detected. In summary, we found that the susceptibility to cell stress was higher in mFUS hiPSCs and hiPSC derived motoneurons than in controls and the degree of FUS mislocalization correlated well with the clinical severity of the underlying ALS related mFUS. The accumulation of DNA damage and the cellular response to DNA damage stressors was more pronounced in post-mitotic mFUS motoneurons than in dividing hiPSCs suggesting that mFUS motoneurons accumulate foci of DNA damage, which in turn might be directly linked to neurodegeneration.Mutations within the  TARDBP gene was identified as a major component of ubiquitinated aggregates in ALS and frontotemporal lobar degeneration (FTLD)  is a fatal neurodegenerative disorder characterized by the selective demise of upper and lower motoneurons (MNs). In most patients, the disease is sporadic and the pathogenesis is unknown. The identification of genetic causes  gave another scope into understanding familial ALS (fALS). In 2006, the RNA binding protein TAR DNA-binding protein 43 (TDP-43) encoded by the n (FTLD) . The iden (FTLD) . FUS is n (FTLD)  but is an (FTLD) . FUS seen (FTLD) . Furthern (FTLD) . In thisn (FTLD) . Similarn (FTLD) .In affected patients carrying FUS mutations, FUS is partially or totally excluded from the nucleus and forms cytoplasmic inclusions in neurons  of the brain and spinal cord . In someUp to now, the exact pathomechanism induced by mutated FUS (mFUS) in ALS still remains unclear, but there is evidence that under physiological conditions FUS is involved in DNA damage responses (DDR) as well as RNA processing and transcription. In this respect, it was shown that FUS is a component of DDR machinery since wild type (WT)-FUS is recruited to DNA damage foci in neurons and interacts with histone deacyclase-1 (HDAC1), a chromatin modifying enzyme involved in DDR signaling and DNA repair. Mutations in FUS, however, presented a weaker HDAC1-interaction leading to an impaired DDR in neurons. In addition, neuropathological examination revealed increased DNA damage in cortical and spinal neurons from fALS patients expressing mFUS variants . Along tThe cellular and molecular analysis of ALS pathogenesis has been impeded by the absence of suitable cell model systems, in which mutated proteins are usually overexpressed. Post mortem tissues are also difficult to obtain and mouse models have been useful but they do not reflect the complete pathology seen in humans. In this study, we took advantage of the generation of human induced pluripotent stem cells (hiPSCs) by reprogramming somatic cells  and deriWe addressed whether the above described pathophysiological phenotypes associated with mFUS, i.e., FUS mislocalization, SG formation and enhanced DNA damage are present in hiPSCs derived from three different fALS-FUS patients under physiological and stress conditions as well as after induced DNA damage. To this end hiPS cell lines were generated from fALS-FUS patients harboring three different endogenous mutations  in the FUS C-terminus.de novo FUS mutation Asp502Thrfs∗27  (day E14.5)  was performed as previously described .Viral particles were produced according to published protocols. For more detailed information, see octamer-binding transcription factor 4 (OCT4), sex determining region Y-box 2 (SOX2), Kruppel-like factor 4(KLF4), and c-MYC  supplemented with 8 μg/ml polybrene  on two subsequent days. Afterwards, keratinocytes were detached with TrypLE Express  for 10 min at 37°C and transferred to previously irradiated (30Gy) rat embryonic fibroblasts feeder cells. Keratinocytes were cultured in hiPSC medium consisting of knockout/DMEM supplemented with 20% knockout serum replacement, 2 mM GlutaMAX, 100 mM non-essential amino acids , 1% Antibiotic–Antimycotic, 100 mM β-mercaptoethanol , 50 mg/ml vitamin C, and 10 ng/ml fibroblast growth factor (FGF-2)  in a 5% O2 incubator. Medium was changed daily. After 3–5 days small colonies appeared with typical hiPSCs morphology. Around 14 days later, hiPSC colonies had the appropriate size for mechanical passaging and were transferred onto irradiated MEFs  and further cultivated with hiPSC medium. After one passage hiPSC colonies were mechanically picked and transferred to feeder free plates and maintained with mTReSR1 medium . For splitting, hiPSCs colonies were incubated with dispase (StemCell Technologies) for 5–7 min at 37°C and subsequently detached using a cell scraper. For more detailed methods see Human keratinocytes were reprogrammed as previously described  using a nd c-MYC . BrieflyThe reprogramming of the human fibroblasts was performed essentially as described by in vitro differentiation, hiPSC colonies were mechanically lifted by dispase (Stemcell Technologies) digested and transferred in T75 low-attachment flasks . Embryoid body (EB) formation was performed in suspension in knockout DMEM supplemented with 2 mM GlutaMAX, 20% Knockout Serum Replacement , 1% Antibiotic-Antimycotic (Invitrogen), 100 μM nonessential amino acids (Invitrogen), and 100 μM ß-mercaptoethanol (Invitrogen) for 10 days. Afterwards, EBs were plated on 35 mm dishes  and kept in culture for up to 14 days. Differentiated EBs were stained 1:1000 chicken anti-Tubulin beta-III ; 1:150 mouse anti-Actinin (Sigma Aldrich) and 1:100 goat anti-AFP . Alexa fluor secondary antibodies were used from Invitrogen.The pluripotency tests and the germ-layer-specific detection was performed as previously described  or done Supplementary Figure S1C).Karyograms of all generated iPSC lines were analyzed to exclude chromosomal aberrations after reprograming. Chromosome preparation of hiPS cells was performed according to standard procedures . BrieflyMotoneuronal differentiation of hiPSCs was performed as previously described by Clustered regularly interspaced short palindromic repeats (CRISPR) technology was used to generate an isogenic CNTL cell line derived from R495QfsX527 cell line, by inserting in position 1483 a cytosine (C), which is deleted in the patient cells, and consequently correcting the mutation.c.1483insC, following CRISPR , the sequences upstream and downstream of the insertion site were sequenced to exclude additional alterations. Except for the cytosine insertion (highlighted in red) and the two proto-spacer adjacent motifs , the alignment of the sequencing results of genomic CNTL and R495QfsX527c.1483insC revealed no further alterations within the fragment .Two sgRNAs  and a vector  carrying the template  were designed and transfected into a single iPS cells via human stem cell nucleofection kit using an AMAXA nucleofector . Cells were screened using a PCR based approach. After generation of isogenic CNTL line R495QfsX527® 488, red Alexa Fluor® 568, magenta Alexa Fluor® 647 . Glass cover slides were mounted with ProLong Gold Antifade reagent with DAPI (Invitrogen). Images were analyzed with a fluorescent microscope  using a CCD camera  and the axiovision software (Zeiss).Immunochemical analysis were performed as previously described in standard protocols . HiPSCs Western Blots were performed as previously described . ProteinControl and mFUS iPSCs were seeded on hESC-qualified-matrigel coated 13 mm glass cover slides  1 day before treatment. To ensure a final concentration of 0.3 M, Sorbitol  was dissolved directly into pre-warmed mTeSR1 medium . Media was sterile-filtered and added to the cells followed by immediate incubation at 37°C for 30 min. Afterwards treated cells were fixed for immunostaining. In order to remove dead cells, medium was changed 1 h before treatment.To analyze enhanced sensitivity of patient derived iPSCs to DNA damage, DNA breaks were induced by γ-irradiation (0.5 Gy) in control and mFUS hiPSC cell lines. Cells were lifted via hESC-dispase digestion (Stemcell Technologies) and transferred in suspension to a falcon tube. HiPSCs were then exposed to radiation and carefully seeded on hESC-qualified-matrigel coated 13 mm glass cover slides for immunochemical analysis. Subsequently, hiPSCs were analyzed after 24 h cultivation in mTeSR1 medium by quantifying cell colonies presenting normal/healthy or fragmented/apoptotic nuclei. To remove dead cells medium was changed 1 h before the fixation. All existing colonies from three cover slides were analyzed per experiment and condition. Subsequently, to determine the amount of double strand breaks after irradiation the samples were immunostained for rabbit anti-γH2A.X . To analyze, quantification of DNA damage foci positive cells were counted. Each experiment was repeated three times.To analyze sensitivity of motoneurons expressing WT FUS or mFUS the neuronal spheres were plated on 35 mm dishes and kept in culture for 21/42 days. Medium was changed two times per week. The DNA breaks were induced by γ-irradiation (0.5 Gy) in control and mFUS MNs. Cells were fixed and analyzed after 24 h of cultivation by the quantification of DNA damage foci positive cells.The comet assay was performed according to a standard protocol . HiSPC wn = 3) 3–10 representative images were taken randomly. Cells containing FUS positive inclusions under each condition (±sorbitol or ±irradiation) and the total number of cells labeled with DAPI were counted by using Image J Software1. Then, the mean of the positive cells/total number of cells ratio was calculated for each experiment (n = 3) and presented relative to untreated CNTL. Each specific type of colony  ±irradiation was counted and expressed as a percentage of the total number of colonies relative to untreated CNTL. Cells positive for γH2A.X foci (either iPSC or MN) were counted and the number expressed as the percentage of the total number cells, in the same way as described for the number of cells containing FUS positive inclusions. According to the parameters, values were statistically analyzed either by one way ANOVA with a Bonferroni post hoc test to compare different cell lines or a unpaired t-test to compare two different conditions as indicated in each figure.For counting hiPS cells on each coverslip  was used, which has previously been shown to closely match human assignments and reduce human inconsistencies in foci detection . FUS, whp = 0.05.All fluorescence images were obtained with an upright Axioscope microscope equipped with a Zeiss CCD camera  using Axiovision software (Zeiss). All statistical analyses were performed using GraphPad Prism 5  software. Results are represented as mean values ± SEM. Statistical significance levels were set to All procedures with human material were in accordance with the ethical committee of the Ulm University (Nr. 0148/2009 or 265/12) and Technische Universität Dresden (EK45022009) and in compliance with the guidelines of the Federal Government of Germany (Nr. O.103). The use of human material was approved by the Declaration of Helsinki concerning Ethical Principles for Medical Research Involving Human Subjects . All parFUS gene. FUS1 harbored a “mild” missense mutation (R521C) with a late onset (57 years old), family history of ALS (∗27). This mutation leads to an early juvenile onset of ALS (19 at disease onset). All patients had a spinal onset, to a bulbar disease progression for FUS1 and FUS2. Patient-derived cell lines were compared with three control cell lines  derived from neurologically healthy volunteers . Moreover, we corrected the mutation of the FUS2 line by CRISPR technology  to test for mutation specific phenotypes (isogenic CNTL cell line R495QfsX527c.1483insC) (Table 1). All established hiPS cell lines (Asp502Thrfs∗27 is shown as an example) were checked for the expression of characteristic pluripotency markers including the nuclear factors OCT4, SOX2, NANOG and SSEA-4,TRA-1-60 and TRA-1-81 . Similarly, the generated iPSC line R495QfsX527c.1483insC was positive for specific pluripotency markers. R495QfsX527c.1483insC expressed the nuclear factors  SOX2, OCT4 and NANOG and the surface markers  SSEA-4, TRA1-60 and TRA1-81. The generated cell line showed high endogenous mRNA levels for SOX2, OCT4, and NANOG, whereas KLF4 showed lower expression levels . In addition, cell lines presented normal karyotypes as shown in Supplementary Figure S1D.We generated and differentiated patient-derived cells lines  carrying three different endogenous mutations  within the y of ALS , and a dy of ALS . This muublished . To confSupplementary Figure S1E). Low levels of cytoplasmic FUS were detectable in some cells of the control cell lines CNTL1 and CNTL2 as well as in the cell lines carrying the R521C and R495QfsX527 mutations, but to a negligible degree (see arrowheads). Isogenic CNTL cell line R495QfsX527c.1483insC confirmed this observation and showed also predominantly nuclear FUS . However, even at this early iPSC stage, some cells within the iPSC colonies from the mFUS cell line Asp502Thrfs∗27 exhibited a stronger cytosolic FUS mislocalization when compared to the other cell lines .In hiPSCs of control cell lines expressing WT FUS and hiPSC expressing mFUS, FUS was localized predominantly in the nucleus . In addition, in unstressed control cells the SG marker TIA1 was found within nuclei (red arrowheads) and in some cells in the cytoplasm . In patient-derived cell lines expressing mFUS R521C, R495QfsX527 and Asp502ThrfS∗27 the TIA1 signal was also mainly nuclear without any cytoplasmic localization . After hyperosmolar stress, however, FUS was seen in the cytoplasm forming several granules . This was seen in the control cell line as well as in all mFUS expressing cell lines. FUS+ granules perfectly co-localized with TIA1 (red arrowheads) indicating a specific localization of FUS to SGs . Quantification of cells containing FUS+ cytoplasmic inclusions showed that in unstressed hiPSCs lines a very low proportion of cells displayed FUS aggregation and there were no significant differences between control and patient-derived cell lines . After treatment with sorbitol, all cell lines responded to the treatment by having a significant proportion of cells containing FUS+ granules. This increase was 5.65 ± 0.42 fold in the control cell line CNTL and 6.02 ± 0.61, 9.50 ± 0.81, and 11.8 ± 1.12 fold in the mFUS cell lines R521C, R495QfsX527 and Asp502Thrfs∗27, respectively  . The comparison of FUS/TIA1 inclusions of mFUS lines with controls after sorbitol treatment revealed a highly significant increase .Hyperosmolar stress was induced by exposing the hiPSCs to 0.3 M sorbitol for 30 min. Unstressed hiPSCs showed typical stem cell morphology with oval nuclei lying close to each other. Cells expressing WT or mFUS displayed a predominantly nuclear FUS localization in the absence of sorbitol or following mock-treatment , which are proportional to the amount of DNA breaks (p ≤ 0.05) and R495QfsX527 (2.464 ± 0.107 p ≤ 0.01). No increase was seen in the control cell line (1.4 ± 0.1)  indicating that in the mFUS cell lines a higher number of DNA breaks was still detectable after 24 h of repair time . The mFUS Asp502Thrfs∗27 line presented no significant increase, possibly due to a high amount of apoptotic cells as shown below. The specificity of this effect could be confirmed by the isogenic control line R495QfsX527c.1483insC. Quantification of γH2A.X positive cells before and 24 h after irradiation revealed that R495QfsX527c.1483insC  as well as R495QfsX527  showed an increased number of γH2A.X+ cells 24 h after DNA damage. This increase was much stronger in R495QfsX527 (p ≤ 0.001) expressing mFUS. Even without the induction of DNA damage, an increased amount of γH2A.X positive cells could be detectable for R495QfsX527  compared to R495QfsX527c.1483insC . Next, we classified the hiPSC colonies according to morphological alterations after irradiation as being: (I) “healthy” , (II) apoptotic colonies, (III) colonies with differentiated cells, and (IV) colonies forming clumps due to poor adherence . Twenty-four hours after irradiation, all mFUS cell lines responded with a significant increase in apoptotic colonies . The significant increase in the number of apoptotic colonies after irradiation was 1.3 ± 0.2 fold (p ≤ 0.01) in the control cell line and 1.0 ± 0.1 (p ≤ 0.01) and 2.0 ± 0.2 (p ≤ 0.01) fold in R521C and R495QfsX527, respectively. A highly significant increase  of apoptotic colonies was seen in the Asp502Thrfs∗27 line, in which most of the colonies were apoptotic after irradiation . Unpaired t-test was used to compare non-irradiated versus irradiated cells within each group (red asterisks) and a one way ANOVA with Bonferroni post hoc test to compare the different groups. The number of differentiated and clumping colonies after irradiation was similar in the cell lines (data not shown). Finally, we evaluated the amount of DNA breaks by the comet assay. To this end, single cells of the colonies were γ-irradiated and the assay was performed directly thereafter. The CNTL as well as the mFUS cell line showed an increase in tail intensity after irradiation, however, no significant differences were seen between these lines .Next we analyzed the susceptibility of hiPSCs specifically to DNA damage and repair by the induction of DNA breaks (γ-irradiation). DNA damage in hiPSC colonies was measured by immunolabeling γH2A.X positive foci . To quantify the amount of caspase-3 activation, caspase-3 and the two cleaved fragments (cl.casp-3 17 kDa and cl.casp-3 19 kDa) were analyzed via western blot 1 and 24 h after irradiation. The amount of full-length caspase-3 slightly decreased 1 and 24 h after irradiation for CNTL  and mFUS . Unpaired t-test was performed to compare untreated versus irradiated cells (red asterisks). Analysis of cleaved caspase-3 revealed a significant increase in cl.casp-3 (17 kDa) and cl.casp-3 (19 kDa) 1 h after the induction of DNA damage in mFUS Asp502ThrfS∗27 compared to the CNTL cell line. This increase after 1 h was 7.5 ± 0.52 fold in the CNTL cell line for cl.casp-3 (17 kDa) and 5.08 ± 0.3 fold for cl.casp-3 (19 kDa). Cell line Asp502Thrfs∗27 showed a 4.2 ± 0.1 fold increase in cl.casp-3 (17 kDa) and 6.59 ± 0.06 fold in cl.casp-3 (19 kDa) .To confirm apoptosis induced by irradiation we also immunostained for caspase-3 and found that caspase-3 positive cells remained low in colonies from control cell line before and after irradiation but increased substantially in cells derived from the Asp502Thrfs∗27 presented a certain degree of cytoplasmic FUS . TIA1 staining was also mainly nuclear with some cytoplasmic staining . After DNA damage we found a significant increase in the number of cells that contained cytoplasmic FUS forming aggregates that were positive for TIA1 . In all mFUS cell lines, the number of cells with FUS/TIA1 cytoplasmic inclusions was significantly increased after irradiation. The most pronounced effect was seen in the cell line with the Asp502Thrfs∗27 mutation with an significant increase of 9.5 ± 0.1.4 fold (p ≤ 0.001) as opposed to increases of 3.3 ± 1.3 (p ≤ 0.01) and 2.7 ± 0.5 (p ≤ 0.01) fold in R521C and R495QfsX527, respectively and no significant increase in the control cell line  . Unpaired t-test was used to compare non-irradiated versus irradiated cells within each group (red asterisk), one way ANOVA with Bonferroni post hoc test to compare different lines).In parallel, hiPSCs were also co-immunostained for FUS and TIA1 before and 24 h after irradiation. In non-irradiated cells, FUS was mainly distributed in the nuclei, however, the cell line Asp502ThrfsSupplementary Figure S3A). On day 21 of motoneuron differentiation all cell lines developed a dense neuronal network and expressed the early neuronal protein marker beta-III tubulin . At this stage, HB9 and Islet-1 (transcription factors specific for MNs) as well as ChAT (characteristic for cholinergic MN) were also detectable . At day 42, the neuronal network had matured in all cell lines and contained dense NF-H positive filaments .Motoneurons differentiated from hiPSCs were tested for spinal MN differentiation by immunostaining with antibodies directed against TUJ1 (beta-III tubulin), NF-H, HB9, Islet-1 (ISL-1), and ChAT . Similar to controls, neurons carrying the benign missense mutation R521C exhibited an exclusive nuclear localization of the FUS protein. However, cytoplasmic FUS was detectable in some MNs expressing mFUS with the frame shift mutation R495QfsX527. In contrast, almost every cell harboring the most severe mutation Asp502Thrfs∗27 contained cytoplasmic FUS . To visualize motoneuronal cell bodies as well as dendrites, differentiated cells were stained for MAP-2 .Within developing and mature MN, FUS was predominantly localized in the nucleus of all 21-day-old MNs , in patient-derived cells, the protein was in addition detectable in the cytoplasm of almost every neuronal cell carrying mFUS R521C, R495QfsX527 and Asp502Thrfs∗27. In particular, MNs with the mFUS R521C displayed some FUS protein in the cytoplasm as well as along dendrites . MNs carrying the malignant FUS mutations R495QfsX527 or Asp502Thrfs∗27 showed a signal for FUS in cytoplasm and FUS inclusions within the cytoplasm and along the MAP-2 or NF-H positive neurites . FUS+ punctae were especially seen in neurites of patient-derived MN, especially Asp502Thrfs∗27 derived MNs had an aberrant and an increased amount of large FUS+ punctae . In contrast to MNs expressing the mFUS variant R495QfsX527 the CRISPR corrected cell line R495QfsX527c.1483insC showed only nuclear FUS in differentiated MN .At day 42 of neuronal differentiation, FUS was localized to the nucleus of control cells . To quantify the amount of cytoplasmic and nuclear FUS, we measured the intensity  of FUS in the cytoplasm and/or in the nuclei of NF-H positive MN . In controls the homogenous cell population showed low values for the cytoplasm and high intensities for the nuclei. In MN carrying the Asp502ThrfS∗27 mutation two separated MN populations could be identified according to their nuclear staining. They either had a very reduced nuclear FUS value  or a significantly higher intensity  . In the cytoplasm, there was a significant higher amount of cytoplasmic FUS in Asp502ThrfS∗27  compared to CNTL .Interestingly, several mature 42-day-old MN derived from cell line Asp502Thrfs∗27 MN were further characterized by a correlation analysis taking cytoplasmic and nuclear intensity of each single cell into account. In contrast to CNTL the mFUS cells were splitted into two populations with high cytoplasmic intensity and either low nuclear intensity  or high nuclear intensity . The thorough analysis of the first subgroup (∗1) displaying a high degree of cytoplasmic FUS were found to be also displacing large FUS+ granules along the neurites. Neurites derived from healthy CNTL showed 5.5 ± 0.8 FUS+ granules per 10 μm whereas Asp502Thrfs∗27 presented significantly more FUS+ granules  . In addition, in CNTLs the average size of the FUS+ granules was 0.17 ± 0.004 μm2, whereas the granules found in the mFUS Asp502Thrfs∗27 line where significantly larger (0.4 ± 0.013 μm2) (p ≤ 0.001) . Unpaired t-test was used to compare CNTL versus mFUS cells.The two populations of Asp502ThrfS+ granules along mature neurites, the intracellular distribution of TIA1 positive SGs in 42 days old MN was investigated. Similar to cells expressing WT FUS, MNs carrying FUS mutations exhibited a predominantly nuclear expression of TIA1. In addition, TIA1+ inclusions were found along the neurites and partially co-localizing with FUS in control cell lines as well as in mFUS MN . In cells expressing mFUS, however, there were notably more and larger TIA1/FUS positive granules than in control MNs. Neurites derived from CNTL cell lines showed an average of 9.0 ± 0.8 TIA1+ SGs per 10 μm length whereas Asp502Thrfs∗27 presented a significant increase by having an average of 16.3 ± 1.05 TIA1 granules per 10 μm (p ≤ 0.001) . The ratio of co-localization between FUS and TIA1 granules showed no differences (p > 0.05) between CNTL (46.6 ± 3.2%) and mFUS Asp502ThrfS∗27 (42.1 ± 4.2%) , but in line with the elevated number of FUS+ granules per se there is a significant increase of FUS positive TIA1 foci in cells expressing Asp502ThrfS∗27 (5.9 ± 1.0) compared to CNTL  .In order to further characterize these FUS+ labeling . Interestingly, we found that even in non-stressed 21 days old MNs the number of cells containing γH2A.X+ foci was significantly higher in the mFUS Asp502Thrfs∗27 line (5.0 ± 0.6 fold) compared to control . Both neuronal lines responded to irradiation by showing a significant increase in cells containing positive γH2A.X foci in the nuclei . This increase of γH2A.X positive neurons after irradiation was not significantly different between the two cell lines. Similar to young cells, the non-stressed 42 days old MNs expressing Asp502Thrfs∗27 exhibited a significant increased number of γH2A.X+ cells (1.9 ± 0.1 fold) compared to control . After irradiation, all lines responded with an increase of γH2A.X positive neurons . Unpaired t-test was used to compare non-irradiated versus irradiated within each cell line. However, different from the iPSCs (Asp502Thrfs∗27), the increase of DNA damage positive cells after irradiation was significantly increased for stressed mFUS compared to stressed CNTL  . A comparable result was seen using the isogenic control line R495QfsX527c.1483insC underlining the assumption that mFUS is most likely responsible for the observed phenotype. Here, an effect of irradiation was only seen for R495QfsX527c.1483insC showing an increase of 1.8 ± 0.12  24 h after irradiation. Again, differences between the mutated and rescued cell line was detectable even before and after irradiation of mature motoneurons. Compared to non-irradiated R495QfsX527c.1483insC, non-irradiated R495QfsX527 showed higher numbers of γH2A.X+ cells . Although irradiation had no obvious effect on R495QfsX527 (2.7 ± 0.2), significantly more γH2A.X+ cells were detectable compared to irradiated R495QfsX527c.1483insC  .As already performed for iPS cells, we next analyzed the amount of DNA-damage in young (21 days old) and mature (42 days old) MNs 24 h after γ-irradiation. MNs were classified either negative (I) or positive (II) for DNA damage foci as indicated by γH2A.Xp ≤ 0.001). DNA damage levels were generally increased in mFUS cells. Twenty-one days old Asp502Thrfs∗27-MN exhibited 10.4 ± 1.4% γH2A.X positive cells; whereas in 42 days old MN 22.9 ± 1.835% of the cells were positive for foci of DNA damage  . To study the effect of DNA-damage on FUS localization in young MN, we also stained for FUS. In young CNTRL-MN, FUS is localized in the nuclei, after irradiation FUS could partially be seen in the cytoplasm . After irradiation of young mFUS-MN cultures large FUS clusters were detected in the cytoplasm in nearly all neurons compared non-stressed cells .Finally, we analyzed the number of DNA damage foci during motoneuronal maturation. Young 21 days old CNTL-MN showed only a 2.0 ± 0.6% of γH2A.X positive cells whereas 11.6 ± 1.0% γH2A.X positive cells were present in mature 42 days old cultures . Different studies have already shown the association of FUS function in DNA repair and genome instability. KO FUS mice die at birth and show genomic instability . In addition, in the cell line harboring the most severe mutation, with an early juvenile onset, we could identify two subgroups of MN with high amounts of cytoplasmic FUS and either low or high nuclear FUS levels. In these MN the degree of punctuated FUS along the neurites was much stronger and abundant compared to controls. Furthermore, some of these large FUS punctae co-localized with SG marker, which were also present along the neurites. This indicates, that in mFUS, FUS is spontaneously aberrantly distributed along the axons, as opposed to iPSCs, in which as stressor was needed to see changes in FUS mislocalization and SG formation  the cytoplasmic FUS mislocalization, (II) the formation of FUSclusions  as well clusions . MoreoveFUS gene leading to a cytoplasmic mislocalization of mFUS in MN depending upon the severity of the mutation that correlates with the onset of the disease . The second hit is an external factor such as DNA damage or stress. In young control cells, FUS was mislocalized only after the induction of DNA damage, in mFUS large FUS positive inclusions were seen. A strong FUS mislocalization was also detectable in older mFUS MNs. We hypothesize that this is due to neuronal maturation/aging and a later event during ALS pathology. In hiPSC derived MNs only a few larger FUS+ inclusions were detected giving rise to the supposition that possibly a third hit is needed to increase the size and toxicity of FUS inclusions that finally induce neuronal degeneration.On the basis of a two hit hypothesis, we conclude that the first hit is the modification of the TMB, MD, and JH designed and outlined the study. MD and JH carried out all experiments and analyzed the images. SL, SP, A-KL, A-KH, MK, and AH reprogrammed and characterized the hiPSC cell lines. GB analyzed the karyotypes. JB and GS performed and analyzed the results from the comet assay. JD and CJ contributed to the image analysis and created a program to analyze protein co-localization. TMB, MD, and JH jointly wrote the manuscript. ACL, AMH, JHW, and AH provided the keratinocytes and the fibroblasts and the participants clinical information.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Neuroblastoma (NB), the most common extracranial solid tumor of childhood, causes death in almost 15% of children affected by cancer. Treatment of neuroblastoma is based on the combination of chemotherapy with other therapeutic interventions such as surgery, radiotherapy, use of differentiating agents, and immunotherapy. In particular, adoptive NK cell transfer is a new immune-therapeutic approach whose efficacy may be boosted by several anticancer agents able to induce the expression of ligands for NK cell-activating receptors, thus rendering cancer cells more susceptible to NK cell-mediated lysis. Here, we show that chemotherapeutic drugs commonly used for the treatment of NB such as cisplatin, topotecan, irinotecan, and etoposide are unable to induce the expression of activating ligands in a panel of NB cell lines. Consistently, cisplatin-treated NB cell lines were not more susceptible to NK cells than untreated cells. The refractoriness of NB cell lines to these drugs has been partially associated with the abnormal status of genes for ATM, ATR, Chk1, and Chk2, the major transducers of the DNA damage response (DDR), triggered by several anticancer agents and promoting different antitumor mechanisms including the expression of ligands for NK cell-activating receptors. Moreover, both the impaired production of reactive oxygen species (ROS) in some NB cell lines and the transient p53 stabilization in response to our genotoxic drugs under our experimental conditions could contribute to inefficient induction of activating ligands. These data suggest that further investigations, exploiting molecular strategies aimed to potentiate the NK cell-mediated immunotherapy of NB, are warranted. In particular, systemic chemotherapy administered to all patient groups , before surgery (neoadjuvant chemotherapy) or after surgery (adjuvant chemotherapy), represents the main treatment [Neuroblastoma (NB), the most aggressive extracranial solid tumor occurring in childhood, remains a challenging malignancy . In spitreatment . Of noteThe NK cell-based immunotherapy of cancer, including NB, is attracting more and more interest , 5. The The genotoxic effect typical of several anticancer agents induces DNA damage response (DDR), the pathway most extensively associated with the induction of the expression of ligands for NK cell-activating receptors –14. AtaxOf note, several chemotherapeutic agents have been reported to function as potent immune adjuvants  and to bATM, ATR, CHEK1, and CHEK2 genes evaluated in these cell lines was partially altered, suggesting an impaired DDR pathway. Of note, other authors reported that the loss of ATM in NB patient samples was associated with poor prognosis [ATM maps on chromosome 11q whose hemizygous deletion, together with the loss of chromosome 1p [MYCN [ALK [To investigate whether DDR caused by genotoxic drugs in NB cells could affect the expression of ligands for NK cell-activating receptors, we tested the effect of chemotherapeutic agents currently used in the treatment of NB. Differently to the reported effect of several drugs on different tumor cell lines , 30, ourrognosis . Interesosome 1p , the gaiosome 1p , and the1p [MYCN  and ALK YCN [ALK , 36, is The NB refractoriness in response to these genotoxic agents, in terms of induction of activating ligands, suggests that these drugs do not function as immune adjuvants and, thus, could not support the NK cell-mediated recognition and lysis of tumor cells. In order to boost NK cell-based immunotherapy of NB, the effect of different molecules should be more extensively investigated.Human NB cell lines were obtained as follows: SK-N-AS, SH-SY5Y, SH-EP, SK-N-SH, SK-N-BE(2)c, and IMR-32 from the American Type Culture Collection (ATCC) and LA-N-5 from the Leibniz-Institut DSMZ. All NB cell lines were characterized by (i) HLA class I typing by PCR-SSP sets (Genovision) according to the instructions of the manufacturer and (ii) array comparative genomic hybridization (a-CGH) and single-nucleotide polymorphism (SNP) array analyses (see below). The human non-small-cell lung cancer cell line A549 was purchased from Sigma-Aldrich. The human erythroleukemia cell line K562 was purchased from ATCC and used as a control target for NK cell functional assays. Cells were grown in RPMI 1640 medium supplemented with 10% FBS (Thermo Fisher Scientific), 2 mM glutamine, 100 mg/ml penicillin, and 50 mg/ml streptomycin (EuroClone S.p.A.). Cisplatin , etoposide , irinotecan , and topotecan (GlaxoSmithKline) were kindly provided by the pharmacy of our institution.The following antibodies for flow cytometry were used: anti-CD107a-FITC (H4A3), anti-CD3-Alexa-700 (UCHT1), anti-CD56-PE-Cy7 (B159), and anti-CD45 (HI30), purchased from BD Biosciences; anti-ULBP1-PE (170818), anti-ULBP2/5/6-PE (165903), anti-ULBP3-PE (166510), anti-MICA (159227), anti-MICB (236511), anti-TRAIL/R2-APC (17908), anti-CD155/PVR-PE (300907), and anti-Nectin-2/CD112-APC (610603), purchased from R&D Systems; W6/32 which recognizes human fully assembled MHC class I heavy chains; and goat F(ab′)2 Fragment anti-mouse IgG FITC  for flow cytometry. Apoptosis of tumor cells was evaluated with APC-conjugated AnnexinV (BD-Pharmingen) and propidium iodide (PI) (Sigma-Aldrich).Flow cytometry was performed on FACSCantoII and analysed by FACSDiva Software (BD Biosciences).ROS production was evaluated in drug-treated NB cell lines by using CellROX Deep Red Reagent  and measured by flow cytometry.Whole-cell extracts were quantified by a bicinchoninic acid assay (Thermo Fisher Scientific), resolved on 8–10% SDS-PAGE and electroblotted. Filters were probed with primary antibodies followed by goat anti-mouse and HRP-conjugated rabbit anti-goat IgG (Jackson). The following antibodies for Western blotting were used: anti-p53 (FL-393) and anti-actin (I-19), purchased by Santa Cruz Biotechnology.http://genome.ucsc.edu, Feb. 2009 release). The quality of the test was assessed on the strength of the QCmetrics values. Polymorphisms (http://dgv.tcag.ca/dgv/app/home) were not included because they were considered normal variants.DNA from NB cell lines was tested by the high-resolution a-CGH and SNP arrays using the 4 × 180K kit (Agilent Technologies) with a mean resolution of approximately 40 kb. SNP-array and oligoarray data were analysed with Genomic Workbench 7.0.40 software (Agilent). Chromosome positions were determined using GRCh/hg19  at 37°C and tested up to 5 days after isolation.Human NK cells were isolated from peripheral blood mononuclear cells (PBMCs) of healthy donors with the RosetteSep NK cell-enrichment mixture method (StemCell Technologies) and Ficoll-Paque Plus (Lympholyte Cedarlane) centrifugation. NK cells were routinely checked for the CD3−CD56+CD45+ subset.A degranulation assay was performed by coculturing NK cells with target cells at a 1 : 1 ratio for K562 and a 1 : 2 ratio for A562 and NB cell lines, for 3 hours, in complete medium, in the presence of anti-CD107a, and in the last 2 hours of GolgiStop (BD Biosciences). Then, cells were stained with anti-CD3, anti-CD56, and anti-CD45, and the expression of CD107a was evaluated by flow cytometry in the CD3t-test. Normalized values were analysed for correlation by the regression analysis using GraphPad software. p values not exceeding 0.05 were considered to be statistically significant.Data values were evaluated by two-tailed paired Student's in vitro the following NB cell lines: SK-N-AS, SH-SY5Y, SH-EP, SK-N-SH, SK-N-BE(2)c, LA-N-5, and IMR-32. Of note, the status of p53, a key regulator of the induction of some NKG2D ligands [μM, etoposide at 0.1 μM, irinotecan at 1 nM, and topotecan at 10 nM induced apoptosis of all NB cell lines after 48 hours of treatment, with the exception of SK-N-AS treated with the first three drugs and SK-N-BE(2)c treated with all four drugs (Supplementary We investigated whether drugs used in the treatment of NB could affect the expression of ligands for NK cell-activating receptors. The genotoxic drugs cisplatin (DNA binder), etoposide (topoisomerase II inhibitor), irinotecan, and topotecan (topoisomerase I inhibitors) were used to treat  ligands , 20, is  ligands . These c ligands . Cell viThese data suggest that four drugs commonly used for chemotherapy of NB patients, that is, cisplatin, etoposide, irinotecan, and topotecan, induce apoptosis of p53 wild-type NB cell lines and, at preapoptotic concentrations, do not induce the expression of activating ligands. Thus, these drugs do not exert immune adjuvant effects on antitumor NK cell-mediated functions.ATM), ATR (ATR), Chk1 (CHEK1), and Chk2 (CHEK2), the main molecules involved in DDR [ATM gene was normal in SH-SY5Y, SK-N-SH, and LA-N-5 but was lost in SK-N-AS, SH-EP, and IMR-32. By contrast, the status of the ATR gene was normal in all NB cell lines with the exception of SK-N-AS and SH-EP in which ATR gain was detected. In addition, the status of genes for checkpoint kinases was shown to be partially altered: CHEK1 was lost in both SK-N-AS and IMR-32, CHEK2 was lost in both SK-N-AS and SK-N-BE(2)c, and both were normal in all other cell lines. Moreover, ATM and CHEK1 were subjected to copy-neutral loss of heterozygosity (cnLOH) in SK-N-BE(2)c c . cnLOH rK1 genes . Thus, cATM, CHEK1, and CHEK2 and the gain of ATR in NB cell lines tested. Additional molecules involved in the DDR pathway triggered by genotoxic drugs and leading to defective modulation of activating ligand expression in NB cells should be investigated.These data suggest that the impaired induction of the expression of activating ligands upon DDR was due at least in part to the loss of The production of ROS in genotoxic drug-treated tumor cells has been associated with the induction of NKG2D and DNAM1 ligands –26. ThenATM single copy LA-N-5 NB cell line was treated with cisplatin, etoposide, irinotecan, or topotecan for 2, 5, and 8 hours, and the level of p53 was evaluated by Western blotting. LA-N-5 cells showed higher levels of p53 2 hours after drug treatment, particularly evident after cisplatin and etoposide treatment that returned to normal levels at latter time points . Then, we analysed the expression levels of ligands for NK cell-activating receptors in the cisplatin-treated A549 cell line. At 48 hours of treatment, the MICA and MICB expression was enhanced, as previously reported [μM of cisplatin for 48 hours and both SH-SY5Y and LA-N-5 cell lines treated with 2 μM of cisplatin for 24 hours. The cisplatin-treated A549 cell line was significantly more susceptible to NK cell recognition than untreated cells, as previously reported [To test whether the impaired, genotoxic drug-dependent modulation of ligands for NK cell-activating receptors in NB cell lines affected NK cell-mediated recognition, we performed an NK cell degranulation assay by using drug-treated NB cell lines as target cells. Cisplatin was previously reported to induce the MICA and MICB expression in non-small-cell lung cancer A549 cell line, through DNA stress-induced ATM-ATR signalling, resulting in enhanced sensitivity to NK cell-mediated cytotoxicity . Thereforeported . First, reported , as wellreported . In contThese data confirm that genotoxic drugs such as cisplatin do not render NB cell lines more susceptible to NK cell recognition.Chemotherapy is the antitumor intervention most commonly employed for several cancers including NB , 2. The cis-retinoic acid [Multiagent chemotherapy represents the conventional therapy for NB patients, especially for those at an advanced disease stage. Depending on the risk group, NB chemotherapy can be associated with surgical tumor removal, myeloablative therapy followed by hematopoietic stem cell infusion, and the use of differentiating agents such as 13-in vivo adoptive transfer of mature in vitro activated and expanded NK cells, in autologous or allogeneic settings, represents a promising approach to treat NB. Different clinical trials based on the use of mature NK cells in combination with some chemotherapeutic agents, such as topotecan, are in the recruiting phase as reported on the http://ClinicalTrials.gov website. In order to evaluate whether drugs such as cisplatin, etoposide, irinotecan, and topotecan could sustain the NK cell-mediated antitumor function, we studied the expression of ligands for NK cell-activating receptors on a panel of NB cell lines. No drugs showed a significant immune adjuvant effect confirmed also by an NK cell functional assay upon cisplatin treatment. This refractoriness was partially related to the abnormal status of ATM, ATR, CHEK1, and CHEK2 genes in the NB cell lines tested. Of note, ATM deletion correlates with lower event-free survival and overall survival in patients affected by NB, independently of MYCN amplification [ATM deletion could often result in resistance to chemotherapy due to an impaired DDR pathway and, consequently, to an inefficient apoptosis or cell cycle arrest. The altered ATM/ATR induction in NB cells could inhibit the effect mediated by many genotoxic drugs in terms of the induced expression of ligands for NK cell-activating receptors. Moreover, our data showed that both the impaired production of ROS in some NB cell lines and the transient p53 stabilization after drug treatment at preapoptotic concentrations could contribute to inefficient induction of NKG2D and DNAM1 ligands. It cannot be excluded that in our drug treatment conditions the apoptosis induced at 48 hours and at higher doses could depend also on p53-independent mechanisms occurring in NB, as reported by other authors [Presently, the fication . Thus, N authors , 43. A nIn conclusion, based on these data and concepts, in order to boost the NK cell-mediated immunotherapy of NB, the combined use of immune adjuvant agents could represent a winning approach. Thus, the effect of molecules efficiently inducing the expression of ligands for NK cell-activating receptors on NB cells remains to be evaluated.


Dasatinib is a potent Src/Abl inhibitor and has demonstrated to have anti-proliferative and anti-invasive activity in many preclinical models. The objective of this study was to determine the anti-tumor activity of dasatinib using 50 ≤ 0.08 μmol/L was considered sensitive. We treated 17 patient-derived CRC explants with dasatinib  for 28 days to determine in vivo efficacy. Tumor growth inhibition (TGI) ≥ 50% was considered sensitive.CRC cell lines and patient-derived tumor explant (PDX) models were used to investigate the efficacy of dasatinib. We treated 50 CRC cell lines with dasatinib for 72 hours and proliferation was assayed by a sulforhodamine B (SRB) assay; an IC50 ≤ 0.08 μmol/L with dasatinib treatment. In addition, of 17 CRC explants grown in the xenograft mouse model, 2 showed sensitivity to dasatinib. The anti-tumor effects observed in this study were a result of G1 cell cycle arrest as the dasatinib sensitive CRC cell lines exhibited G1 inhibition. Moreover, those CRC cell lines that were responsive (0.08 μmol/L) to treatment demonstrated a significant baseline increase in Src and FAK gene expression.We found that 8 out of 50 CRC cell lines reached an ICin vitro, especially in those with increased Src expression at baseline, but only showed modest efficacy in CRC explants. Dasatinib is currently being studied in combination with chemotherapy in patients with advanced CRC, as its use as a single agent appears limited.Dasatinib demonstrated significant anti-proliferative activity in a subset of CRC cell lines  Animals were housed 5 per cage in ventilated cages and allowed free access to food and water.Colorectal cancer patient-derived tumor specimens were acquired from consenting patients at the University of Colorado Hospital in accordance with protocols approved by the Colorado Multiple Institutional Review Board. Female athymic nude (nu+/nu+) mice (6–8 weeks of age) were purchased from Envigo under an approved research protocol by the Institutional Animal Care and Use Committee (IUCAC). The expansion of tumor specimens for treatment studies were performed as previously described . Briefly2. The cells were routinely screened for the presence of Mycoplasma . The CRC cell lines were treated with dasatinib when they reached ∼70% confluence. All cell lines were tested and authenticated in the University of Colorado Cancer Center DNA Sequencing and Analysis Core. CRC cell line DNA was tested using the Profiler Plus kit (Applied Biosystems). The data obtained were compared with cell line databases to ensure the cell lines have not changed.The CRC cell lines examined in this study were purchased from the American Type Culture Collection (ATCC), European Collection of Cell Cultures (ECACC) and Korean Cell Line Bank (KCLB)—Seoul National University. Cells were maintained and treated in RPMI media that contained 10% fetal bovine serum, 1% nonessential amino acids, and 1% penicillin/streptomycin. The cells were placed in an incubator at 37°C containing 5% COThe anti-proliferative effects of dasatinib were determined using a sulforhodamine B (SRB) assay (13). Cells in logarithmic growth phase were plated into 96-well cell culture treated plates. Cells were plated at a concentration 500–3,000 viable cells in 100 ul of complete media per well and incubated overnight before treated to varying concentrations of dasatinib  for 72 hours. After 72 hours of exposure to dasatinib, 50 ul of cold 10% TCA was added to the well after media removal and the cells were incubated for 30 minutes at 4°C. Following fixation, cells were then washed with water and stained with 0.4% sulforhodamine B for 30 minutes at room temperature. The plates were washed with 1% acetic acid followed by stain solubilization with 10 mM Tris. A synergy 2 plate reader (Biotek) was used to determine the effects of treatment on the CRC cell lines.5 per well) were plated in six-well plates. The next day, dasatinib (0.08 μmol/L) was added to the wells and incubated for 24 hours. After 24 hours of treatment the wells were washed in PBS, and Krishanaposs stain was added to each well. Flow cytometry analysis (University of Colorado Cancer Center Flow Cytometry Core Facility) was performed on the cells after 24 hours of staining. For determination of the treatment effects on apoptosis, the sensitive and resistant CRC cell lines were plated in a 96-well white-walled plate. The cells were then treated with dasatinib (0.08 μmol/L) for 24 hours and apoptosis was determined by the measurement of caspase-3/7 activity using a luminometric Caspase-Glo-3/7 assay (Promega) according to the manufacturer's protocol and read using a 96-well plate reader (Biotek).For treatment effects on cell cycle, the sensitive (HCT116 and LS174T) and resistant (HT15 and SW620) CRC cell lines  and tumor (end of study) protein concentration were determined using the 660 Protein Assay kit. Fifty micrograms of sample were run on 4–12% Bis-Tris precast gels . Following electrophoresis, the proteins were transferred to a nitrocellulose membrane using an iBlot . The membranes were blocked at room temperature with 5% nonfat milk (BioRad) in TBS/tween 20 (TBST) for 1 hour at room temperature. The following primary antibodies were used at a concentration of 1:1000: p/t Src, p/t FAK, p/t paxillin, p/t AKT, p/t Stat3, and Actin  and the membranes were incubated overnight at 4°C with rocking. The membranes were washed three times with TBST, and anti-rabbit or anti-mouse IgG horseradish peroxidase–conjugated antibody were added at a final dilution of 1:50,000 in TBST. After washing three times with TBST, bound antibodies were detected by enhanced chemiluminescence .http://www.broad.mit.edu/gsea). We used the pathways defined by the Kyoto Encyclopedia of Genes and Genomes as the gene set in this study (REF). Gene set permutations were performed 1,000 times for each analysis. We used the nominal p-value and Normalized Enrichment Score obtained from GSEA to sort the pathways up and down regulated in the dasatinib-treated groups.Total RNA from CRC explants were extracted using RNAeasy kit (Qiagen) and profiled using Affymetrix gene array and RNA Seq. Raw expression values were extracted and normalized by the Affymetrix Power Tools based on robust multiarray average approach. Multiple probe sets representing the same gene were collapsed by the maximum value. To analyze the pathway enriched in the control versus dasatinib-treated explants, we used the GSEA  software version 2.0.13 obtained from the Broad Institute (An unpaired Student t-test was used to determine whether the means between control and dasatinib were significant at end of treatment (28 days). The differences were considered significant when the p value were <0.05. All error bars are represented as the SEM. A tumor growth index (TGI) was calculated (mean tumor/mean control x 100) and a cut-off point of 50% was used to dichotomize treatment effects into sensitive (TGI < 50%) and resistant (TGI ≥ 50%). All analyses were carried out by GraphPad Prism version 5.0c for Windows (GraphPad Software).50 ≤ 0.08 μmol/L (dose range 0.08–5 μmol/L). A cut-off of 0.08 μmol/L was used to determine sensitivity in CRC cell lines considering this is the concentration that is achievable in humans. The CRC cell lines HCT116, LS174T, SW1417, WiDR, KM20, SKCO1, SNU-796, and SNU977 all reached an IC50 < 0.08 μM. All other cell lines were shown to be more resistant to dasatinib. 50 as well as the clinically relevant mutational analyses of the CRC cell lines. Next, we evaluated treatment effects on 17 unique CRC explants grown in a xenograft mouse model and treated with dasatinib 50 mg/kg/day, administered once-daily for 28 days. We first investigated the antiproliferative effects of dasatinib on 50 CRC cell lines. As demonstrated in 2M were determined after 24 hours of treatment. As shown in To examine the anti-proliferative mechanisms of Src inhibition, two sensitive (HCT116 and LS174T) and two resistant (HT15 and SW620) CRC cell lines were treated with dasatinib (0.08 μmol/L) and the proportion of cells cycling in G1, S and GWe next evaluated the treatment effects of dasatinib on the Src signalling pathway on the sensitive HCT116 CRC cell line and CRC036 explant as well as on the resistant CRC explant CRC027. In the HCT116 CRC cell line we demonstrated a significant reduction in the activation of Src and the downstream kinases FAK and paxillin as early as 30 minutes of treatment . This deWe performed a comprehensive pathway analysis via gene array on the 8 sensitive (0.08 μmol/L) and 18 most resistant CRC cell lines  to compaThe importance of Src in tumor development, migration, and proliferation has been elucidated over the past century , 23. Des50 was ≤ 0.08 μmol/L in 8 sensitive cell lines, which is a dose achievable in humans [We demonstrated that dasatinib showed anti-proliferative and antitumor effects in 8 out of 50 CRC cell lines and 1 out of 17 CRC explants, respectively. The ICn humans . In thosin vivo and in vitro experiments investigating Saracatinib (AZD0530), a Src inhibitor, in the treatment of CRC cell lines and explants [et al has also shown that Src siRNA knockdown decreased proliferation rate of the HCT-116 cell line similar to our results with dasatinib treatment [The mechanism of Src inhibition occurs through cell cycle arrest during G1 phase as supported by our cell cycle arrest assay, consistent with other preclinical studies investigating other solid tumor types , 27. Subexplants . Zheng ereatment .et al. reported that 24% of patients achieved a partial response (PR) with a 17% PR rate in patients previously reported to be refractory to FOLFOX and cetuximab [The limited efficacy of Src inhibition in our study is consistent with previously published clinical studies using single-agent dasatinib –32. Combetuximab . Resultsetuximab . Based oLack of efficacy of dasatinib may be due to multiple factors; including unselective properties as a multi-kinase inhibitor, lack of predictive biomarker data, variability of Src biology and role in CRC tumorigenesis, and efflux of dasatinib from CRC cells via activation of p-glycoprotein (p-gp), a member of the ABC transporter family (Ekblad 2010). There is also evidence that resistance to Src inhibition may be related to Src-induced reduction in immune responses .KRAS mutant CRC tumors to anti-EGFR therapy in vitro and in vivo, suggesting that there may be additional biomarker data yet to be fully elucidated to help drive future studies [et al found that addition of dasatinib to fluoruracil and oxalplatin re-sensitize CRC tumors that expressed high levels of p-SRC [Despite limited efficacy, this study suggests that increased Src expression may be a potential predictive biomarker to guide treatment with Src inhibition in patients with advanced CRC. In addition, preclinical data investigating combinational approaches has demonstrated that dasatinib sensitizes  studies . Similarof p-SRC  BiomarkeSrc plays an important role in tumor migration and proliferation, and is commonly found to be upregulated in CRC tumors. This has made Src inhibition an attractive target for treatment of CRC. Dasatinib, a Src kinase family inhibitor, showed anti-tumor activity in a subset of CRC cell lines and in one explant mouse model. The greatest effects were seen in cell lines that had elevated baseline levels of Src activity; however, the baseline level of Src activity in the explant models did not correlate with improved responses. The anti-tumor activity of single-agent Src inhibition appears to be limited, and the focus of Src inhibition in future studies should be limited to combinational approaches in the treatment of CRC.Patient-derived colorectal adenocarcinoma tumor specimens were obtained from consenting patients at the University of Colorado Hospital in accordance with a protocol approved by the Colorado Multiple Institutional Review Board (08–0439).Patients signed informed consent to participate in this research.S1 Table(XLSX)Click here for additional data file.


TBLR1), was highly predictive of other marks in two hematopoietic cell lines. The methodology presented here shows how the potentially vast complexity of TFs, coregulators, and epigenetic marks at eukaryotic genes is highly redundant and that the information present can be compressed onto a much smaller subset of marks. These findings could be used to efficiently characterize cell lines and tissues based on a small number of diagnostic marks and suggest how the DNA microstates, which regulate the expression of individual genes, can be specified.The DNA microstates that regulate transcription include sequence-specific transcription factors (TFs), coregulatory complexes, nucleosomes, histone modifications, DNA methylation, and parts of the three-dimensional architecture of genomes, which could create an enormous combinatorial complexity across the genome. However, many proteins and epigenetic marks are known to colocalize, suggesting that the information content encoded in these marks can be compressed. It has so far proved difficult to understand this compression in a systematic and quantitative manner. Here, we show that simple linear models can reliably predict the data generated by the ENCODE and Roadmap Epigenomics consortia. Further, we demonstrate that a small number of marks can predict all other marks with high average correlation across the genome, systematically revealing the substantial information compression that is present in different cell lines. We find that the linear models for activating marks are typically cell line-independent, while those for silencing marks are predominantly cell line-specific. Of particular note, a nuclear receptor corepressor, transducin beta-like 1 X-linked receptor 1 ( The decision to transcribe genes relies on DNA sequence information, which is interpreted by transcription factors (TFs) or other sequence-specific DNA-binding proteins. In eukaryotes, TFs interact with a variety of mechanisms that reorganize chromatin structure, remodel nucleosomes, recruit coregulators, methylate DNA, and post-transcriptionally modify histones and regulatory proteins to collectively regulate transcription. These genetic and epigenetic mechanisms “mark” regulatory loci to yield DNA microstates, a term derived from thermodynamics , which e160 ≈ 1048 potential modification patterns on a single nucleosome and vastly more when all the other marks are considered. Only a small fraction of these microstates are observed in practice, implying associations between different marks and high levels of redundancy between them. We pursue several questions: How strong are these associations? Can most marks be predicted by knowing only a few? Are the rules of association specific to particular cell lines or do they hold generally across many different cell lines?In this study, we focus on the structure of DNA microstates, whose combinatorial complexity is potentially enormous. For example, the histone proteins H2A, H2B, H3, and H4 can be modified at as many as 160 different sites. If these modifications were merely binary, as in the case of phosphorylation, this would result in 2The large-scale data emerging from consortia like ENCODE  and the Here, we combine data for epigenetic marks with data for TFs, coregulators, and chromatin remodelers and avoid discretization to enhance sensitivity. We find that simple linear models capture strong associations among these marks within a cell line, with a small subset of marks being able to predict most other marks with high average correlation across the genome, which means here at protein coding and lincRNA genes. We further show that these linear models are largely cell line-independent for activating marks and largely cell line-specific for silencing marks. Our results suggest how cell lines can be characterized by epigenetic and regulatory protein marks and improve our understanding of gene regulation.Table A in Table B with Table C in In this study, we analyzed genome-wide data obtained from the ENCODE and NIH Roadmap Epigenomics consortia for five cell lines . We selected these five cell lines as GM12878, H1, and K562 are Tier 1 cell lines in the ENCODE consortium, and therefore ChIP-seq data for histone modifications as well as a large number of regulatory proteins were available. Furthermore, in the NIH Roadmap Epigenomics Consortium the largest datasets for histone modifications and DNA methylation data were available for the cell lines H1, H9, and IMR90. In total, data was available for DNA methylation, DNase hypersensitivity, 18 chromatin remodelers, 21 coregulators, 30 histone modifications, and 106 transcription factors in these cell lines  to test the generalizability of our predictions.To predict the enrichment value of one mark using the other marks, we fitted a linear model to the data for all marks for a given constellation using the 1-bin resolution data,Then, for a given constellation, we evaluated the Pearson correlation coefficient (Pearson’s r) between the measured and the predicted enrichment values for each mark.Table B in Table B in The median Pearson’s r was 0.92 over all marks and all possible constellations , that have an absolute weight above 0.3. These are ATF3 and CEBPD, which are both known to interact directly with CEBPB [GATA1, which is an important regulator of erythroid development by regulating large numbers of genes [P300 [Table D in GATA1 binding to DNA in K562 cells, which apparently possess a similar or better information content than P300 in this context  with significant overrepresentation among the strong model weights for the target mark types. Interestingly, in the case of TSSs of protein coding genes in H1, we do see for all mark types that in the models the identical mark types are overrepresented regarding the relative frequency of that mark type , the histone deacetylase SIRT6 [TCF12 [USF1 [NR2C2 [TR4), YY1 [FOSL1 [SP2 [ZBTB33 [ZNF274 [EGR1 [RXRA [SPI1 [PU1). We note that some of these marks, like YY1 and SPI1, are also known to play a role in gene-activating mechanisms. The remaining two mark H3K36me3 and POL3 are positively associated with gene expression.There are 19 marks standing out, because their median Pearson’s r fell by more than 0.3 compared to the median Pearson’s r in the above intracellular 10-fold CV setting for each mark . 17 of t3 [KDM5B  , YY1 , 29, FOS1 [FOSL1 , 31, SP2SL1 [SP2 , 33, ZBT [ZBTB33 , 35, ZNF [ZNF274 , 37, EGR74 [EGR1 , 39,RXRAR1 [RXRA , and SPIRA [SPI1 –43  are all included in this set. That is particularly interesting for DNA methylation: In our analysis, it is overall a rather invariable mark between different cell lines .In contrast to the silencing epigenetic marks, the activating histone modifications, e.g., histone acetylations and H3K4 methylations, had a median drop of 0.01 . An illuOur findings suggest that activating marks follow association rules that hold throughout various cell lines and possibly interact with other marks in the very same manner, whereas silencing marks follow more cell line-specific rules and might possess unique interaction partners in each cell line. This finding further suggests that proteins mediating or interacting with activating marks are more ubiquitously expressed and active at similar levels across many cell types and that those proteins that mediate or interact with silencing marks might vary substantially from cell type to cell type in their expression and activity patterns .Table E and G in When comparing the cross-cell line linear model performance with the performance of taking data for the identical mark from another cell line as prediction, we observe that the cross-cell line model performance is better for most marks  and bettOur model comparisons across cell types enable the clustering of samples by using the predictive strengths as a distance metric. When we fit a model for a specific mark in cell line 1 in order to evaluate it in cell line 2, we can cluster the cell lines with regard to how well one cell line predicts the mark enrichments of another one . Here wegex is obtained by taking log(GEx + ε), setting the top 1% of values to 1, the bottom 1% of values to 0 and then scaling the rest linearly between 0 and 1, n is the number marks, m is the number of bins (either 1 or 40), marki,bin stands for the enrichment value of the i-th mark at the respective bin around the TSS, and ε is a small pseudocount accounting for genes with GEx values of 0. At 40-bin resolution, we considered a third alternative, where we only took data of the middle two bins for each mark (bin 20 and 21) of the 40 bins into account. In that case, we restricted our model to the information in the +/− 100 bp region around the TSS.When modeling gene expression  is a piecewise linear function  models , which cTable H in We then used 10-fold CV for both linear and MARS models, 1-bin and 40-bin resolution  around TSSs, and varying pseudocounts  can be achieved with linear or mixed linear models (like MARS) as long as the resolution around the TSS is sufficiently high. Also, the information in the +/− 100 bp region around the TSS seems to be of particular importance for each model’s performance. This conclusion is supported by the case, where we fitted the MARS models on 100% of the data on 40-bin resolution  marks, for each ordered pair of different cell lines we considered the marks for which data is available in both cell lines, fitted a model in the first cell line for either protein coding or lincRNA genes and then predicted gene expression in the other cell line. The performance of these cross-cell line models was comparable to the performance of the models obtained, when using 10-fold CV in the intra-cell line setting  complemented by DNA methylation and RNA-Seq data for a large panel of cell lines. This raises the question in how far we can account for the information content of all marks in various cell types by the six IHEC histone modifications. For the sake of simplicity, we will refer to these six histone modifications here as the “IHEC marks”. First, we aimed to predict for all cell lines all other marks from the IHEC marks using linear models at 1-bin resolution. When performing 10-fold CV on each fixed constellation, we obtain a median over each other mark’s median Pearson’s r over all possible constellations of 0.76  can in principle be applied to extend the epigenome and regulatory protein data for any cell line for which ChIP-Seq data for the six IHEC histone modifications are available  was more important for predicting gene expression than the window width around the TSS. The best-performing models used a 100 bp resolution around the TSS. Utilizing this binning our models with a large window (+/− 2 kb relative to the TSS) and with a relatively small window of only a fraction of the larger window (+/−100 bp) performed similarly well. This observation is suggestive of strong enrichments of predictive marks located very close to the TSS.TBLR1), for which we only had data in the two hematopoietic cell lines K562 and GM12878, was always the best mark for predicting all other marks, with a performance equal or superior to models, which used all six of the IHEC histone modifications. Thus, it could prove valuable to advance the study of this nuclear receptor corepressor further [The extent of information “compression” that can be achieved depends strongly on the type of marks we included in our analysis. For example, the transducin beta-like 1 X-linked receptor 1  marks. From another perspective, this could be utilized for predicting most other marks based on few measured signals. We speculate that the dynamic causal relationships between different marks—which marks recruit other marks—and the network effects through which different genes influence each other, will be more difficult to dissect. As we have previously shown this dynamic type of information is required to predict gene regulatory functions . Approachttp://genome.ucsc.edu/ENCODE/ (old) and http://encodeproject.org/ (new)Encyclopedia of DNA Elements (ENCODE) Consortium, http://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeMapability/wgEncodeDacMapabilityConsensusExcludable.bed.gzENCODE blacklist, http://roadmapepigenomics.orgNIH Roadmap Epigenomics project, http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19 data, http://www.gencodegenes.org/releases/18.htmlGencode v. 18, http://www-huber.embl.de/users/anders/HTSeq/Python package HTSeq, http://pypi.python.org/pypi/wiggelen/Python packge wiggelen, http://cran.r-project.org/R software, http://cran.r-project.org/package=earth.R package earth, Table Z in http://vcp.med.harvard.edu/linear-epigenome.html, which make use of the HTSeq and wiggelen packages. First, for each protein coding or lincRNA gene, respectively, we determined with the help of the Gencode annotation set (version 18) where the outmost TSS or TTS of all transcripts of the respective gene lie in the hg19 assembly. Then we considered for each gene three regions:We downloaded the ENCODE and NIH Roadmap Epigenomics data (+/− 2kbs around the outmost TSSthe gene body (the region between the outmost TSS and TTS)+/− 2kbs around the outmost TTSFollowing this, for each cell line and each data file for an individual mark we counted the number of tags falling into the aforementioned regions for each gene, where we considered 1-bin and 40-bin resolutions for each of these regions (40-bin resolution was just considered for TSSs). Then we stored for each resolution type and each region type the result for each gene and each bin, where we divided each count for the bin by the size of the bin. The latter matters only since the gene body varies in length from gene to gene. In addition to that for DNA methylation data we also count the number of CpGs in each of the above regions and bins from the hg19 sequence data and normalize the DNA methylation values regarding the number of CpGs.i, j) reflects the enrichment of mark j at the TSS of gene i. If we consider the 40-bin resolution in the same setting, we obtain a matrix of dimension 19399 × (40 ⋅ 132), since for each mark we have 40 columns. If we consider a cell line like H9, where we have DNA methylation data at hand, we obtain in the 1-bin setting for protein coding genes a matrix of dimension 19399 × 31, where we have 30 marks for that cell line, but included two columns for DNA methylation, one for the absolute value and one for the normalized value with respect to the number of CpGs in the region. All generated data can be found at http://vcp.med.harvard.edu/linear-epigenome.html.After we did this for both protein coding and lincRNA genes, we created with R  a matrixlm for fitting linear models. For the gene expression analysis, we also used multivariate adaptive regression spline (MARS), where the fitted models are weighted sums of piecewise linear functions B(x), which are of the form max or max for some constant c. Here we used the R package earth and the function earth. We obtained the Pearson’s r between the measured and predicted values and the p-value of t-test by the built-in R function cor.test.Unless otherwise stated, we used the built-in R function For a fixed gene type (protein coding or lincRNA genes), region type , and cell line we first took one column out of the matrix for this locus constellation, which corresponds to the mark of interest. For DNA methylation, we used the normalized data values (see above). For the remaining marks, we removed the absolute DNA methylation data when predicting DNA methylation. When predicting values other than DNA methylation, although DNA methylation data were available for this cell line, we deleted the normalized DNA methylation data as we were only interested in the total presence of DNA methylation and its effects. We then used 10-fold CV to obtain the predictions. For fitting the 100% models we simply considered 100% of the genes, fitted a model for a given mark and constellation, and extracted all weights from this linear model.For a fixed ordered pair of different cell lines, e.g., , and fixed locus constellation, we considered all marks for which data in both cell lines was available. We reduced the respective matrices for both cell lines here to these marks. Once we fixed a mark of interest (for which we do have data in both cell lines), we took this mark out of the matrix for the first cell line and obtained a vector and the remaining matrix (DNA methylation was treated as in the paragraph from above). We fitted a model for this mark in the first cell line on the 100% data and tried to predict the mark in the second cell line by using data for all other marks that were present in both cell lines. We measured the Pearson’s r and the p-value by comparing the measured and predicted value for the mark of interest in the second cell line.Table Z in GEx = bin20,+ + bin21,+ + bin20,− + bin21,−, where binj,⋆ should indicate the value of bin j for the respective strand file (⋆) for a particular gene of interest. Hence, we took the sum of values in the +/− 100 bp region around the TSS for both strand information data.Gene expression data was available only for four of the cell lines  and we chose to take CAGE nucleus data  DNA methylation data. The models were fitted as a (completely) linear model (lm) or as a (piecewise linear) MARS model (earth). In analogy to predicting the marks we used 10-fold CV here and calculated the Pearson’s r and the p-value between the measured and predicted values.For a fixed cell line of the four cell lines mentioned above and a fixed gene type, we took the gene expression data file from above and added various pseudocounts ε from above that maximized the Pearson’s r for the first cell line for that gene type, input data type, and model type. We obtained the gene expression (gex) for the gene type of interest in both cell lines with respect to the ε in the first cell line just as above. We took only those marks for the model fitting (in the first cell line) and validating (in the second cell line), that were present in both cell lines, where again only absolute DNA methylation data was taken, if available. We then fitted the model on 100% of the data in the first cell line, predicted the gene expression in the second cell line, and calculated the Pearson’s r and p-value between predicted and measured values.For a fixed ordered pair of different cell lines, gene type, input data type for the marks , and model type (linear or MARS model), we took the For a fixed constellation we took the data from the six IHEC histone modifications  and fitted a model for each other mark available . Again, if we wanted to predict DNA methylation we just considered the normalized value. We used 10-fold CV to obtain the Pearson’s r and p-value. For establishing the models on 100% of the data we did the same analysis as in the case, where all other marks were used to predict a particular mark of interest.For a fixed ordered pair of different cell lines and fixed locus constellation, we applied the same strategy as above with the only difference being that the input data was restricted to the six IHEC histone modifications. The marks that were to be predicted were marks for which we have data for both cell lines (aside from the six IHEC histone modifications).n is the number of marks for the given cell line) we applied the following algorithm:For the sake of simplicity, we focused here on the +/− 2 kb regions around TSSs of protein coding genes. For each cell line ( ∈ K\S:    For markSj = S ∪ {markj).vj → median of the Pearson’s r values.    m correspond to the maximum entry of v.    Let markS by markm.    // Extend S → S ∪ {markm}.    i—th entry of ordervec to m.    // Set orderveci = m.    ordervec gives us the selection order.The vector S1 DatasetThis file contains 27 sheets, where we do have information about the marks used, Pearson’s r and p-values for each situation evaluated, fitted linear models on 100% of the data, mark information content, and a list with download links.(XLSX)Click here for additional data file.S1 FigHistogram of Pearson’s r between measured and predicted values (when using 10-fold CV) for all marks and constellations.(TIF)Click here for additional data file.S2 FigHistogram of normalized DNA methylation enrichments in the regions around TSSs of protein coding genes in H1. Here a value of 0 means that 0% of the CpGs are methylated, and a value of 1 means that 100% of the CpGs are methylated.(TIF)Click here for additional data file.S3 FigA) Scatter plot for median Pearson’s r comparison for each mark at TSSs of protein coding genes between the 10-fold CV model performance and the correlation of the best correlated mark in the same respective cell line. That means for each mark we take the median 10-fold CV Pearson’s r over all cell lines, where there is data for that mark. Then for each other mark, which we name mark2 here, we take median Pearson’s r between the target mark and mark2 enrichments at TSSs of protein coding genes over all cell lines, where there is data for both, and then we take the maximum value of it. (B) same as (A), only that we consider just histone modifications, where the value for the “reference model” is still taken over all marks and not just histone modifications. (C) Scatter plot for median Pearson’s r comparison for each mark, where there is data for that mark available in at least two cell lines, at TSSs of protein coding genes between the 10-fold CV model performance and the correlation of the identical mark in all other cell lines. Whereas the first part is just as above, for the second one we do consider for each mark all ordered pairs of different cell lines, where we do have data for that mark in both cell lines, calculate the Pearson’s r between the enrichments at TSSs of protein coding genes in both cell lines and take the median over it. (D) same as (C), only that we consider just histone modifications.((TIF)Click here for additional data file.S4 FigA) For TSSs in H1, (B) transcripts in H1, (C) TTSs in H1, (D) TSSs in H9, (E) transcripts in H9, (F) TTSs in H9, (G) TSSs in GM12878, (H) transcripts in GM12878, (I) TTSs in GM12878, (J) TSSs in IMR90, (K) transcripts in IMR90, (L) TTSs in IMR90, (M) TSSs in K562, (N) transcripts genes in K562, and (O) TTSs in K562.((TIF)Click here for additional data file.S5 FigA) For TSSs of lincRNA genes in H1, (B) transcripts in H1, (C) TTSs in H1, (D) TSSs in H9, (E) transcripts in H9, (F) TTSs in H9, (G) TSSs in GM12878, (H) transcripts in GM12878, (I) TTS in GM12878, (J) TSSs in IMR90, (K) transcripts in IMR90, (L) TTSs in IMR90, (M) TSSs in K562, (N) transcripts in K562, and (O) TTSs in K562.((TIF)Click here for additional data file.S6 FigA) For transcripts in H1, (B) TTSs in H1, (C) TSSs in GM12878, (D) transcripts in GM12878, (E) TTSs in GM12878, (F) TSSs in IMR90, (G) transcripts in IMR90, (H) TTSs in IMR90, (I) TSSs in K562, (J) transcripts in K562, and (K) TTSs in K562. The description of the plots is analogous to ((TIF)Click here for additional data file.S7 FigA) For TSSs in H1, (B) transcripts in H1, (C) TTSs in H1, (D) TSSs in GM12878, (E) transcripts in GM12878, (F) TTSs in GM12878, (G) TSSs in IMR90, (H) transcripts in IMR90, (I) TTSs in IMR90, (J) TSSs in K562, (K) transcripts in K562, and (L) TTSs in K562. The description of the plots is analogous to ((TIF)Click here for additional data file.S8 FigA) Scatter plot for median Pearson’s r comparison for each mark at TSSs of protein coding genes between the median correlation between predicted and measured values, when the models, with which the predictions are made, are fitted in other cell lines , and the median correlation of the identical mark in all other cell lines (the latter part is as in B) same as (A), only that we consider just histone modifications.((TIF)Click here for additional data file.S9 FigA) Heatmap showing median Pearson’s r between predicted and measured values for transcripts of protein coding genes over all marks in that target cell line, that are also present in the starting cell line, where the models for the prediction are fitted in the starting cell line and then used to predict the enrichments in the target cell line. For each entry, the target cell line is named as the row entry and the starting cell line as named as the column entry. (B),(C),(D), and (E) same as (A) for TTS of protein coding genes, TSSs of lincRNA genes, transcripts of lincRNA genes, and TTSs of lincRNA genes, respectively.((TIF)Click here for additional data file.S10 Figε was optimized.Barplot of Pearson’s r (when using 10-fold CV) for different models for lincRNA genes. The models are indexed analogously to (TIF)Click here for additional data file.S11 FigA), MARS models on 40-bin resolution (middle two bins) for protein coding genes (B), linear models on 40-bin resolution (middle two bins) for protein coding genes (C), MARS models on 1-bin resolution for protein coding genes (D), linear models on 1-bin resolution for protein coding genes (E), MARS models on 40-bin resolution for lincRNA genes (F), linear models on 40-bin resolution for lincRNA genes (G), MARS models on 40-bin resolution (middle two bins) for lincRNA genes (H), linear models on 40-bin resolution (middle two bins) for lincRNA genes (I), MARS models on 1-bin resolution for lincRNA genes (J), and linear models on 1-bin resolution for lincRNA genes (K). The description of the plots is analogous to Barplot of Pearson’s r, when considering each possible ordered pair of different cell lines, shown in blue, and the Pearson’s r (when using 10-fold CV) for individual cell lines, shown in red, when using linear models on 40-bin resolution for protein coding genes ((TIF)Click here for additional data file.S12 FigA) Histogram of Pearson’s r over all other marks between predicted and measured values (when using 10-fold CV) over all cell lines (where data was available for this mark) around the TSSs of protein coding genes. (B) Histogram of Pearson’s r over all cell lines, all other histone modifications (where data for these marks was available for this cell line), and all locus constellations. (C) Histogram of Pearson’s r over all cell lines, marks that are not histone modifications (where data for these marks was available for this cell line), and all locus constellations.((TIF)Click here for additional data file.S13 FigA) Scatter plot for median Pearson’s r comparison for each histone modification (apart from the six IHEC histone modifications) at TSSs of protein coding genes between the 10-fold CV model performance, where the models are fitted on all other marks, and the 10-fold CV model performance, where the models are fitted on IHEC marks. (B) Scatter plot for median Pearson’s r comparison for each mark (apart from the six IHEC histone modifications), where there is data for that mark available in at least two cell lines, at TSSs of protein coding genes between the 10-fold CV model performance, where the models are fitted on IHEC marks, and the median correlation of the identical mark in all other cell lines (as in C) same as (B), only that we consider just histone modifications (apart from the six IHEC histone modifications).(es as in . (C) sam(TIF)Click here for additional data file.S14 FigA) Scatter plot for median Pearson’s r comparison for each histone modification (apart from the six IHEC histone modifications), where there is data for at least two cell lines available, at TSSs of protein coding genes between the median 10-fold CV model performance, where the models are fitted on IHEC marks, and the median correlation between predicted and measured values, when the models, with which the predictions are made, are fitted in other cell lines on the IHEC marks. (B) Scatter plot for median Pearson’s r comparison for each mark (apart from the six IHEC histone modifications), where there is data for at least two cell lines available, at TSSs of protein coding genes between the correlation between predicted and measured values, when the models, with which the predictions are made, are fitted in other cell lines on IHEC marks, and the median correlation of the identical mark in all other cell lines (as in C) same as (B), only that we consider just histone modifications. (D) Scatter plot for median Pearson’s r comparison for each mark (apart from the six IHEC histone modifications), where there is data for at least two cell lines available, at TSSs of protein coding genes between the median correlation between predicted and measured values, when the models, with which the predictions are made, are fitted in other cell lines on the IHEC marks, and the median correlation between predicted and measured values, when the models, with which the predictions are made, are fitted in other cell lines on all marks, that are present in both cell lines. (E) same as (D), only that we consider just histone modifications.(es as in . (C) sam(TIF)Click here for additional data file.S15 FigA) Heatmap showing median Pearson’s r between predicted and measured values for transcripts of protein coding genes over all marks for which there is data available in all cell lines apart from the IHEC marks , where the models for the prediction are fitted on the IHEC marks in the starting cell line and then used to predict the enrichments in the target cell line. For each entry, the target cell line is named as the row entry and the starting cell line as named as the column entry. (B),(C),(D), and (E) same as (A) for TTS of protein coding genes, TSSs of lincRNA genes, transcripts of lincRNA genes, and TTSs of lincRNA genes, respectively.((TIF)Click here for additional data file.S16 FigA) For GM12878, (B) H1, (C) IMR90, and (D) K562. The description of the plots is analogous to ((TIF)Click here for additional data file.S17 FigA) For GM12878, (B) H1, (C) H9, and (D) IMR90. The description of the plots is analogous to ((TIF)Click here for additional data file.