HOXC9 directly regulates distinct sets of genes to coordinate diverse cellular processes during neuronal differentiation
- Xiangwei Wang†1,
- Jeong-Hyeon Choi†2, 3,
- Jane Ding†2, 4,
- Liqun Yang†6,
- Lambert C Ngoka2,
- Eun J Lee2, 5,
- Yunhong Zha7,
- Ling Mao8,
- Bilian Jin2, 5,
- Mingqiang Ren2, 4,
- John Cowell2, 4,
- Shuang Huang2, 5,
- Huidong Shi2, 5,
- Hongjuan Cui6Email author and
- Han-Fei Ding2, 4, 5Email author
© Wang et al.; licensee BioMed Central Ltd. 2013
Received: 26 July 2013
Accepted: 20 November 2013
Published: 25 November 2013
Cellular differentiation is characterized by the acquisition of specialized structures and functions, cell cycle exit, and global attenuation of the DNA damage response. It is largely unknown how these diverse cellular events are coordinated at the molecular level during differentiation. We addressed this question in a model system of neuroblastoma cell differentiation induced by HOXC9.
We conducted a genome-wide analysis of the HOXC9-induced neuronal differentiation program. Microarray gene expression profiling revealed that HOXC9-induced differentiation was associated with transcriptional regulation of 2,370 genes, characterized by global upregulation of neuronal genes and downregulation of cell cycle and DNA repair genes. Remarkably, genome-wide mapping by ChIP-seq demonstrated that HOXC9 bound to 40% of these genes, including a large number of genes involved in neuronal differentiation, cell cycle progression and the DNA damage response. Moreover, we showed that HOXC9 interacted with the transcriptional repressor E2F6 and recruited it to the promoters of cell cycle genes for repressing their expression.
Our results demonstrate that HOXC9 coordinates diverse cellular processes associated with differentiation by directly activating and repressing the transcription of distinct sets of genes.
KeywordsNeuronal differentiation Cell cycle arrest DNA damage response E2F6 HOXC9 Neuroblastoma
Cellular differentiation is an essential process of normal development by which a stem or progenitor cell becomes a post-mitotic, specialized cell with unique morphology and function. In addition, it has long been recognized that differentiated cells of both normal and tumor origin are defective in the DNA damage response and repair at the global level, displaying a marked increase in sensitivity to ionizing radiation and other DNA damaging agents [1–3]. Consistent with these observations, recent studies have shown that brain and breast cancer stem cells, a small subpopulation of tumor cells thought to be responsible for initiating and sustaining tumor growth [4–6], are more resistant to irradiation and chemotherapy than bulk tumor cells [7–10]. Particularly interesting is the observation that inhibition of DNA damage checkpoint kinases can reverse the radioresistance of glioma stem cells . Thus, a molecular understanding of cellular differentiation may suggest new therapeutic strategies that target both cell proliferation and the DNA damage response.
Among the genes that have a critical role in the control of cellular differentiation are the HOX gene family members. HOX genes encode a family of transcription factors that function as master regulators of morphogenesis and cell fate specification [11–13]. Dysregulation of HOX gene expression has been implicated in the pathogenesis of cancers of different tissue types. In most tumor types, HOX genes function as oncogenes to promote cancer development such as HOXA9 in leukemia and HOXB13 in ovarian and breast cancers [13, 14]. However, in neuroblastoma, a common childhood malignant tumor of the sympathetic nervous system [15, 16], there is evidence suggesting that HOX genes may function as tumor suppressors . Particularly, downregulation of HOXC9 expression is significantly associated with poor prognosis in neuroblastoma patients [17, 18].
Neuroblastoma cells can be induced to undergo neuronal differentiation by serum deprivation , nerve growth factor  or retinoic acid (RA) . RA-induced neuronal differentiation of neuroblastoma cells is a well-established model for molecular investigation of neuronal differentiation . We recently reported that RA-induced differentiation of neuroblastoma cells required the activation of several HOX genes [18, 23]. Among them, HOXC9 appeared to be a major mediator of RA action in neuroblastoma cells. HOXC9 expression was upregulated by RA, and silencing HOXC9 expression conferred resistance to RA-induced differentiation. Importantly, ectopic HOXC9 expression alone was sufficient to induce growth arrest and morphologic differentiation in neuroblastoma cells, fully recapitulating the neuronal differentiation phenotype induced by RA .
Differentiated neuroblastoma cells morphologically and functionally resemble mature peripheral neurons characterized by G1 arrest, extensive neurite outgrowth, and significant resting potential. It has long been observed that differentiated neuroblastoma cells are highly sensitive to UV and X-ray radiation with a significantly reduced rate of DNA damage repair [20, 24–27]. The molecular basis for the differentiation-induced radiosensitivity is not well understood. The biological functions of RA are mediated by multiple isotypes of RA receptors (RARs) and retinoid X receptors (RXRs), which form RAR/RXR heterodimers that bind RA response elements in the regulatory regions of RA target genes and regulate their transcription . The complexity of multiple RARs and RXRs involved in the action of RA presents a daunting challenge to dissect the molecular mechanism that coordinates the diverse cellular events associated with differentiation. Thus, the finding that HOXC9 alone is able to initiate a robust transcriptional program that drives neuronal differentiation provides a unique experimental system for this investigation. In this study, we conducted genome-wide profiling of the HOXC9-initiated transcriptional program. Our investigation reveals that HOXC9 directly regulates the expression of three major sets of genes that separately control neuronal differentiation, cell cycle progression, and the DNA damage response.
Gene expression profiling of HOXC9-induced neuronal differentiation
Global upregulation of neuronal genes
Gene Ontology (GO) analysis of the 879 HOXC9-upregulated genes by DAVID [29, 30] revealed that they were significantly enriched for genes that control nervous system development such as neuron generation and differentiation, axonogenesis, and synapse formation and organization (Figure 1B and Additional file 2: Table S2, enrichment fold ≥ 2.0, false discovery rate (FDR) ≤1%). A total of 105 HOXC9-responsive genes were involved in nervous system development (Figure 1B), accounting for approximately 12% of the 879 genes upregulated by HOXC9. We obtained similar results with Gene Set Enrichment Analysis (GSEA), which showed significant enrichment of gene sets involved in synaptogenesis and neuron differentiation among the genes upregulated by HOXC9 (Figure 1C). Particularly significant was the activation of ASCL1, GFRA3, RET, and NTN3 (Figure 1D). ASCL1, a member of the basic helix-loop-helix (bHLH) family of transcription factors, is a master regulator in the generation and differentiation of sympathetic neurons [31, 32]. GFRA3 encodes the glial cell line-derived neurotrophic factor (GDNF) family receptor alpha 3 (GFRα3), which forms a receptor complex with RET that preferentially binds the GDNF family ligand Artemin. This receptor signaling has a critical role in embryonic development of the sympathetic nervous system, promoting the survival, differentiation, axonal outgrowth, and target innervation of sympathetic neurons . NTN3 (netrin 3) belongs to a family of extracellular proteins that promote axon growth and migration during the development of the nervous system . Ingenuity Pathways Analysis (IPA) further revealed a network of HOXC9-upregulated genes relevant to the development and function of sympathetic neurons (Additional file 3: Figure S1). Together, these analyses demonstrate that HOXC9 activates a large number of neuronal genes, providing the molecular mechanism for its ability to induce neuronal differentiation of neuroblastoma cells.
Global downregulation of cell cycle and DNA repair genes
Genome-wide mapping of HOXC9-binding sites
Genome-wide identification of HOXC9 target genes
The ChIP-seq assay revealed that a total of 4,992 genes contained at least one HOXC9-binding peak within 5-kb upstream or downstream of their genomic loci (Figure 3A and Additional file 6: Table S5). We next combined the anti-HOXC9 ChIP-seq data with the HOXC9 microarray data to generate a list of genes that were bound by HOXC9 and whose expression levels were significantly changed as a result of HOXC9 induction (≥ +1.5 and ≤ −1.5 fold, P <0.01). The analysis revealed that 954 genes or 40.3% of the 2,370 HOXC9-responsive genes are direct targets of HOXC9, with 445 and 509 genes being upregulated and downregulated, respectively (Additional file 7: Table S6). GO analysis of HOXC9 direct target genes revealed a transcriptional program characterized by coordinated regulation of genes critical for neuron differentiation, cell cycle progression, and the DNA damage response.
HOXC9 directly induces a large number of neuronal genes
HOXC9 directly represses a large number of genes essential for cell cycle progression and the DNA damage response
We also identified 32 genes associated with the DNA damage response that were directly repressed by HOXC9 (Figure 5A), accounting for 32.7% (32/98) of the HOXC9-responsive genes involved in the DNA damage response. Figure 5D shows the binding of HOXC9 to the promoter of FANCM and to both the promoter and 3’ region of FEN1. FANCM is a component of the FANCM–FAAP24–MHF protein complex that binds to DNA with interstrand cross-links and is responsible for recruiting the FA core complex to the damaged site . FEN1 (flap endonuclease 1) is essential for DNA replication and repair by removing RNA and DNA 5' flaps .
Collectively, these findings suggest that HOXC9 directly regulates the expression of distinct sets of genes to coordinate the molecular and cellular processes characteristic of neuronal differentiation.
HOXC9 targets E2F6 to the promoters of cell cycle genes
We next sought to determine the molecular basis for HOXC9 regulation of gene expression by identifying HOXC9-interacting proteins. We used a myc-tag antibody to isolate myc-HOXC9 and its associated proteins from nuclear extracts of BE(2)-C/Tet-Off/myc-HOXC9 cells cultured in the absence of doxycycline for 6 days (Additional file 3: Figure S3A). Mass spectrometric analysis of two independent samples identified E2F6 as a HOXC9-interacting protein (Additional file 3: Figure S3B), a well characterized transcriptional repressor that plays a major role in repressing E2F-responsive genes essential for cell proliferation . It is known that E2F family proteins (E2F1-6) share the same core consensus G/CTTTG/C binding site . Interestingly, GSEA revealed significant enrichment of the E2F-binding motif among the genes downregulated by HOXC9 (Additional file 3: Figure S3C). Taken together, these observations suggest that E2F6 has an important role in HOXC9-mediated repression of cell cycle genes.
To determine whether the HOXC9-E2F6 interaction plays a role in recruiting E2F6 to HOXC9 target genes in vivo, we performed anti-E2F6 ChIP using BE(2)-C/Tet-Off/myc-HOXC9 cells before and after HOXC9 induction. HOXC9 induction had no apparent effect on E2F6 expression as determined by microarray gene expression profiling (−1.003 fold). ChIP-qPCR assay revealed that E2F6 was recruited to specific promoter regions of the cell cycle genes CCNB1 and CDCA8 only after HOXC9 induction (Figure 6D). By contrast, no significant binding of E2F6 to the NEFM promoter was observed before and after HOXC9 induction (Additional file 3: Figure S4A). As reported previously, NEFM is a neuronal gene directly activated by HOXC9 during differentiation  (See also Additional file 3: Figure S4B). Together, these data suggest that elevated levels of HOXC9 facilitate the formation of a repressive complex with E2F6, which is then recruited to cell cycle but not neuronal genes during differentiation.
E2F6 is essential for HOXC9-induced cell cycle arrest and transcriptional repression of cell cycle genes
Our findings that HOXC9 can both activate and repress gene transcription are consistent with previous observations from the study of spinal cord development in chick and mouse embryos. In the developing spinal cord, Hoxc9 functions as a transcription activator to promote the fate of preganglionic motor column (PGC) neurons , most likely through its interaction with the transcription factor FoxP1 [43, 44]. However, Hoxc9 can also specify the fate of hypaxial motor column (HMC) neurons by repressing the Hox genes that promote the switch of HMC neurons to the lateral motor column (LMC) neurons . Importantly, our study further demonstrated that within the same population of neuroblastoma cells, HOXC9 could simultaneously activate the genes that promote neuronal differentiation and repress the genes that are essential for cell cycle progression and the DNA damage response. While the molecular basis for the transcription activator function of HOXC9 in neuroblastoma cells remain to be defined, we showed that the ability of HOXC9 to repress cell cycle genes depended on its interaction with the transcription repressor E2F6, a member of the E2F family of transcription factors that have a critical role in the control of cell proliferation .
Cellular differentiation is tightly linked to cell cycle exit, with the differentiated cell containing the G1 content of DNA. The molecular mechanism that couples cell cycle exit and differentiation is not well understood, although it is generally recognized that cell cycle regulators influence differentiation, and cell fate determinants influence the cell cycle [45–48]. A primary example is the CDK inhibitor p27Kip1 as a key regulator that links cell cycle exit and differentiation during development. p27Kip1 induces G1 arrest by associating with CDK/cyclin complexes and inhibits their kinase activity . Overexpression of p27Xic1, a Xenopus homolog of p27Kip1, in Xenopus retina glial progenitor cells promotes both cell cycle exit and differentiation . Knockout and overexpression studies also demonstrate an important role of p27Kip1 in neuronal differentiation in the mouse cerebral cortex by stabilizing Neurogenin 2 , a proneural bHLH transcription factor with a central role in cortical neurogenesis . On the other hand, cell fate determinants can also modulate the expression of p27Kip1 for coordinated regulation of cell cycle exit and differentiation. For instance, Drosophila proneural bHLH proteins cooperate with epidermal growth factor signaling to directly activate the transcription of Dapaco, a homolog of p21Cip/p27Kip1, during the differentiation of photoreceptor cells .
Our findings suggest an alternative mechanism for coupling cell cycle exit and differentiation. HOXC9 does not regulate the expression of CDK inhibitors, including p27Kip1 and p21Cip, and overexpression of either p27Kip1 or p21Cip fails to stop the proliferation of BE(2)-C cells . Rather, HOXC9 induces G1 arrest by directly repressing a large number of genes essential for cell cycle progression through the S to M phases, including cyclin B1, CDCA3, CDCA8, BUB1B, MCM3 and MCM8. This transcriptional repression function of HOXC9 requires E2F6. We found that HOXC9 interacts with E2F6 and recruits it specifically to the promoters of cell cycle genes. E2F6 lacks a transactivation domain and functions as a transcriptional repressor for E2F-responsive genes that drive cell proliferation [54–58]. Mechanistically, E2F6 interacts with chromatin modifiers with transcription repressor activity to establish a repressive chromatin structure. These chromatin modifiers include the DNA methyltransferase Dnmt3b  and polycomb-group (PcG) proteins [60–63]. In our study, we identified HOXC9 and E2F6 within a complex of approximately 1,800 kDa. Whether this complex contains chromatin modifiers is currently under investigation.
Terminal cell differentiation is also tightly associated with a global reduction in DNA damage repair activities [1–3]. The underlying molecular mechanism is not well understood. It has been reported that E1 ubiquitin-activating enzyme can complement nucleotide-excision repair deficiency in extracts from differentiated macrophages, suggesting a role of ubiquitination in the control of the DNA damage response during differentiation . Our study revealed that in HOXC9-induced neuronal differentiation, attenuation of the DNA damage response resulted from global transcriptional repression of DNA repair genes. This finding provides a molecular mechanism for the long observed differentiation-induced radiosensitivity in neuroblastoma cells [20, 24–27]. For HOXC9-induced differentiation, a total of 98 genes with functions in the DNA damage response were significantly downregulated. These genes are involved in all types of DNA damage checkpoints and repair pathways. Importantly, we show that 32 of the 98 genes are direct targets of HOXC9. Thus, to a large extent, HOXC9 coordinates neuronal differentiation and attenuation of DNA repair activities by simultaneously activating neuronal genes and repressing DNA repair genes. Since the DNA damage response and DNA replication machineries share many components, we speculate that the downregulation of DNA repair genes during differentiation is a consequence of repression of cell cycle genes, particularly those involved in DNA replication.
The stem cell model of cancer attributes cancer growth to a subpopulation of cancer stem cells. It has been shown recently that cancer stem cells are intrinsically resistant to ionizing radiation and chemotherapy, as a result of enhanced checkpoint activation and more effective DNA damage repair [7–10]. Since differentiation is associated with global downregulation of DNA repair activities, a combination of differentiation-inducing agents and irradiation or chemotherapy may prove to be a more effective therapeutic strategy for targeting cancer stem cells.
Using neuroblastoma cell differentiation as an experimental system, we delineate a molecular mechanism by which HOXC9 coordinates diverse cellular processes associated with differentiation by directly activating and repressing the transcription of distinct sets of genes.
Cell culture and growth assays
The human neuroblastoma cell line BE(2)-C (CRL-2268, ATCC) with Tet-Off inducible expression of myc-tagged human HOXC9 has been described previously . For E2F6 knockdown, BE(2)-C/Tet-Off/myc-HOXC9 cells were infected with lentiviruses expressing shRNA against E2F6 (TRCN013819, E2F6sh-2, TTTCGAGTTAAATAAACCAGC; TRCN013821, E2F6sh-4, ATTGGTGATGTCATACACTCT; TRCN018201, E2F6sh-6, ATCCAAAGCATCTTCCATTGC; Thermo Fisher Scientific). Cells were cultured in a 1:1 mixture of DMEM and Ham’s nutrient mixture F12 supplemented with 10% fetal bovine serum (Invitrogen-Gibco) in the presence or absence of doxycycline. Cells were examined and phase contrast images captured using an Axio Observer microscope and AxioVision software (Carl Zeiss MicroImaging), and viable cell numbers were determined by trypan blue exclusion assay. For cell cycle analysis, cells were fixed in 70% ethanol, incubated with ribonuclease A (Sigma-Aldrich), and stained with 20 μg/ml propidium iodide (Invitrogen-Gibco). Samples were analyzed using a FACSCalibur system and ModFitLT V3.2.1 software (BD Bioscience).
Microarray gene expression profiling
Total RNA was isolated using Trizol (Invitrogen) from three independent samples of BE(2)-C/Tet-Off/myc-HOXC9 cells cultured in the presence or absence of doxycycline for 6 days. RNA was measured and quality assessed by a NanoDrop spectrophotometer and an Agilent 2100 Bioanalyzer (Agilent Technologies). Affymetrix microarray analysis was performed using the Human Gene 1.0 ST microarray chip. Data were normalized, significance determined by ANOVA, and fold change calculated with the Partek Genomics Suite (Partek Inc.). Gene annotation enrichment analysis was performed with DAVID v6.7 , GSEA , and IPA (Ingenuity® Systems http://www.ingenuity.com) for all significantly changed genes (≥ +1.5 and ≤ −1.5 fold, P < 0.01).
ChIP-seq and ChIP-qPCR
Two independent preparations of BE(2)-C/Tet-Off/myc-HOXC9 cells cultured in the presence or absence of doxycycline for 6 days were used for ChIP. Cross-linked chromatin DNA was sheared through sonication and immunoprecipitated using mouse anti-myc tag (clone 4A6, Millipore) or mouse anti-E2F6 (sc-53273, Santa Cruz Biotechnologies) according to the published procedure . For ChIP-seq, libraries were generated from ChIP genomic DNA samples according to the Illumina ChIP-seq library construction procedure, and sequenced using Illumina Genome Analyzer IIx with a read length of 36 or 76 bp. For ChIP-qPCR, ChIP genomic DNA samples were assayed in triplicate by PCR using an iQ5 real-time PCR system (Bio-Rad) and the following primer sets that cover the promoter regions of CCNB1 (CCNB1_2 and CCNB1_6), CDCA8 (CDCA8_5P200 and CDCA8_5P1K), and NEFM (NEFM_5P1 and NEFM_5P2): CCNB1_2: CCAGAGAGTTGTTGCAACGAT, CTGGAGAGCAGTGAAGCCAGT; CCNB1_6: GGAAGGATTGATCAAACCCAG, AGTCACGGATCCGAAAGAAGG; CDCA8_5P200: GGTATTGCAGAGCCGCCA, CCTCCCCACCAACCCACC; CDCA8_5P1K: TGGTGCCCATCAGGAGCC, GGCTATGGGAGTGATAATC; NEFM_5P1: GCAGAAAGTAATAAGCAACAA, CCTGCCTTCTGTAAAGTATTG; NEFM_5P2: CCTTTCCTGATTACTTACTGA, AGGGACTCCAGACCGAAATAG.
ChIP-seq data analysis
Raw Illumina sequencing reads from the two independent ChIP replicates (rep1, GEO GSM848788 and rep2, GEO GSM848789) in the FASTQ format were cleaned using in-house scripts by trimming sequencing adaptors and low quality bases in both ends (Q < 67 in Illumina 1.5). Cleaned sequences were then mapped to the human genome (hg19) using Novoalign v2.07 for identifying the reads that were mapped uniquely to a single genomic locus. The identified reads from the rep1 ChIP sample (GEO GSM848788) were used for peak calling with Model-based Analysis of ChIP-Seq (MACS v1.4) , and only those peaks with FDR <1% were compared with RefSeq genes in the UCSC genome browser and classified into functional categories such as promoters, 5’-UTRs, exons, introns, 3’-UTRs, downstream, and intergenic regions. To measure the correlation of two HOXC9 replicates, we used 200 bp non-overlapping windows where a tag density is defined as the number of reads in a window. We calculated Pearson correlation coefficient with R > 0.9 being highly correlated. For motif analysis, we extracted 100 bp flanking sequences from predicted peak summits and ran MEME for identifying statistically overrepresented motifs. We performed MAST to search motifs in the peaks using the model built by MEME.
Identification of HOXC9 target genes
Genes with HOXC9-binding peaks that are non-intergenic (i.e., within −5 ~ +5 kb of genes) were defined as HOXC9 target genes. To correlate HOXC9 binding to gene expression, we combined the HOXC9 ChIP-seq data with the HOXC9 microarray data using in-house scripts to generate a list of the genes whose regulatory elements are bound by HOXC9 and whose expression levels are significantly changed (≥ +1.5 and ≤ −1.5 fold, P < 0.01) as the result of HOXC9 induction. The significantly up- and down-regulated HOXC9 target genes were then subjected to gene annotation enrichment analysis with DAVID v6.7, GSEA, and IPA.
Immunoprecipitation and mass spectrometric analyses
BE(2)-C/Tet-Off/myc-HOXC9 cells were cultured in the absence of doxycycline for 6 days and nuclear extracts were prepared following the Dignam protocol  except that buffer C contained 300 mM NaCl. Extracts from 1 × 107 cells were incubated with Protein A/G beads (Invitrogen) coated with 4 μg mouse anti-Myc tag (clone 4A6, Millipore) or mouse IgG for overnight at 4°C. The beads were washed 3 times with buffer C containing 150 mM NaCl, dried in a SpeedVac, re-suspended in a buffer containing 8M urea, 5 mM DTT and 100 mM ammonium bicarbonate, and alkylated with 15 mM iodoacetamide for 1 hour. After alkylation, unreacted iodoacetamide was removed by 15 mM DTT and the urea concentration was diluted to ~1M with a buffer containing 50 mM ammonium bicarbonate and 2 mM CaCl2. Immunoprecipitated proteins were digested with 14 ng/μl sequencing grade trypsin (Promega) for 24 hours at 37°C. The digests were desalted with a Micro Trap desalting cartridge (Michrom BioResources), and tryptic peptides eluted with LC-MS Solvent B (90/10/0.05%: Acetonitrile/water/heptafluorobutyric acid) and dried in a SpeedVac. The digests were analyzed by Nano-HPLC using a Nano Trap column (CL5/61241/00, Michrom BioResources) and an Agilent 1200 Series Nano pump (Agilent Technologies) equipped with a refrigerated autosampler. An Agilent 1200 Series Capillary LC loading pump was used to introduce the sample onto a Captrap cartridge for sample concentration and de-salting.
Data-dependent MS and MS/MS spectra were acquired on an LTQ Orbitrap Discovery (Thermo Fisher Scientific) using 2 micro-scans, with a maximum injection time of 200 ms with 2 Da peak isolation width. Six scan events were recorded for each data acquisition cycle. The first scan event, acquired by the FTMS, was used for full scan MS acquisition from 300–2000 m/z. Data were recorded in the Centroid mode only. The remaining five scan events were used for collisionally activated dissociation (CAD): the five most abundant ions in each peptide MS were selected and fragmented to produce product-ion mass spectra.
Database searching and protein identification
All MS/MS data were analyzed using BioWorks Rev.3.3.1 SP1 (Thermo Fisher Scientific) and X!Tandem (thegpm.org). SEQUEST was set up to search NCBInr_Homosapiens_05262011.fasta (221863 entries) and the human.protein_RefSeq_01192012 database (33376), and X!Tandem was set up to search subsets of the databases. SEQUEST and X!Tandem were searched with a fragment ion mass tolerance of 0.80 Da and a parent ion tolerance of 10.0 PPM. Scaffold (Proteome Software) was used to validate MS/MS-based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than 95.0% probability as specified by the Peptide Prophet algorithm . Protein identifications were accepted if they could be established at greater than 90.0% probability and contained at least 1 identified peptide. Protein probabilities were assigned by the Protein Prophet algorithm . Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. Single-peptide protein identification was accepted only if the protein was independently identified by both SEQUEST and X!Tandem.
Size-exclusion chromatography was performed with a Superose-6 10/300 GL column (24 ml bed volume) and an AKTA purifier (GE Healthcare). Nuclear extracts (0.5 ml) were loaded onto the column equilibrated with PBS, and 0.5 ml fractions were collected and analyzed.
Nuclear extracts or pooled Sepharose-6 fractions were incubated with protein A/G beads coated with mouse anti-Myc tag (clone 4A6), mouse anti-E2F6, or control mouse IgG for 2 hours at 4°C. After washing 3 times with PBS, the beads were suspended in standard SDS sample buffer and analyzed by immunoblotting.
Unless indicated, all antibodies were from Santa Cruz Biotechnologies. Samples were suspended in SDS sample buffer and boiled. Proteins were separated on SDS-polyacrylamide gels, transferred to nitrocellulose membranes, and probed with the following primary antibodies: rabbit anti-cyclin A2 (sc-751, 1:200), rabbit anti-cyclin B1 (sc-752, 1:200), mouse anti-myc-tag (9E10, hybridoma supernatant, 1:10), rabbit anti-E2F6 (sc-22823, 1:200), mouse anti-MEIS2 (63-T, sc-81986, 1:400), mouse anti-NEFM (NF-09, sc-51683, 1:200), and rabbit anti-β-actin (600-401-886, Rockland Immunochemicals, 1:2000). Horseradish peroxidase-conjugated goat anti-mouse and goat anti-rabbit IgG were used as secondary antibodies. Proteins were visualized using a SuperSignal West Pico chemiluminescence kit (Pierce, Thermo Fisher Scientific) and quantified with ImageJ (National Institutes of Health). For visualization and quantification with the Odyssey system, goat anti-mouse IRDye 800, anti-rabbit IRDye 800, anti-mouse IRDye 680, and anti-rabbit IRDye 680 were used as secondary antibodies (LI-COR Biosciences).
All quantitative data were analyzed and presented with GraphPad Prism 5.0f for Mac using unpaired, two-tailed Student’s t-test.
Base excision repair
Chromatin immunoprecipitation and sequencing
Database for annotation visualization and integrated discovery
Dulbecco's modified eagle medium
False discovery rate
Forkhead box protein P1
Gene set enrichment analysis
Hypaxial motor column
Ingenuity pathways analysis
lateral motor column
Nucleotide excision repair
Phosphate buffered saline
Preganglionic motor column
Transcription start site
We thank Drs. LesleyAnn Hawthorn and Sam Chang at the Georgia Regents University Cancer Center Genomics Core for assistance in Microarray and ChIP-seq. This work was supported by grants from the National Institutes of Health (CA124982) and Department of Defense (W81XWH-12-1-0613) to H.-F.D., the National Basic Research Program of China (No. 2012cb114603) to H.C., and the National Natural Science Foundation of China (No. 81172443) to X.W. H.-F.D., H.S., and J.C. are Georgia Cancer Coalition Distinguished Scholars.
- Nouspikel T, Hanawalt PC: DNA repair in terminally differentiated cells. DNA Repair (Amst). 2002, 1 (1): 59-75. 10.1016/S1568-7864(01)00005-2.View ArticleGoogle Scholar
- Simonatto M, Latella L, Puri PL: DNA damage and cellular differentiation: more questions than responses. J Cell Physiol. 2007, 213 (3): 642-648. 10.1002/jcp.21275.View ArticlePubMedGoogle Scholar
- Fortini P, Dogliotti E: Mechanisms of dealing with DNA damage in terminally differentiated cells. Mutat Res. 2010, 685 (1–2): 38-44.View ArticlePubMedGoogle Scholar
- Reya T, Morrison SJ, Clarke MF, Weissman IL: Stem cells, cancer, and cancer stem cells. Nature. 2001, 414 (6859): 105-111. 10.1038/35102167.View ArticlePubMedGoogle Scholar
- Clarke MF, Dick JE, Dirks PB, Eaves CJ, Jamieson CH, Jones DL, Visvader J, Weissman IL, Wahl GM: Cancer stem cells–perspectives on current status and future directions: AACR workshop on cancer stem cells. Cancer Res. 2006, 66 (19): 9339-9344. 10.1158/0008-5472.CAN-06-3126.View ArticlePubMedGoogle Scholar
- Visvader JE, Lindeman GJ: Cancer stem cells in solid tumours: accumulating evidence and unresolved questions. Nat Rev Cancer. 2008, 8 (10): 755-768. 10.1038/nrc2499.View ArticlePubMedGoogle Scholar
- Bao S, Wu Q, McLendon RE, Hao Y, Shi Q, Hjelmeland AB, Dewhirst MW, Bigner DD, Rich JN: Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature. 2006, 444 (7120): 756-760. 10.1038/nature05236.View ArticlePubMedGoogle Scholar
- Phillips TM, McBride WH, Pajonk F: The response of CD24(−/low)/CD44+ breast cancer-initiating cells to radiation. J Natl Cancer Inst. 2006, 98 (24): 1777-1785. 10.1093/jnci/djj495.View ArticlePubMedGoogle Scholar
- Eramo A, Ricci-Vitiani L, Zeuner A, Pallini R, Lotti F, Sette G, Pilozzi E, Larocca LM, Peschle C, De Maria R: Chemotherapy resistance of glioblastoma stem cells. Cell Death Differ. 2006, 13 (7): 1238-1241. 10.1038/sj.cdd.4401872.View ArticlePubMedGoogle Scholar
- Liu G, Yuan X, Zeng Z, Tunici P, Ng H, Abdulkadir IR, Lu L, Irvin D, Black KL, Yu JS: Analysis of gene expression and chemoresistance of CD133+ cancer stem cells in glioblastoma. Mol Cancer. 2006, 5: 67-10.1186/1476-4598-5-67.PubMed CentralView ArticlePubMedGoogle Scholar
- Pearson JC, Lemons D, McGinnis W: Modulating Hox gene functions during animal body patterning. Nat Rev Genet. 2005, 6 (12): 893-904. 10.1038/nrg1726.View ArticlePubMedGoogle Scholar
- Moens CB, Selleri L: Hox cofactors in vertebrate development. Dev Biol. 2006, 291 (2): 193-206. 10.1016/j.ydbio.2005.10.032.View ArticlePubMedGoogle Scholar
- Shah N, Sukumar S: The Hox genes and their roles in oncogenesis. Nat Rev Cancer. 2010, 10 (5): 361-371. 10.1038/nrc2826.View ArticlePubMedGoogle Scholar
- Argiropoulos B, Humphries RK: Hox genes in hematopoiesis and leukemogenesis. Oncogene. 2007, 26 (47): 6766-6776. 10.1038/sj.onc.1210760.View ArticlePubMedGoogle Scholar
- Brodeur GM: Neuroblastoma: biological insights into a clinical enigma. Nat Rev Cancer. 2003, 3: 203-216. 10.1038/nrc1014.View ArticlePubMedGoogle Scholar
- Maris JM: Recent advances in neuroblastoma. N Engl J Med. 2010, 362 (23): 2202-2211. 10.1056/NEJMra0804577.PubMed CentralView ArticlePubMedGoogle Scholar
- Kocak H, Ackermann S, Hero B, Kahlert Y, Oberthuer A, Juraeva D, Roels F, Theissen J, Westermann F, Deubzer H, et al: Hox-C9 activates the intrinsic pathway of apoptosis and is associated with spontaneous regression in neuroblastoma. Cell Death Dis. 2013, 4: e586-10.1038/cddis.2013.84.PubMed CentralView ArticlePubMedGoogle Scholar
- Mao L, Ding J, Zha Y, Yang L, McCarthy BA, King W, Cui H, Ding HF: HOXC9 Links cell-cycle exit and neuronal differentiation and is a prognostic marker in neuroblastoma. Cancer Res. 2011, 71 (12): 4314-4324. 10.1158/0008-5472.CAN-11-0051.PubMed CentralView ArticlePubMedGoogle Scholar
- Seeds NW, Gilman AG, Amano T, Nirenberg MW: Regulation of axon formation by clonal lines of a neural tumor. Proc Natl Acad Sci USA. 1970, 66 (1): 160-167. 10.1073/pnas.66.1.160.PubMed CentralView ArticlePubMedGoogle Scholar
- Jensen L, Linn S: A reduced rate of bulky DNA adduct removal is coincident with differentiation of human neuroblastoma cells induced by nerve growth factor. Mol Cell Biol. 1988, 8 (9): 3964-3968.PubMed CentralView ArticlePubMedGoogle Scholar
- Sidell N: Retinoic acid-induced growth inhibition and morphologic differentiation of human neuroblastoma cells in vitro. J Natl Cancer Inst. 1982, 68 (4): 589-596.PubMedGoogle Scholar
- Abemayor E, Sidell N: Human neuroblastoma cell lines as models for the in vitro study of neoplastic and neuronal cell differentiation. Environ Health Perspect. 1989, 80: 3-15.PubMed CentralView ArticlePubMedGoogle Scholar
- Zha Y, Ding E, Yang L, Mao L, Wang X, McCarthy BA, Huang S, Ding HF: Functional dissection of HOXD cluster genes in regulation of neuroblastoma cell proliferation and differentiation. PLoS One. 2012, 7 (8): e40728-10.1371/journal.pone.0040728.PubMed CentralView ArticlePubMedGoogle Scholar
- Byfield JE, Lee YC, Klisak I, Finklestein JZ: Effect of differentiation on the repair of DNA single strand breaks in neuroblastoma cells. Biochem Biophys Res Commun. 1975, 63 (3): 730-735. 10.1016/S0006-291X(75)80444-X.View ArticlePubMedGoogle Scholar
- McCombe P, Lavin M, Kidson C: Control of DNA repair linked to neuroblastoma differentiation. Int J Radiat Biol. 1976, 29 (6): 523-531. 10.1080/09553007614550621.View ArticleGoogle Scholar
- Lavin MF, McCombe P, Kidson C: DNA replication and post-replication repair in U.V.-sensitive mouse neuroblastoma cells. Int J Radiat Biol. 1976, 30 (1): 31-40. 10.1080/09553007614550781.View ArticleGoogle Scholar
- James M, Mansbridge J, Kidson C: Ultraviolet radiation sensitivity of proliferating and differentiated human neuroblastoma cells. Int J Radiat Biol. 1982, 41 (5): 547-556. 10.1080/09553008214550621.View ArticleGoogle Scholar
- Duester G: Retinoic acid synthesis and signaling during early organogenesis. Cell. 2008, 134 (6): 921-931. 10.1016/j.cell.2008.09.002.PubMed CentralView ArticlePubMedGoogle Scholar
- Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA: DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003, 4 (5): 3-10.1186/gb-2003-4-5-p3.View ArticleGoogle Scholar
- Huang DW, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2008, 4 (1): 44-57. 10.1038/nprot.2008.211.View ArticleGoogle Scholar
- Francis NJ, Landis SC: Cellular and molecular determinants of sympathetic neuron development. Annu Rev Neurosci. 1999, 22: 541-566. 10.1146/annurev.neuro.22.1.541.View ArticlePubMedGoogle Scholar
- Goridis C, Rohrer H: Specification of catecholaminergic and serotonergic neurons. Nat Rev Neurosci. 2002, 3 (7): 531-541.View ArticlePubMedGoogle Scholar
- Ernsberger U: The role of GDNF family ligand signalling in the differentiation of sympathetic and dorsal root ganglion neurons. Cell Tissue Res. 2008, 333 (3): 353-371. 10.1007/s00441-008-0634-4.PubMed CentralView ArticlePubMedGoogle Scholar
- Rajasekharan S, Kennedy T: The netrin protein family. Genome Biol. 2009, 10 (9): 239-10.1186/gb-2009-10-9-239.PubMed CentralView ArticlePubMedGoogle Scholar
- Jung H, Lacombe J, Mazzoni EO, Liem KF, Grinstein J, Mahony S, Mukhopadhyay D, Gifford DK, Young RA, Anderson KV, et al: Global control of motor neuron topography mediated by the repressive actions of a single hox gene. Neuron. 2010, 67 (5): 781-796. 10.1016/j.neuron.2010.08.008.PubMed CentralView ArticlePubMedGoogle Scholar
- Bell SP, Dutta A: DNA replication in eukaryotic cells. Annu Rev Biochem. 2002, 71 (1): 333-374. 10.1146/annurev.biochem.71.110601.135425.View ArticlePubMedGoogle Scholar
- Carmena M, Wheelock M, Funabiki H, Earnshaw WC: The chromosomal passenger complex (CPC): from easy rider to the godfather of mitosis. Nat Rev Mol Cell Biol. 2012, 13 (12): 789-803. 10.1038/nrm3474.PubMed CentralView ArticlePubMedGoogle Scholar
- Kee Y, D'Andrea AD: Expanded roles of the Fanconi anemia pathway in preserving genomic stability. Genes Dev. 2010, 24 (16): 1680-1694. 10.1101/gad.1955310.PubMed CentralView ArticlePubMedGoogle Scholar
- Liu Y, Kao H-I, Bambara RA: FLAP ENDONUCLEASE 1: a central component of DNA metabolism. Annu Rev Biochem. 2004, 73 (1): 589-615. 10.1146/annurev.biochem.73.012803.092453.View ArticlePubMedGoogle Scholar
- Trimarchi JM, Lees JA: Sibling rivalry in the E2F family. Nat Rev Mol Cell Biol. 2002, 3 (1): 11-20.View ArticlePubMedGoogle Scholar
- Xu X, Bieda M, Jin VX, Rabinovich A, Oberley MJ, Green R, Farnham PJ: A comprehensive ChIP-chip analysis of E2F1, E2F4, and E2F6 in normal and tumor cells reveals interchangeable roles of E2F family members. Genome Res. 2007, 17 (11): 1550-1561. 10.1101/gr.6783507.PubMed CentralView ArticlePubMedGoogle Scholar
- Dasen JS, Liu JP, Jessell TM: Motor neuron columnar fate imposed by sequential phases of Hox-c activity. Nature. 2003, 425 (6961): 926-933. 10.1038/nature02051.View ArticlePubMedGoogle Scholar
- Rousso DL, Gaber ZB, Wellik D, Morrisey EE, Novitch BG: Coordinated actions of the forkhead protein Foxp1 and Hox proteins in the columnar organization of spinal motor neurons. Neuron. 2008, 59 (2): 226-240. 10.1016/j.neuron.2008.06.025.PubMed CentralView ArticlePubMedGoogle Scholar
- Dasen JS, De Camilli A, Wang B, Tucker PW, Jessell TM: Hox repertoires for motor neuron diversity and connectivity gated by a single accessory factor, FoxP1. Cell. 2008, 134 (2): 304-316. 10.1016/j.cell.2008.06.019.View ArticlePubMedGoogle Scholar
- Edlund T, Jessell TM: Progression from extrinsic to intrinsic signaling in cell fate specification: a view from the nervous system. Cell. 1999, 96 (2): 211-224. 10.1016/S0092-8674(00)80561-9.View ArticlePubMedGoogle Scholar
- Ohnuma S, Harris WA: Neurogenesis and the cell cycle. Neuron. 2003, 40 (2): 199-208. 10.1016/S0896-6273(03)00632-9.View ArticlePubMedGoogle Scholar
- Galderisi U, Jori FP, Giordano A: Cell cycle regulation and neural differentiation. Oncogene. 2003, 22 (33): 5208-5219. 10.1038/sj.onc.1206558.View ArticlePubMedGoogle Scholar
- Salomoni P, Calegari F: Cell cycle control of mammalian neural stem cells: putting a speed limit on G1. Trends Cell Biol. 2010, 20 (5): 233-243. 10.1016/j.tcb.2010.01.006.View ArticlePubMedGoogle Scholar
- Sherr CJ, Roberts JM: CDK inhibitors: positive and negative regulators of G1-phase progression. Genes Dev. 1999, 13 (12): 1501-1512. 10.1101/gad.13.12.1501.View ArticlePubMedGoogle Scholar
- Ohnuma S, Philpott A, Wang K, Holt CE, Harris WA: p27Xic1, a Cdk inhibitor, promotes the determination of glial cells in Xenopus retina. Cell. 1999, 99 (5): 499-510. 10.1016/S0092-8674(00)81538-X.View ArticlePubMedGoogle Scholar
- Nguyen L, Besson A, Heng JI, Schuurmans C, Teboul L, Parras C, Philpott A, Roberts JM, Guillemot F: p27kip1 independently promotes neuronal differentiation and migration in the cerebral cortex. Genes Dev. 2006, 20 (11): 1511-1524. 10.1101/gad.377106.PubMed CentralView ArticlePubMedGoogle Scholar
- Nieto M, Schuurmans C, Britz O, Guillemot F: Neural bHLH genes control the neuronal versus glial fate decision in cortical progenitors. Neuron. 2001, 29 (2): 401-413. 10.1016/S0896-6273(01)00214-8.View ArticlePubMedGoogle Scholar
- Sukhanova MJ, Deb DK, Gordon GM, Matakatsu MT, Du W: Proneural basic helix-loop-helix proteins and epidermal growth factor receptor signaling coordinately regulate cell type specification and cdk inhibitor expression during development. Mol Cell Biol. 2007, 27 (8): 2987-2996. 10.1128/MCB.01685-06.PubMed CentralView ArticlePubMedGoogle Scholar
- Morkel M, Wenkel J, Bannister AJ, Kouzarides T, Hagemeier C: An E2F-like repressor of transcription. Nature. 1997, 390 (6660): 567-568. 10.1038/37507.View ArticlePubMedGoogle Scholar
- Gaubatz S, Wood JG, Livingston DM: Unusual proliferation arrest and transcriptional control properties of a newly discovered E2F family member, E2F-6. Proc Natl Acad Sci USA. 1998, 95 (16): 9190-9195. 10.1073/pnas.95.16.9190.PubMed CentralView ArticlePubMedGoogle Scholar
- Cartwright P, Muller H, Wagener C, Holm K, Helin K: E2F-6: a novel member of the E2F family is an inhibitor of E2F-dependent transcription. Oncogene. 1998, 17 (5): 611-623. 10.1038/sj.onc.1201975.View ArticlePubMedGoogle Scholar
- Trimarchi JM, Fairchild B, Verona R, Moberg K, Andon N, Lees JA: E2F-6, a member of the E2F family that can behave as a transcriptional repressor. Proc Natl Acad Sci USA. 1998, 95 (6): 2850-2855. 10.1073/pnas.95.6.2850.PubMed CentralView ArticlePubMedGoogle Scholar
- Giangrande PH, Zhu W, Schlisio S, Sun X, Mori S, Gaubatz S, Nevins JR: A role for E2F6 in distinguishing G1/S- and G2/M-specific transcription. Genes Dev. 2004, 18 (23): 2941-2951. 10.1101/gad.1239304.PubMed CentralView ArticlePubMedGoogle Scholar
- Velasco G, Hube F, Rollin J, Neuillet D, Philippe C, Bouzinba-Segard H, Galvani A, Viegas-Pequignot E, Francastel C: Dnmt3b recruitment through E2F6 transcriptional repressor mediates germ-line gene silencing in murine somatic tissues. Proc Natl Acad Sci USA. 2010, 107 (20): 9281-9286. 10.1073/pnas.1000473107.PubMed CentralView ArticlePubMedGoogle Scholar
- Ogawa H, Ishiguro K, Gaubatz S, Livingston DM, Nakatani Y: A complex with chromatin modifiers that occupies E2F- and Myc-responsive genes in G0 cells. Science. 2002, 296 (5570): 1132-1136. 10.1126/science.1069861.View ArticlePubMedGoogle Scholar
- Attwooll C, Oddi S, Cartwright P, Prosperini E, Agger K, Steensgaard P, Wagener C, Sardet C, Moroni MC, Helin K: A novel repressive E2F6 complex containing the polycomb group protein, EPC1, that interacts with EZH2 in a proliferation-specific manner. J Biol Chem. 2005, 280 (2): 1199-1208.View ArticlePubMedGoogle Scholar
- Deshpande AM, Akunowicz JD, Reveles XT, Patel BB, Saria EA, Gorlick RG, Naylor SL, Leach RJ, Hansen MF: PHC3, a component of the hPRC-H complex, associates with E2F6 during G0 and is lost in osteosarcoma tumors. Oncogene. 2007, 26 (12): 1714-1722. 10.1038/sj.onc.1209988.PubMed CentralView ArticlePubMedGoogle Scholar
- Trojer P, Cao AR, Gao Z, Li Y, Zhang J, Xu X, Li G, Losson R, Erdjument-Bromage H, Tempst P, et al: L3MBTL2 protein acts in concert with PcG protein-mediated monoubiquitination of H2A to establish a repressive chromatin structure. Mol Cell. 2011, 42 (4): 438-450. 10.1016/j.molcel.2011.04.004.PubMed CentralView ArticlePubMedGoogle Scholar
- Nouspikel T, Hanawalt PC: Impaired nucleotide excision repair upon macrophage differentiation is corrected by E1 ubiquitin-activating enzyme. Proc Natl Acad Sci USA. 2006, 103 (44): 16188-16193. 10.1073/pnas.0607769103.PubMed CentralView ArticlePubMedGoogle Scholar
- Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005, 102 (43): 15545-15550. 10.1073/pnas.0506580102.PubMed CentralView ArticlePubMedGoogle Scholar
- Lee TI, Johnstone SE, Young RA: Chromatin immunoprecipitation and microarray-based analysis of protein location. Nat Protoc. 2006, 1 (2): 729-748. 10.1038/nprot.2006.98.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, et al: Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008, 9 (9): R137-10.1186/gb-2008-9-9-r137.PubMed CentralView ArticlePubMedGoogle Scholar
- Dignam JD, Lebovitz RM, Roeder RG: Accurate transcription initiation by RNA polymerase II in a soluble extract from isolated mammalian nuclei. Nucleic Acids Res. 1983, 11 (5): 1475-1489. 10.1093/nar/11.5.1475.PubMed CentralView ArticlePubMedGoogle Scholar
- Keller A, Nesvizhskii AI, Kolker E, Aebersold R: Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem. 2002, 74 (20): 5383-5392. 10.1021/ac025747h.View ArticlePubMedGoogle Scholar
- Nesvizhskii AI, Keller A, Kolker E, Aebersold R: A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem. 2003, 75 (17): 4646-4658. 10.1021/ac0341261.View ArticlePubMedGoogle Scholar
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