An incomplete understanding of the biological consequences of chromosome 17q gain remains a barrier to the understanding of high-risk neuroblastoma. 1132 genes are located on 17q (Figure 1—source data 1). We surmised that some of the 17q genes are particularly important for neuroblastoma cell survival. Analysis of the cancer dependency genes in neuroblastoma cell lines screened with the Avana sgRNA library (Meyers et al., 2017) revealed that 114 were essential to neuroblastoma (mean score <−0.4) (Figure 1A, Figure 1—source data 1). Protein interaction network analysis followed by functional annotation revealed that proteins encoded by these 114 essential genes formed distinct but interconnected modules including RNA splicing (i.e. SRSF2, DDX5, DDX42, DHX8), mitochondrial metabolism (i.e. NDUFA8, COX11, SLC25A10, SLC35B1), protein homeostasis (i.e. UBE2O, PSMB3, PSMC5), DNA repair (i.e. BRIP1, BRCA1, RAD51C, RAD51D) and transcriptional regulation (i.e. PHF12, CBX1, SMARCE1, MED1), as well as endocytosis (i.e. CHMP6, CTLC, EPN3, HGS, SNF8, VPS25) (Figure 1B). Children aged ≥18 months with metastatic disease and patients with MYCN amplification tumors are classified as high-risk, which requires a multimodal therapy including induction chemotherapy, surgical resection of primary disease, consolidation with high-dose chemotherapy and stem cell rescue, radiotherapy, and post-consolidation treatment with cis-retinoic acid and immunotherapy (Morgenstern et al., 2019). Using these 114 genes as a signature, we found that 81 of them were highly expressed in high-risk neuroblastomas, which were enriched with MYCN amplification (Figure 1C). Correspondingly, neuroblastomas with high expression levels of this gene signature were associated with a poorer event-free (time from treatment until the cancer progresses) and overall survival (time from treatment to death) of patients in two large clinic cohorts (Figure 1D and E, Figure 1—figure supplement 1). Interestingly, in low-risk neuroblastoma patients, high expression of the 114 essential genes was associated with poor event-free and overall survival, while no difference was observed in high-risk patients (Figure 1—figure supplement 2). These data demonstrate that 17q genes are involved in essential biological processes and highly expressed in high-risk neuroblastomas. Nevertheless, the 114 essential genes cannot further stratify the high-risk patients.

JMJD6 was among these 114 essential genes. To understand the role of JMJD6, we examined the genetic features of JMJD6 in neuroblastoma and other types of cancers. Among the genes encoding JmjC domain containing proteins, JMJD6 was the only one that was frequently amplified in neuroblastoma (Figure 2A). High JMJD6 expression was associated with poor event-free outcome, as shown by Kaplan-Meier analysis (Figure 2B). Further analysis of JMJD6 expression in low-risk and high-risk patients showed that high levels of JMJD6 expression was associated with poor event-free and overall survival in both low-risk and high-risk patients (Figure 2—figure supplement 1), indicating that JMJD6 is high-risk factor regardless of disease status. To examine whether JMJD6 amplification is limited to specific tumor types, we explored genomic data from different cancers using the cBioportal program (Cerami et al., 2012). JMJD6 was amplified across multiple types of adult cancers such as breast and liver cancer (Figure 2—figure supplement 2A), and correlated with worse relapse-free survival (Figure 2—figure supplement 2B). We further compared the RNA-seq expression of JMJD6 in 2337 samples across over 20 pediatric cancer subtypes and found that JMJD6 showed the highest expression levels in neuroblastoma (Figure 2—figure supplement 2C), suggesting that JMJD6 might be particularly important in neuroblastoma. We validated this hypothesis using shRNA knockdown of JMJD6 in MYCN-amplified cells (BE2C, SIMA, KELLY, IMR32) and non-MYCN-amplified cells (SK-N-AS and CHLA20). The results showed that loss of JMJD6 greatly reduced the colony numbers in all tested cell lines (Figure 2C and Materials and methods), demonstrating that JMJD6 is essential to neuroblastoma cells regardless of MYCN amplification. Neuroblastic tumors comprise a histological spectrum that ranges from less-differentiated neuroblastoma to well-differentiated ganglioneuroma. The extent of differentiation in the tumor cells is correlated with prognostic significance (Brodeur and Bagatell, 2014). We noticed that the loss of JMJD6 led to neurite outgrowth (Figure 2—figure supplement 2D), a unique structure of neuroblastoma cells differentiating in vitro. This morphological change suggests that JMJD6 is required to regulate cellular differentiation. Lastly, we validated that JMJD6 is essential to neuroblastoma growth in MYCN-amplified (BE2C) and MYC-overexpressed (SK-N-AS) xenograft models (Figure 2D and E). Taken together, these data demonstrate that loss of JMJD6 function impedes neuroblastoma cell survival and tumor growth.

Next, we investigated whether gain of function of JMJD6 could facilitate MYC-mediated oncogenic transformation. To test this, we used an NIH3T3 transformation assay that provides a straightforward method to assess the transforming potential of an oncogene, which may lead to morphological transformation and loss of contact inhibition, a typical feature of cellular transformation. Like the GFP control (Figure 2—figure supplement 3A), we found that NIH3T3 cells with overexpressed JMJD6 stopped proliferation after being confluent (Figure 2—figure supplement 3B), indicating JMJD6 alone is unable to transform NIH3T3 cells. However, overexpression of MYCN induced foci formation with enhanced cell death (Figure 2—figure supplement 3C). Importantly, NIH3T3 cells with overexpressed MYCN and JMJD6 lost contact inhibition, accompanied with morphological change, and formed larger foci (Figure 2—figure supplement 3D), indicating that JMJD6 enhances MYCN activity to transform NIH3T3 cells. Interestingly, MYCN alone also reprogrammed metabolism of NIH3T3 cells as shown by the color change of the media, which was largely rescued by co-expression of JMJD6 (Figure 2—figure supplement 3E), suggesting that cells with enhanced lactate production by MYCN were directed to oxidative phosphorylation by JMJD6. It is believed that the cell of origin of neuroblastoma is the progeny of neural crest cells (Kameneva et al., 2021; Jansky et al., 2021). We therefore tested the role of JMJD6 in MYC-mediated transformation using JoMa1 (Maurer et al., 2007), a cell line derived from murine neural crest, by transducing GFP, JMJD6, MYCN, and JMJD6/MYCN (Figure 2F). While JMJD6 showed no difference from GFP control in regulating cell proliferation, MYCN slightly enhanced cell proliferation (Figure 2G). However, the combination of JMJD6 and MYCN remarkably increased cell proliferation, mirrored by the colony formation assay which showed JMJD6/MYCN-induced rapid growth of colonies with distinct transformation morphology (Figure 2H). Implantation of each group into immune-deficient mice led to tumor development of MYCN and JMJD6/MYCN groups (Figure 2I). However, JMJD6/MYCN group tumors appeared to grow faster than the MYCN alone tumors. Taken together, these data indicate that JMJD6 enhances the MYC-mediated transformation, demonstrating the oncogenic role of JMJD6.

We surmised that loss of function of genes in the same functional module/pathway may induce similar effects across different cell lineages, which in turn corroborates the hypothesis that JMJD6 is a player in that signaling pathway. To test this hypothesis, we analyzed the dependency correlation of JMJD6 knockout and other genes (defined as co-dependency genes if they are positively correlated), by using the DepMap data (https://depmap.org) that includes genome-wide knockout in 1027 cell lines of more than 20 cancer types (Figure 3A–D; Figure 3—source data 1), followed by pathway enrichment. The data showed that JMJD6 co-dependency genes were significantly and positively correlated with spliceosome/mRNA splicing (i.e. RBM39, SF3B1), ubiquitin-mediated proteolysis and endocytosis and a number of 17q25 genes (Figure 3A and C), which mirrored the pathway network of 17q essential genes in neuroblastoma (Figure 1B). JMJD6 knockout was negatively correlated with the knockout of genes housed at chromosome 1p, which is frequently deleted in high-risk neuroblastoma, and oxidative phosphorylation as well as protein translation (Figure 3B and D).

BRD4 is known to regulate MYC expression (Puissant et al., 2013; Delmore et al., 2011). Previous studies have shown that JMJD6 and BRD4 interact to regulate gene transcription (Wong et al., 2019; Miller et al., 2017), suggesting that JMJD6 might directly modulate MYC expression. To assess this possibility, we knocked down JMJD6 in neuroblastoma cell lines BE2(C) and SK-N-AS, which express MYCN and MYC, respectively, for RNA-seq analysis. The sequencing data were analyzed for differential gene expression (Figure 3—figure supplement 3). Interestingly, loss of JMJD6 showed minimal impact on expression of MYCN in BE2C cells or MYC in SK-N-AS cells (Figure 3—figure supplement 1A), and western blot analysis did not show alteration of MYCN expression although MYC protein was slightly downregulated by loss of JMJD6 (Figure 3—figure supplement 1B). However, BRD4 inhibitors drastically inhibited both MYCN and MYC expression in neuroblastoma cells (Lovén et al., 2013; Chapuy et al., 2013; Puissant et al., 2013), suggesting that JMJD6 inhibition has a distinct effect from the BRD4 inhibition. Gene set enrichment analysis (GSEA) for pathway engagement for the genes commonly downregulated or upregulated in both cell lines revealed that loss of JMJD6 most significantly repressed the expression of genes involved in pre-mRNA splicing, histones, and cell cycle G1/S checkpoint (Figure 3E), and enhanced the pathways involved in mitochondrial functions and heat shock response (Figure 3E). Interestingly, the genes transcribed from the mitochondrial genome were elevated in both cell lines (Figure 3—figure supplement 1C), suggesting that JMJD6 directly or indirectly regulates the transcription of mitochondrial genome. These data are consistent with the co-dependency pathways of JMJD6 (Figure 3A–D). Depletion of JMJD6 in both cell lines led to the downregulation of MYC signaling pathways (Figure 3—figure supplement 2A), although ranked behind the pathways of splicing and metabolism, suggesting that the MYC pathways are not primarily regulated by JMJD6. These data indicate that JMJD6 does not regulate the gene expression of the MYC family of transcription factors but might indirectly regulate the MYC pathway. Nevertheless, loss of JMJD6 induced an induction of gene signatures related to axon, neuron projection, and Schwann cell differentiation (Figure 3—figure supplement 2B), which is consistent with the induction of neurite outgrowth observed in BE2C cells after JMJD6 knockdown (Figure 2—figure supplement 2D), indicative of neuroblastoma cell differentiation.

To verify if JMJD6 regulates pre-mRNA splicing, we analyzed the RNA-seq using two algorithms (Wu et al., 2018). The first one is event-based analysis to identify the altered exon splicing (Figure 3F, Figure 3—source data 1). We found that knockdown of JMJD6 dominantly affects the exon skipping although other splicing events were also altered, albeit with a much smaller number (Figure 3F). Pathway analysis of common events in both BE2C and SK-N-AS cells demonstrated that genes involved in metabolism and splicing are most significantly affected by loss of function of JMJD6 (Figure 3G). Using the second algorithm of RNA splicing analysis previously developed (Figure 3H, Figure 3—source data 1) that allows discovery of new isoforms of genes generated through alternative splicing (Wu et al., 2018), we identified 580 genes in BE2C cells and 1018 genes in SK-N-AS cells undergoing alternative splicing after JMJD6 knockdown, 133 of which were shared by both (Figure 3H, Figure 3—figure supplement 3, Figure 3H—source data 1). The alternatively spliced genes were involved in a variety of pathways (Figure 3—figure supplement 3). Among the 133 commonly alternatively spliced genes, KEGG pathway enrichment analysis showed that metabolic pathway genes were the only ones significantly enriched (Figure 3H, Figure 3—source data 1), most of which are involved in mitochondrial bioenergetics and folate metabolism (Figure 3H). Collectively, these data demonstrate that JMJD6 regulates RNA splicing of genes engaged in mitochondrial metabolism, being one of the key mediators of the 17q locus activity in neuroblastoma.

Overactive MYC signaling leads to altered macromolecular processing machineries in response to an increase in total RNA and protein synthesis (Hsu et al., 2015). MYC is also a master regulator of cancer metabolism involved in ribosomal and mitochondrial biogenesis, glucose and glutamine metabolism, and lipid synthesis, leading to the acquisition of bioenergetic substrates enabling the cancer cell to grow and proliferate (Dang, 2013; Miller et al., 2012; Gordan et al., 2007). ‘Glutamine addiction’ is one feature of MYC-driven cancer (Wise and Thompson, 2010). The pre-mRNA splicing altered by JMJD6 knockdown included GLS, the key enzyme of glutaminolysis, prompting us to investigate the GLS splicing mediated by JMJD6 knockdown. GLS is known to catalyze the conversion of glutamine to glutamate, and is alternatively spliced to form two isoforms, GAC and KGA (Porter et al., 2002), with different cellular localization and catalytic capacities (Cassago et al., 2012). The GAC isoform is more frequently upregulated in cancer cells than KGA (Wang et al., 2010), and has been shown to be regulated by MYC (Gao et al., 2009; Wise et al., 2008; Yuneva et al., 2007), leading to a ‘glutamine addiction’ phenotype in MYC-driven tumors (Wise and Thompson, 2010). We found that loss of JMJD6 led to a splicing switch from the GAC isoform (with exons 1–15) to the KGA isoform (with exons 1–14 and 16–19) (Figure 4A, Figure 4—figure supplement 1), which was further confirmed by isoform-specific real-time (RT) polymerase chain reaction (PCR) (Figure 4B). We then investigated the expression of GAC/KGA at the protein levels after JMJD6 knockdown. Western blot showed that the KGA isoform was increased after JMJD6 knockdown in all three tested cell lines, MYC overexpressed SK-N-AS, BE2C and SIMA with MYCN amplification (Figure 4C). Then, we further validated the JMJD6 effect on GLS isoform expression by using a luciferase reporter that indicates the isoforms of GAC and KGA. Indeed, JMJD6 knockdown significantly increased the expression of the KGA reporter (Figure 4D). RNA immunoprecipitation showed that JMJD6 bound to GLS RNA (Figure 4E), suggesting that JMJD6 may directly regulate the splicing of GLS. We reasoned that if the regulation of GLS splicing by JMJD6 was a bone fide mechanism, the expression levels of JMJD6 would correlate with the levels of GAC/KGA in tumors. Indeed, JMJD6 was positively correlated with GAC and negatively correlated with KGA in two independent neuroblastoma cohorts (Figure 4F), supporting the hypothesis that JMJD6 is required to maintain the high ratio of GAC/KGA in cancer cells by controlling their alternative splicing. Clinical relevance of GAC and KGA in neuroblastoma was evidenced by the findings that high GAC was associated with a worse event-free survival and high KGA was associated with a better event-free survival (Figure 4G and H), suggesting that the GAC/KGA ratio may play a role in cancer progression.

To understand if GAC and KGA play distinct roles in neuroblastoma cells, we transduced GAC or KGA into BE2C and SKNAS cells. However, introduction of either GAC or KGA in BE2C cells or SKNAS cells promoted colony formation (Figure 5A–D), indicating that enhanced glutaminolysis by either GAC or KGA overexpression is pro-proliferative. Interestingly, RNA-seq analysis revealed that GAC and KGA share common targets but also have distinct targets in both BE2C and SKNAS cells (Figure 5—figure supplement 1A, B). KEGG pathway analysis of the commonly upregulated genes by GAC and KGA showed that both GAC and KGA promoted the PI3K-AKT and MAPK pathways in both cell lines (Figure 5E and F). However, for the genes commonly downregulated by GAC and KGA, KEGG pathway showed that calcium signaling was significantly downregulated in BE2C cells while steroid biosynthesis was significantly downregulated in SKNAS cells (Figure 5E and F). Then, we examined GAC- and KGA-specific effects in both cell lines. In BE2C cells, GAC expression promoted Hedgehog, Hippo, and Notch signaling pathways (Figure 5—figure supplement 1C), while KGA expression promoted mitophagy signaling pathway (Figure 5—figure supplement 1D). In SKNAS cells, GAC expression promoted PI3K-AKT and Hippo pathways (Figure 5—figure supplement 1E) and KGA expression promoted TNF signaling pathway (Figure 5—figure supplement 1). Thus, while GAC and KGA catalyzes the same chemical reaction by converting glutamine to glutamate, they also induce non-redundant cellular responses in neuroblastoma cells, suggesting that both isoforms might be important for neuroblastoma cells. Indeed, knockdown of both isoforms (GLS) showed a greater cancer cell killing phenotype than that of GAC or KGA alone did (Figure 5G–I).

To understand the mechanism of JMJD6 in regulating splicing in neuroblastoma, we performed an unbiased identification of JMJD6-interacting partners by introducing a FLAG-tagged JMJD6 into SK-N-AS and BE2C cells, followed by immunoprecipitation to pull down the JMJD6-associated complex and protein identification with mass spectrometry (Figure 6A). We found that JMJD6 mainly interacted with two classes of proteins which are involved in RNA splicing and protein synthesis in both cell lines, respectively (Figure 6A, Figure 6—figure supplement 1). We then validated the interactions of JMJD6 with splicing factors using immunoprecipitation and western blot, and demonstrated that JMJD6 formed a complex with these RNA binding proteins, including RBM39 (Figure 6B), a therapeutic target of high-risk neuroblastoma (Singh et al., 2021). Since JMJD6 also interacted with several molecules involved in protein translation, we investigated if JMJD6 also regulates protein synthesis by using an approach, Click-iT AHA, to label the newly synthesized proteins, followed by western blot assessment (Figure 6C). Interestingly, overexpression of JMJD6 greatly reduced total protein synthesis (Figure 6C), suggesting that JMJD6 may antagonize protein production.

Then, we determined if the splicing factors interacting with JMJD6 also regulate GLS isoform expression. Among the splicing factors with which JMJD6 interacted, CPSF6 has been previously shown to regulate the alternative splicing of GLS (Masamha et al., 2016). We validated the function of CPSF6 in neuroblastoma cells and found that, indeed, loss of CPSF6 led to a dramatic switch from GAC to KGA in BE2C and SK-N-AS cells (Figure 6D and E). Previous studies showed that JMJD6 and U2AF2 (U2AF65) interact to regulate splicing (Webby et al., 2009; Yi et al., 2017). We also found that knockdown of U2AF2 resulted in a similar phenotype to JMJD6 knockdown in that the expression of KGA isoform was greatly increased in both cell lines (Figure 6D and E). These data collectively support the functions of JMJD6 in regulating the splicing of metabolic genes and protein homeostasis in MYC-driven neuroblastoma.

To further dissect the biological consequences of loss of function of JMJD6, we created stable JMJD6 knockout clones (Figure 7—figure supplement 1) and defined the metabolite spectrum affected by loss of function by using liquid chromatography with tandem mass spectrometry (LC-MS/MS). JMJD6 knockout greatly reduced the production of TCA cycle intermediates (i.e. oxoglutarate, fumarate) and nucleoside triphosphate (ATP, CTP, GTP) (Figure 7A), indicating that JMJD6 is a key bioenergetic regulator in cancer cells. Pathway analysis revealed that the reduced metabolites were involved in the Warburg effect, TCA, pentose phosphate pathway, and mitochondrial electron transport chain (Figure 7B), all of which are critical for providing cancer cell bioenergetics for proliferation and survival. Oxoglutarate (α-ketoglutarate) and fumarate are downstream products of glutaminolysis (Figure 7c). We reasoned that cellular metabolites such as glutamate and oxoglutarate may predict the cytotoxic effect of loss of function of JMJD6. If cells have higher levels of glutamate and oxoglutarate, they might be less dependent on JMJD6 due to their higher capacity of buffering against reduced glutaminolysis. To test this hypothesis, we used DepMap data that included 225 metabolites in 928 cancer cell lines from over 20 lineages (Li et al., 2019), and analyzed the correlation of each metabolite with JMJD6 gene dependency. The data showed that cells with high levels of AMP, glutamate, alanine, 2-hydroxyglutarate, and 2-oxoglutarate were more resistant to JMJD6 knockout, while cells with high levels of lactose and sucrose were more sensitive to JMJD6 knockout (Figure 7C). High levels of AMP activate AMP kinase, consequently leading to enhanced fatty acid oxidation to stimulate ATP production while alanine can be converted to pyruvate to provide acetyl-CoA to fuel the TCA cycle (Figure 7C). Therefore, high levels of AMP and alanine may provide cells alternative bioenergetics sources for survival. These data further indicate that JMDJ6 function is wired into the regulation of mitochondrial metabolism.

Dysregulated splicing as a vulnerability of MYC-driven cancers provides a rationale to target neuroblastoma by using splicing inhibitors as a therapeutic approach. We and others have recently reported that indisulam, a ‘molecular glue’ that selectively degrades the splicing factor RBM39, is exceptionally effective at causing tumor regression in multiple high-risk neuroblastoma models without overt toxicity (Singh et al., 2021; Nijhuis et al., 2022), suggesting indisulam has translational potential. Understanding the factors determining the efficacy of indisulam or any other drug is critical for developing precision therapy, combination therapy, or preventing therapy resistance. In addition to complexing together (Figure 6A and B), JMJD6 and RBM39 exhibit significant correlation of co-dependency in cancer cells, namely, cancer cells have similar dependency on JMJD6 and RBM39 for survival (Figure 8A). These data indicate that JMJD6 may play a role in modulating the effect of indisulam. To test this hypothesis, we performed GSEA to identify the differential pathways between indisulam-sensitive and indisulam-less-sensitive neuroblastoma cells. It turned out that histone lysine demethylase (HDM) genes, including JMJD6, are present in the most significantly enriched gene signature in indisulam-sensitive cells (Figure 8B, C, and D). Indeed, knockout of JMJD6 led to partial but significant resistance to indisulam treatment of SK-N-AS cells (Figure 8E–G) and BE2C cells (Figure 8H–J), supporting that cells with high JMJD6 expression are more dependent on RBM39. Since we found that JMJD6 plays a key role in modulating glutaminolysis, we tested if expression of GAC or KGA could affect the activity of indisulam. Interestingly, overexpression of either GAC and KGA renders BE2C and SK-N-AS cells more resistant to indisulam treatment (Figure 8K–N), suggesting that enhanced glutaminolysis may confer therapeutic resistance to spliceosome inhibition.

Our study focused on the understanding of JMJD6 function in neuroblastoma cell lines. In the future, we will consolidate our study by expanding our models to patient-derived xenografts, organoids, and neuroblastoma genetic models, in comparison with non-cancerous cells. Although we have identified a conserved interactome of JMJD6 in neuroblastoma cells, it remains to be determined whether it is neuroblastoma-specific and essential to MYC-driven cancers. The genome-wide RNA binding by JMJD6 in cancer cells and normal cells coupled with isotope labeling to dissect the metabolic effect of JMJD6 will enhance our understanding of the biological functions of JMJD6, awaiting future studies. Inability to target the enhanced pre-mRNA splicing of metabolic genes in MYC-driven cancer cells by pharmacological inhibition of JMJD6 is another limitation, due to lack of selective and potent JMJD6 inhibitors.

KELLY (ECACC, 92110411), SIMA (DSMZ, ACC164), BE2C (ATCC, CRL2268), IMR32 (ATCC, CCL127), SK-N-AS (ATCC, CRL2137), CHLA20 (COG) were cultured in 1× RPMI1640 (Corning, 15-040-CV) supplemented with 10% fetal bovine serum (Sigma-Aldrich, F2442), 1% L-glutamine (Corning, A2916801). NIH3T3 (ATCC, CRL1658) and 293T (ATCC, CRL3216) cells were cultured in 1 DMEM supplemented with 10% fetal bovine serum (Sigma-Aldrich, F2442), 1% L-glutamine (Corning, A2916801). All cells were maintained at 37°C in an atmosphere of 5% CO₂. JoMa1 cells kindly provided by Dr Schulte (Department of Pediatric Oncology and Hematology, University Children’s Hospital Essen, Essen, Germany) were cultured in NCC Medium: DMEM (4.5 mg/ml glucose, L-glutamine, pyruvate): Ham’s F12 (1:1) was supplemented with: 1% N2-Supplement (Invitrogen, no. 17502-048), 2% B27-Supplement (Invitrogen, no. 17504-044), 10 ng/mL EGF (Invitrogen), 1 ng/mL FGF (Invitrogen), 100 U/mL Penicillin-Streptomycin (Invitrogen), and 10% Chick-Embryo-Extract (Gemini Bio-Products, CA, USA). Neural crest culture medium was supplemented with 200 nM 4-OH-tamoxifen (Sigma no. H7904) in routine culture to ensure nuclear localization of c-MycERT and JoMa1 cell proliferation. JoMa1 cells were grown on cell culture flask/dish coated with fibronectin, NCC Medium supplemented with 200 nm 4-OHT was changed daily. Cells were passaged after 3–4 days in culture when 70% confluence was reached (4×10⁶ cells/10 cm dish).

All human-derived cell lines were validated by short tandem repeat profiling using PowerPlex 16 HS System (Promega) once a month. Additionally, a PCR-based method was used to screen for mycoplasma once a month employing the LookOut Mycoplasma PCR Detection Kit (MP0035, Sigma-Aldrich) and JumpStart Taq DNA Polymerase (D9307, Sigma-Aldrich) to ensure cells were free of mycoplasma contamination.

GAPDH (Cell Signaling Technology, 5174s, rabbit antibody), MYCN (Santa Cruz Biotechnology, 53993, mouse antibody), FLAG (Sigma, F1804, mouse antibody), Biotin (Bethyl Laboratories, A150-109A, rabbit antibody), ACTIN (Sigma, A3854, mouse antibody), PUF60 (Thermo Fisher, PA5-21411, rabbit antibody), U2AF2 (Novus Biologicals, NBP2-04140, rabbit antibody), CPSF6 (Bethyl Laboratories, 357A, rabbit antibody), DHX40 (Novus Biologicals, NBP1-91834, rabbit antibody), DHX8 (Abcam, AB181074, rabbit antibody), LUC7L1 (Novus Biologicals, NBP2-56401, rabbit antibody), LUC7L2 (Novus Biologicals, NBP2-33621, rabbit antibody), LUC7L3 (Novus Biologicals, NBP1-88053, rabbit antibody), RBM39 (ATLAS, HPA001519, rabbit antibody), GLS (KGA-specific) (Proteintech, 20170-1-AP, rabbit antibody), GLS (GAC-specific) (Proteintech, 19958-1-AP, rabbit antibody), JMJD6 (ATLAS, HPA059156, rabbit antibody), JMJD6 (Santa Cruz Biotechnology, sc-28348, mouse antibody).

MSCV-IRES-GFP and MSCV-IRES-mCherry were obtained from St Jude Vector Core. Human JMJD6 and murine MYCN were subcloned into MSCV-IRES-GFP and MSCV-IRES-mCherry, respectively. The MSCV-CMV-CMV-FLAG-HA-JMJD6 was purchased from Addgene (Addgene # plasmid 31358). The retrovirus packaging was done as described in the following procedure. Briefly, HEK93T cells were transfected with viral vectors by combining 5 μg of target vector, 4.4 μg of pMD-old-gag-pol, and 0.6 μg of VSV-G plasmids in 400 µL of DMEM without serum or L-glutamine. PEIpro transfection reagent (Polyplus 115-010) was added at 2:1 (PEIpro μL:μg of plasmid) per 100 mm dish of cells and mixed well, and incubated at room temperature for at least 20 min, prior to adding cells. The following day, fresh medium was added to cells. For 3–4 days, viral media was harvested and replaced twice per day. Viral media was centrifuged at 1500 RPM for 10 min and filtered through a 0.45 µm vacuum filter. Virus was concentrated by ultracentrifugation at 28.5 kRPM for 2 hr at 4°C, aspirated, and resuspended in either OptiMEM or phosphate-buffered saline (PBS), aliquoted, and frozen at –80°C until use. cDNAs for GAC and KGA were synthesized by GenScript and cloned into pGenLenti vector for virus packaging.

25 µM of each siRNA oligo was resuspended in 500 µL of prewarmed Opti-MEM, reduced serum medium (Gibco Life Technologies # 31985-070) in six-well plates. To each well, 7 μL of RNAiMax (Invitrogen Lipofectamine RNAiMAX transfection reagent 13778100) was added, mixed, and left at room temperature for 10 min. After incubation, 100,000 cells of each indicated cell line were added to each well in a total of 2 mL volume with RPMI medium supplemented with 10% FBS. JMJD6 siRNA#63, 5-CCAAAGUUAUCAAGGAAA-3; JMJD6 siRNA#75, 5-CAGUGAAGAUGAAGAUGAA-3. U2AF2 siRNA#1 AGAAGAAGAAGGUCCGU; U2AF2 siRNA#2 GUGGCAGUUUCAUAUUUG. CPSF6 siRNA#1 GGAUCACCUUCCAAGACA. CPSF6 siRNA#2 AGAACCGUCAUGACGAUU. GLS1 siRNA, CAACTGGCCAAATTCAGTC; GAC siRNA, CCTCTGTTCTGTCAGAGTT; KGA siRNA, ACAGCGGGACTATGATTCT.

Cells were washed twice with ice-cold PBS and directly lysed on ice with 2× sample loading buffer (0.1 M Tris HCl [pH 6.8], 200 mM dithiothreitol [DTT], 0.01% bromophenol blue, 4% sodium dodecyl sulfate [SDS], and 20% glycerol). On ice, cell lysates were sonicated once with a 5 s bursts at 40% amplitude output (Sonics, VIBRA CELL) followed by 25 min heating at 95°C. After the cell lysates were centrifuged at 13,000×g at room temperature for 2 min, 10–20 µL of the cell lysates were separated on 4–15% Mini-PROTEAN TGX Stain-Free Protein Gels from Bio-Rad and transferred to methanol-soaked polyvinylidene difluoride membranes (Millipore). Lysates for RBM39 G268V mutant cell lines and DCAF15 genetically modified cells were generated as previously described (Han et al., 2017). Membranes were blocked in PBS buffer supplemented with 0.1% Tween 20 and 5% skim milk (PBS-T) and incubated for 1 hr at room temperature under gentle horizontal shaking. Membranes were incubated overnight at 4°C with the primary antibodies. The next day, membranes were washed three times (for 5 min) with PBS-T at room temperature. Protected from light, membranes were then incubated with goat anti-mouse or goat anti-rabbit HRP-conjugated secondary antibodies (1:5000) for 1 hr at room temperature, followed by three 5 min washes with PBS-T at room temperature. Lastly, membranes were incubated for 1 min at room temperature with SuperSignal West Pico PLUS Chemiluminescent Substrate (34580, Thermo Fisher Scientific) and the bound antigen-antibody complexes were visualized using Odyssey Fc Imaging System (LI-COR Corp., Lincoln, NE, USA).

RNA was extracted using RNeasy Plus Mini Kit (QIAGEN, reference # 74136) following the manufacturer’s protocol. cDNA was prepared in 20 µL reaction from 500 ng of total RNA using Superscript IV First Strand Synthesis System (Invitrogen, reference # 1809105) kit. RT-PCRs were run in triplicates (n=3) in the 7500 Real-Time PCR system by Applied Biosystems (Thermo Fisher Scientific) using power SYBR Green PCR master mix (Applied Biosystems, reference # 4367660). ΔΔCT methods were applied to analyze the results. The following primers were used to perform the quantitative Real-Time PCR- GAPDH (Forward: AACGGGAAGCTTGTCATCAATGGAAA, Reverse: GCATCAGCAGAGGGGGCAGAG), GAC (Forward: GAGGTGCTGGCCAAAAAGCCT, Reverse: AGGCATTCGGTTGCCCAAACT), KGA (Forward: CTGCAGAGGGTCATGTTGAA, Reverse: ATCCATGGGAGTGTTATTCCA).

The GAC and KGA cDNAs were synthesized by Genscript company and cloned into pGenLenti vector. The TRC lentiviral-based shRNA knockdown plasmids for JMJD6 were purchased from Horizon Discovery (sh#46: RHS3979-201781036, TTAAACCAGGTAATAGCTTCG; sh#47: RHS3979-201781037, ATCTTCACTGAGTAGCCATCG). The lentiviral shJMJD6 and shControl (pLKO.1) particles were packaged by Vector Lab at St Jude. Briefly, HEK293T cells were transfected with shRNA constructs and helper plasmids (pCAG-kGP1-1R, pCAG4-RTR2, and pHDM-G). The 48 and 72 hr post-transfection replication-incompetent lentiviral particles were harvested and transduced into cells with 8 μg/mL of polybrene. 48 hr later, 1 μg/mL of puromycin was added for selection for additional 48 hr before injection into mice or immunoblotting.

Genetically modified neuroblastoma cells were generated by using CRISPR-Cas9 technology. Briefly, 400,000 NB cells were transiently co-transfected with 100 pmol of chemically modified gRNA (GGACTCTGGAGCGCCTAAAA) (Synthego), 33 pmol of Cas9 protein (St Jude Protein Production Core), 200 ng of pMaxGFP (Lonza), and, using solution P3 and program DS-150 in small cuvettes according to the manufacturer’s recommended protocol. Five days post-nucleofection, cells were sorted for GFP+ (transfected) cells and plated as single cells into 96-well plates. Cells were clonally expanded and screened for the desired modification using targeted next-generation sequencing followed by analysis with CRIS.py (https://pubmed.ncbi.nlm.nih.gov/30862905/).

Matrigel was kept at 4°C to being liquified for 6 hr. 50 μL of Matrigel per 1 cm² area was added to 24-well plate without air bubbles. The 24-well plate was kept at 37°C in cell culture incubator till it was solidified. 200 of JoMa1 cells transduced with GFP, JMJD6, MYCN, MYCN+JMJD6 in DMEM:F12 enriched media without tamoxifen were seeded onto the 24-well coated with Matrigel. This was done in triplicate. Cells were checked daily, and media were changed every 3 days without disturbing the Matrigel by removing and adding media gently. To stain the colonies, cells were fixed by formaldehyde (3.7% in PBS) for 2 min at room temperature, followed by permeabilization with 100% methanol (not ice-cold) for 20 min at room temperature. The colonies were stained by 0.4% crystal violet.

After removing media, cells were washed with Dulbecco’s PBS (DPBS) without calcium or magnesium (DPBS, Lonza) and treated with 4% formaldehyde in PBS (paraformaldehyde [PFA]) for 20 min. Once PFA was removed, cells were stained with 0.1% crystal violet stain for 1 hr. KGA/GAC overexpression colony formation: 5000 cells were plated in BE2C control, KGA and GAC overexpressing cells and were cultured for 7 days; 10,000 cells were plated in SKNAS control, KGA and GAC overexpressing cells and were cultured for 7 days (n=3). After 7 days, medium was removed and cells were washed with DPBS (Lonza) and treated with 4% formaldehyde in PBS [PFA] for 30 min. PFA was later removed and cells were stained with 0.1% crystal violet stain for 1 hr. Experiments were repeated twice. Indisulam treatment on WT and JMJD6 knockout cell lines: JMJD6-WT and knockout cells were plated in 12-well plate (50,000 cells/well for SKNAS) and 6-well plate (5000 cells/well for BE2C) (n=3). Next day, cells were treated with indisulam with indicated concentration for 7 days (SKNAS cells) and 5 days (BE2C cells). Crystal violet staining was performed to visualize and quantify the colony formation. Experiments were repeated twice. Indisulam treatment on KGA/GAC overexpressing cell lines: 10,000 BE2C and 100,000 SKNAS control, KGA and GAC overexpressing cells were plated in six-well plate (n=3). Next day, cells were treated with indisulam for 7 days. Crystal violet staining was performed to visualize and quantify the colony formation. Experiments were repeated twice.

Click-iT was performed as previously described (Hu et al., 2018). Briefly, cells were plated at 5 million cells per 100 mm dish in RPMI supplemented with 10% FBS. Cells were washed with warm PBS and replaced with methionine-free medium (Thermo 21013024 supplement with glutamine and sodium pyruvate) for 1 hr at 37°C in 5% CO₂. Following, fresh methionine-free media containing 50 μM of Click-iT AHA (L-azidohomoalanine) (Thermo C10102) was added to the cells for 2 hr at 37°C. After AHA labeling, cells were washed with warm PBS and lysed with 1% SDS, 50 mM Tris-HCl, (pH 8.0) supplemented with phosphatase inhibitors (PhosSTOP, Sigma) and protease inhibitors (cOmplete Mini, Roche) by applying the buffer directly to the plate, incubating the cells on ice for 30 min, tilting the plates, and collecting the lysate. Lysates were briefly sonicated, vortexed for 5 min, and centrifuged at 18,000×g for 5 min at 4°C. Total protein quantification was assayed using the EZQ Protein Quantification Kit (Thermo R33200) according to the manufacturer’s protocol and results were read on a fluorescence-based microplate reader (BioTek Synergy 2). Click chemistry of the biotin-alkyne (PEG4 carboxamide-propargyl biotin) (Thermo B10185) to the AHA-labeled lysates was performed using the Click-iT Protein Reaction Buffer Kit (Thermo C10276) using a concentration of 40 μM biotin-alkyne per click reaction (and no biotin-alkyne added for controls). Following the click reaction, samples were either assayed for total biotinylated protein by following the manufacturer’s protocol. For total biotinylated protein, briefly, 600 μL of methanol, 150 μL of chloroform, and 400 μL of megaOhm water was sequentially added and vortexed, followed by centrifugation at 18,000×g for 5 min. The upper aqueous phase was discarded, and 450 μL of methanol was added, vortexed, and centrifuged again at 18,000×g for 5 min. This methanol step was performed in duplicate to remove residual reaction components. Protein pellets were allowed to air-dry and resuspended in a suitable volume of sample buffer and heated prior to western blot analysis.

5×10⁶ BE2C and SK-N-AS cells expressing FLAG-JMJD6 were cultured in 150 cm dish with complete RPMI media. Cells were washed twice with cold PBS after reaching 95% confluency, then lysed in 1 mL lysis buffer 50 mM Tris-HCl, pH 7.4, 150 mM NaCl, 1 mM EDTA, 1% Triton X-100 with complete protease inhibitors (Sigma 11836170001, added fresh) and PhosSTOP (Sigma 4906845001). Cells were scrapped into a 1.5 mL Eppendorf tube and incubated on ice for 15 min, which were mixed by vortex every 5 min. Cell lysates were spun by 13,500 RPM for 10 min at 4°C. The supernatant was transferred to a new tube. The cell lysates were subject to immunoprecipitation using M2 anti-FLAG beads (Sigma, M8823) overnight by rocking at 4°C. The following day, beads were washed 3× with buffer and eluted with 5 packed gel volumes of FLAG peptide in TBS buffer (3 µL of stock FLAG peptide at 5 µg/µL per 100 µL of TBS buffer) while rotating at 4°C for 30 min. Beads were briefly spun and the supernatant was removed from the beads (eluate). This elution step was repeated one more time and pooled with the first eluate. Prior to western blot, input, flowthroughs, and elution samples were processed by adding 4× sample buffer supplemented with 50 mM DTT and heated at 75°C for 10 min prior to running on a gel.

SK-N-AS cells expressing FLAG-JMJD6 were grown in a 10 cm dish in RPMI complete media. After 70% confluency, cells were washed with cold PBS twice and then were subject to lysis with Polysome Lysis Buffer (100 mM KCl, 5 mM MgCl₂, 10 mM HEPES, pH 7.0, 0.5% NP-40, 1 mM DTT, 100 U/mL RNasin RNase inhibitor [Promega, N2511], 2 mM vanadyl ribonucleoside complexes solution [Sigma, 94742], 25 μL protease inhibitor cocktail for mammalian cells [Sigma, P8340]). Cell lysates were precleared with magnetic IgG beads for 1 hr. The cell lysates were subject to immunoprecipitation using M2 anti-FLAG beads (Sigma, M8823) overnight by rocking at 4°C. The same amounts of lysates were saved at –80°C for input RNA extraction. The beads were washed with 250 μL Polysome Lysis Buffer for four times, flowed by washing with Polysome Lysis Buffer containing 1M urea. RNA was released by adding 150 μL of Polysome Lysis Buffer containing 0.1% SDS and 45 μg proteinase K (Ambion, AM2548) and incubated at 50°C for 30 min. RNA was extracted with phenol-chloroform-isoamyl alcohol mixture (Sigma, 77618). RNA was recovered by adding 2 μL of GlycoBlue (15 mg/mL, Ambion, AM9516), 36 μL of 3 M sodium acetate and 750 μL ethanol followed by incubation at –20°C for overnight. RNA was precipitated with 70% ethanol and air-dried, followed by resuspension with RNase-free water followed by DNaseI (Promega, M6101) treatment to remove genomic DNA. The resultant RNAs were subjected to RT-qPCR analysis using three sets of GAC and KGA primers and 18S rRNA as control. 18S rRNA F: GCTTAATTTGACTCAACACGGGA; 18S rRNA R: AGCTATCAATCTGTCAATCCTGTC. GLS-GACiso_F: GAGGTGCTGGCCAAAAAGCCT; GLS-GACiso_R: AGGCATTCGGTTGCCCAAACT. GLS-KGAiso_F: CTGCAGAGGGTCATGTTGAA; GLS-KGAiso_R: ATCCATGGGAGTGTTATTCCA. KGA_set2_F: GCAGCCTCCAGGTGCTTTCA; KGA_set2_R: GTAATGGGAGGGCAGTGGCA. KGA_set3_F: TGCCCGACACTGCCCTTTAG; KGA_set3_R: CCTGCCAGACAGACAACAGCA. GAC_set2_F: TGCTTCTCAAGGCCTTACTGC; GAC_set2_R: AGGCATTCGGTTGCCCAAACT. GAC_set3_F: CCTTCTAGAGGTGCTGGCCAAA; GAC_set3_R: TGCAACACAAATATGCAGTAAGGC. For validation of protein immunoprecipitation, 20% of beads after overnight incubation were removed and processed as follows: Beads were washed 3× with buffer and eluted with 5 packed gel volumes of FLAG peptide in TBS buffer (3 μL of stock FLAG peptide at 5 μg/μL per 100 µL of TBS buffer) while rotating at 4°C for 30 min. Beads were briefly spun and the supernatant was removed from the beads (eluate). This elution step was repeated one more time and pooled with the first eluate.

Protein samples were run on a short gel as described in a previously published protocol (Xu et al., 2009). Proteins in the gel bands were reduced with DTT (Sigma) and alkylated by iodoacetamide (Sigma). The gel bands were then washed, dried, and rehydrated with a buffer containing trypsin (Promega). Samples were digested overnight, acidified, and the resulting peptides were extracted. The extracts were dried and reconstituted in 5% formic acid. The peptide samples were loaded on a nanoscale capillary reverse phase C18 column by an HPLC system (Thermo EASY-nLC 1000) and eluted by a gradient. The eluted peptides were ionized and detected by a mass spectrometer (Thermo LTQ Orbitrap Elite). The MS and MS/MS spectra were collected over a 90 min liquid chromatography gradient. Database searches were performed using Sequest (v28, revision 13) search engine against a composite target/decoy Uniprot human protein database. All matched MS/MS spectra were filtered by mass accuracy and matching scores to reduce protein false discovery rate to <1%. Spectral counts matching to individual proteins reflect their relative abundance in one sample after the protein size is normalized. The spectral counts between samples for a given protein were used to calculate the p-value based on G-test (Bai et al., 2013).

JMJD6 knockout or parental SK-N-AS cells were cultured in six-well plates to ~85% confluence and washed with 2 mL ice-cold 1× PBS. The cells were then harvested in 300 µL freezing 80% acetonitrile (vol/vol) into 1.5 mL tubes and lysed in the presence of 0.5 mm zirconia/silica beads by Bullet Blender (Next Advance) at 4°C until the samples were homogenized. The resulting lysate was then centrifuged at 21,000×g for 5 min and the supernatant was dried by SpeedVac. The samples were resuspended in 50 µL of 1% acetonitrile plus 0.1% trifluoroacetic acid, and separated by Ultra-C18 Micro spin columns (Harvard apparatus) into hydrophilic metabolites (flowthrough) and hydrophobic metabolites (eluent of 125 µL of 80% acetonitrile plus 0.1% trifluoroacetic acid). Ten µL of hydrophilic metabolites were dried, reconstituted in 3 µL of 66% acetonitrile, and analyzed by a ZIC-HILIC column (150×2.1 mm², EMD Millipore) coupled with a Q Exactive HF Orbitrap MS (Thermo Fisher) in negative mode and metabolites were eluted within a 45 min gradient (buffer A: 10 mM ammonium acetate in 90% acetonitrile [pH = 8]; buffer B: 10 mM ammonium acetate in 100% H₂O [pH = 8]). Twenty µL of hydrophobic metabolites were dried and resuspended in 3 µL of 5% formic acid followed by separation with a self-packed nanoC18 column (75 μm×15 cm with 1.9 µm C18 resin from Dr Maisch GmbH) and detected with a Q Exactive HF Orbitrap MS (Thermo Fisher) in positive mode. Metabolites were eluted within a 50 min gradient (buffer A: 0.2% formic acid in H₂O; buffer B: 0.2% formic acid in acetonitrile). MS settings for both types of samples included MS1 scans (120,000 resolution, 100–1000 mass/charge [m/z], 3×10⁶ AGC, and 50 ms maximal ion time) and 20 data-dependent MS2 scans (30,000 resolution, 2×10⁵ AGC, ~45 ms maximal ion time, HCD, Stepped NCE [50, 100, 150], and 20 s dynamic exclusion). A mix of all samples served as quality control was injected in the beginning, middle, and the end of the samples to monitor the signal stability of the instrument. The data analysis was performed by a recently developed software suite JUMPm. Raw files were converted to mzXML format followed by peak feature detection for individual sample and feature alignment across samples. Metabolite identification was supported by matching the retention time, accurate m/z ratio, and MS/MS fragmentation data to our in-house authentic compound library and the matching of m/z and MS/MS fragmentation data, to downloaded experimental MS/MS library (MoNA, https://mona.fiehnlab.ucdavis.edu/), in silico database generated from Human Metabolome Database (HMDB), and mzCloud (https://mzcloud.org). Peak intensities were used for metabolite quantification. The data was normalized by both cell numbers (before data collection) and trimmed median intensity of all features across samples (post data collection).

Total RNA from cells and tumor tissues were performed using the RNeasy Mini Kit (QIAGEN) according to the manufacturer’s instructions. Paired-end sequencing was performed using the High-Seq platform with 100 bp read length. Total stranded RNA-seq data were processed by the internal AutoMapper pipeline. Briefly, the raw reads were firs trimmed (Trim-Galore v0.60), mapped to human genome assembly (GRCh38) (STAR v2.7), and then the gene-level values were quantified (RSEM v1.31) based on GENCODE annotation (v31). Low count genes were removed from analysis using a CPM cutoff corresponding to a count of 10 reads and only confidently annotated (level 1 and 2 gene annotation) and protein-coding genes are used for differential expression analysis. Normalization factors were generated using the TMM method, counts were then transformed using voom, and transformed counts were analyzed using the lmFit and eBayes functions (R limma package v3.42.2). The significantly up- and downregulated genes were defined by at least twofold changes and adjusted p-value < 0.05. Then GSEA was carried out using gene-level log2 fold changes from differential expression results against gene sets in the Molecular Signatures Database (MSigDB 6.2) (gsea2 v2.2.3).

After mapping RNA-seq data, rMATS v4.1.0 was used for RNA alternative splicing analysis by using the mapped BAM files as input. Specifically, five different kinds of alternative splicing events were identified, i.e., skipped exon, alternative 5’-splicing site, alternative 3’-splicing site, mutually exclusive exon, and intron retention. To keep consistent, the same GTF annotation reference file for mapping was used for rMATS. For stranded RNA-seq data, the argument ‘--libType fr-firststrand’ was applied. To process reads with variable lengths, the argument ‘--variable-read-length’ was also used for rMATS. To select statistically significantly differential splicing events, the following thresholds were used: FDR < 0.05 and the absolute value of IncLevelDifference > 0.1. For visualization, the IGV Genome Browser was used to show the sashimi plots of splicing events. To investigate the genome-wide correlations of differential splicing between two genotypes (e.g. shRNA knockdown of JMJD6 and non-target shRNA in cells), we extracted splice junctions for all samples of both genotypes of interest from the STAR (Dobin et al., 2013) output files suffixed with ‘SJ.out.tab’, which contain high confidence collapsed splice junctions. Only those unique mapped reads crossing the junctions were considered. By extracting the union of the unique junction positions, we constructed a unified junction-read feature vector for each sample. Then, we normalized the junction-read vectors of each sample with TMM method in ‘voom’ and ‘limma’ and R package, assuming a negative binomial distribution. Next, we averaged the junction-read vectors for samples of the same genotype. The gene-level expression was estimated based on the canonical junctions from the most abundant isoforms estimated for each gene. The fold changes of exon junctions significantly deviated from gene-level changes were regarded as differentially spliced junctions for between cell-line comparisons.

JMJD6, GAC, and KGA expression in tumor tissues were downloaded from R2 (https://portals.broadinstitute.org/ccle), Kocak dataset GSE45547 (649 samples), and Fisher dataset GSE120572 (394 samples). In both datasets, the probe UKv4_A_23_P311616 represented JMJD6, the probe UKv4_A_23_P308800 represented GAC, and UKv4_A_23_P39766 represented KGA. JMJD6 expression data from the RNA-seq data of various pediatric cancer tissues were downloaded from St Jude cloud (https://pecan.stjude.cloud/). The copy number alterations of JMJD6 and the related Kaplan-Meier analysis were downloaded from cBioportal (https://www.cbioportal.org/). The data for correlation of metabolite abundance and JMJD6 knockout effect were downloaded from DepMap (https://depmap.org/portal/).

The 114 essential fitness genes to neuroblastoma cell survival identified through genome-wide CRISPR/Cas9 library screen were uploaded into STRING program (https://string-db.org) for network interaction analysis with confidence threshold 0.15. The resulting network was then uploaded into Cytoscape program for presentation (Shannon et al., 2003). The clusters were grouped based on the biological functions of each gene.

Somatic copy number alternations were determined by CONSERTING (PMID: 25938371) for each pair of tumor and normal samples. The normalize read depth ratio (log2 ratio) for the CNV segments with JmjC domain containing proteins were extracted and used for CNV heatmap generation (https://CRAN.R-project.org/package=pheatmap; Kolde, 2019) and hierarchical clustering of samples.

All murine experiments were done in accordance with a protocol (JY: 615) approved by the Institutional Animal Care and Use Committee of St Jude Children’s Research Hospital. (1) shRNA-mediated JMJD6 knockdown. Neuroblastoma cells were transduced with shRNA lentiviral particles targeting JMJD6. 48 hr later, 1 μg/mL of puromycin was added for selection for additional 48 hr. Cancer cells (5×10⁶) were mixed with Matrigel (1:1 ratio in volume) and subcutaneously injected into the flank sites of NSG mice. (2) JMJD6 and MYC-mediated transformation. After JoMa1 cells were transduced with GFP, JMJD6, MYCN, and JMJD6/MYCN, 10⁴ cells per group were mixed with Matrigel (1:1 ratio in volume) and subcutaneously injected into the flank sites of 4–6 weeks of female NSG mice (JAX, RRID:IMSR_JAX:005557). Mice were sacrificed when they reached the humane endpoint. Tumors were measured by using electronic calipers, and volumes calculated as width π/6×d3, where d is the mean of two diameters taken at right angles.

All quantitative data are presented as mean ± SD. Unpaired Student’s t-test was performed for comparison of two groups. Spearman correlation was used to assess the relationship between two variables. Kaplan-Meier method was used to estimate the survival rate. Mann-Whitney rank test (two-sided) was used to compare the tumor volume between two groups at every time point. p-Values across multiple time points were adjusted for multiple comparison using the Holm-Sidak method. p<0.05 was considered as statistically significant. All the statistical analyses, except where otherwise noted, were performed using GraphPad Prism (v9).

The plasmids expressing JMJD6, MYCN, GAC, and KGA generated in this study will be freely distributed to non-profit in accordance with the guidelines about the sharing of unique research resources. Requests from for-profit corporations will be negotiated by the Office of Technology Management of St Jude Children’s Research Hospital.