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The expression of ELOVL4, repressed by MYCN, defines neuroblastoma patients with good outcome

Oncogene volume 40, pages 5741–5751 (2021)Cite this article

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A Correction to this article was published on 20 January 2022

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Abstract

Cancer cells exhibit dysregulation of critical genes including those involved in lipid biosynthesis, with subsequent defects in metabolism. Here, we show that ELOngation of Very Long chain fatty acids protein 4 (ELOVL4), a rate-limiting enzyme in the biosynthesis of very-long polyunsaturated fatty acids (n-3, ≥28 C), is expressed and transcriptionally repressed by the oncogene MYCN in neuroblastoma cells. In keeping, ELOVL4 positively regulates neuronal differentiation and lipids droplets accumulation in neuroblastoma cells. At the molecular level we found that MYCN binds to the promoter of ELOVL4 in close proximity to the histone deacetylases HDAC1, HDAC2, and the transcription factor Sp1 that can cooperate in the repression of ELOVL4 expression. Accordingly, in vitro differentiation results in an increase of fatty acid with 34 carbons with 6 double bonds (FA34:6); and when MYCN is silenced, FA34:6 metabolite is increased compared with the scrambled. In addition, analysis of large neuroblastoma datasets revealed that ELOVL4 expression is highly expressed in localized clinical stages 1 and 2, and low in high-risk stages 3 and 4. More importantly, high expression of ELOVL4 stratifies a subsets of neuroblastoma patients with good prognosis. Indeed, ELOVL4 expression is a marker of better overall clinical survival also in MYCN not amplified patients and in those with neuroblastoma-associated mutations. In summary, our findings indicate that MYCN, by repressing the expression of ELOVL4 and lipid metabolism, contributes to the progression of neuroblastoma.

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Fig. 1: ELOVL4 is required for neuronal differentiation of NB cells.
Fig. 2: ELOVL4 regulates lipid droplets number and VLV-PUFAs biosynthesis.
Fig. 3: ELOVL4 expression is regulated by MYCN.
Fig. 4: MYCN, HDAC1, HDAC2, and SP1 bind ELOVL4 promoter and repress its expression.
Fig. 5: High levels of ELVOL4 expression are associated with better survival of NB patients.

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References

  1. Snaebjornsson MT, Janaki-Raman S, Schulze A. Greasing the wheels of the cancer machine: the role of lipid metabolism in cancer. Cell Metab. 2020;31:62–76.

    Article  CAS  PubMed  Google Scholar 

  2. Buck MD, O’Sullivan D, Klein Geltink RI, Curtis JD, Chang CH, Sanin DE, et al. Mitochondrial dynamics controls T cell fate through metabolic programming. Cell. 2016;166:63–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Sulciner ML, Gartung A, Gilligan MM, Serhan CN, Panigrahy D. Targeting lipid mediators in cancer biology. Cancer Metastasis Rev. 2018;37:557–72.

    Article  CAS  PubMed  Google Scholar 

  4. DeBose-Boyd RA, Ye J. SREBPs in lipid metabolism, insulin signaling, and beyond. Trends Biochem Sci. 2018;43:358–68.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Rohrig F, Schulze A. The multifaceted roles of fatty acid synthesis in cancer. Nat Rev Cancer. 2016;16:732–49.

    Article  PubMed  Google Scholar 

  6. Snaebjornsson MT, Schulze A. Tumours use a metabolic twist to make lipids. Nature. 2019;566:333–4.

    Article  CAS  PubMed  Google Scholar 

  7. Riscal R, Skuli N, Simon MC. Even cancer cells watch their cholesterol! Mol Cell. 2019;76:220–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Dickerman BA, Garcia-Albeniz X, Logan RW, Denaxas S, Hernan MA. Avoidable flaws in observational analyses: an application to statins and cancer. Nat Med. 2019;25:1601–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. McGregor GH, Campbell AD, Fey SK, Tumanov S, Sumpton D, Blanco GR, et al. Targeting the metabolic response to statin-mediated oxidative stress produces a synergistic antitumor response. Cancer Res. 2020;80:175–88.

    Article  CAS  PubMed  Google Scholar 

  10. Yao L, Han C, Song K, Zhang J, Lim K, Wu T. Omega-3 polyunsaturated fatty acids upregulate 15-PGDH expression in cholangiocarcinoma cells by inhibiting miR-26a/b expression. Cancer Res. 2015;75:1388–98.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Bhattacharjee S, Jun B, Belayev L, Heap J, Kautzmann MA, Obenaus A, et al. Elovanoids are a novel class of homeostatic lipid mediators that protect neural cell integrity upon injury. Sci Adv. 2017;3:e1700735.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Jun B, Mukherjee PK, Asatryan A, Kautzmann MA, Heap J, Gordon WC, et al. Elovanoids are novel cell-specific lipid mediators necessary for neuroprotective signaling for photoreceptor cell integrity. Sci Rep. 2017;7:5279.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Agbaga MP, Brush RS, Mandal MN, Henry K, Elliott MH, Anderson RE. Role of Stargardt-3 macular dystrophy protein (ELOVL4) in the biosynthesis of very long chain fatty acids. Proc Natl Acad Sci USA. 2008;105:12843–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhang K, Kniazeva M, Han M, Li W, Yu Z, Yang Z, et al. A 5-bp deletion in ELOVL4 is associated with two related forms of autosomal dominant macular dystrophy. Nat Genet. 2001;27:89–93.

    Article  CAS  PubMed  Google Scholar 

  15. Bazan NG. Docosanoids and elovanoids from omega-3 fatty acids are pro-homeostatic modulators of inflammatory responses, cell damage and neuroprotection. Mol Asp Med. 2018;64:18–33.

    Article  CAS  Google Scholar 

  16. Horton JD, Shah NA, Warrington JA, Anderson NN, Park SW, Brown MS, et al. Combined analysis of oligonucleotide microarray data from transgenic and knockout mice identifies direct SREBP target genes. Proc Natl Acad Sci USA. 2003;100:12027–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Nohturfft A, Zhang SC. Coordination of lipid metabolism in membrane biogenesis. Annu Rev Cell Dev Biol. 2009;25:539–66.

    Article  CAS  PubMed  Google Scholar 

  18. Mizutani Y, Sun H, Ohno Y, Sassa T, Wakashima T, Obara M, et al. Cooperative synthesis of ultra long-chain fatty acid and ceramide during keratinocyte differentiation. PLoS One. 2013;8:e67317.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Thiam AR, Beller M. The why, when and how of lipid droplet diversity. J Cell Sci. 2017;130:315–24.

    CAS  PubMed  Google Scholar 

  20. Teo W, Caprariello AV, Morgan ML, Luchicchi A, Schenk GJ, Joseph JT, et al. Nile Red fluorescence spectroscopy reports early physicochemical changes in myelin with high sensitivity. Proc Natl Acad Sci USA. 2021;118:e2016897118.

  21. Agostini M, Romeo F, Inoue S, Niklison-Chirou MV, Elia AJ, Dinsdale D, et al. Metabolic reprogramming during neuronal differentiation. Cell Death Differ. 2016;23:1502–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Petroni A, Blasevich M, La Spada P, Papini N, Galli C. Arachidonic acid synthesis and lipid metabolism in retinoic acid-differentiated neuroblastoma cells. J Lipid Mediat Cell Signal. 1996;14:39–44.

    Article  CAS  PubMed  Google Scholar 

  23. Maris JM, Hogarty MD, Bagatell R, Cohn SL. Neuroblastoma. Lancet. 2007;369:2106–20.

    Article  CAS  PubMed  Google Scholar 

  24. Piacentini M, Annicchiarico-Petruzzelli M, Oliverio S, Piredda L, Biedler JL, Melino E. Phenotype-specific “tissue” transglutaminase regulation in human neuroblastoma cells in response to retinoic acid: correlation with cell death by apoptosis. Int J Cancer. 1992;52:271–8.

    Article  CAS  PubMed  Google Scholar 

  25. Ross RA, Spengler BA, Biedler JL. Coordinate morphological and biochemical interconversion of human neuroblastoma cells. J Natl Cancer Inst. 1983;71:741–7.

    CAS  PubMed  Google Scholar 

  26. Lutz W, Stohr M, Schurmann J, Wenzel A, Lohr A, Schwab M. Conditional expression of N-myc in human neuroblastoma cells increases expression of alpha-prothymosin and ornithine decarboxylase and accelerates progression into S-phase early after mitogenic stimulation of quiescent cells. Oncogene. 1996;13:803–12.

    CAS  PubMed  Google Scholar 

  27. Iraci N, Diolaiti D, Papa A, Porro A, Valli E, Gherardi S, et al. A SP1/MIZ1/MYCN repression complex recruits HDAC1 at the TRKA and p75NTR promoters and affects neuroblastoma malignancy by inhibiting the cell response to NGF. Cancer Res. 2011;71:404–12.

    Article  CAS  PubMed  Google Scholar 

  28. Lin YC, Lin JH, Chou CW, Chang YF, Yeh SH, Chen CC. Statins increase p21 through inhibition of histone deacetylase activity and release of promoter-associated HDAC1/2. Cancer Res. 2008;68:2375–83.

    Article  CAS  PubMed  Google Scholar 

  29. Depuydt P, Koster J, Boeva V, Hocking TD, Speleman F, Schleiermacher G, et al. Meta-mining of copy number profiles of high-risk neuroblastoma tumors. Sci Data. 2018;5:180240.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Matthay KK, Maris JM, Schleiermacher G, Nakagawara A, Mackall CL, Diller L, et al. Neuroblastoma. Nat Rev Dis Prim. 2016;2:16078.

    Article  PubMed  Google Scholar 

  31. Thiele CJ. Biology of pediatric peripheral neuroectodermal tumors. Cancer Metastasis Rev. 1991;10:311–9.

    Article  CAS  PubMed  Google Scholar 

  32. Pieraccioli M, Nicolai S, Pitolli C, Agostini M, Antonov A, Malewicz M, et al. ZNF281 inhibits neuronal differentiation and is a prognostic marker for neuroblastoma. Proc Natl Acad Sci USA. 2018;115:7356–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Zirath H, Frenzel A, Oliynyk G, Segerstrom L, Westermark UK, Larsson K, et al. MYC inhibition induces metabolic changes leading to accumulation of lipid droplets in tumor cells. Proc Natl Acad Sci USA. 2013;110:10258–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Ruiz-Perez MV, Sainero-Alcolado L, Oliynyk G, Matuschek I, Balboni N, Ubhayasekera S, et al. Inhibition of fatty acid synthesis induces differentiation and reduces tumor burden in childhood neuroblastoma. iScience. 2021;24:102128.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Amelio I, Bertolo R, Bove P, Candi E, Chiocchi M, Cipriani C, et al. Cancer predictive studies. Biol Direct. 2020;15:18.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Han Y, Ye X, Wang C, Liu Y, Zhang S, Feng W, et al. Integration of molecular features with clinical information for predicting outcomes for neuroblastoma patients. Biol Direct. 2019;14:16.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Han YT, Ye XF, Cheng J, Zhang SY, Feng WX, Han Z, et al. Integrative analysis based on survival associated co-expression gene modules for predicting Neuroblastoma patients’ survival time. Biol Direct. 2019;14:4. https://doi.org/10.1186/s13062-018-0229-2.

  38. Sumsion GR, Bradshaw MS, Beales JT, Ford E, Caryotakis GRG, Garrett DJ, et al. Diverse approaches to predicting drug-induced liver injury using gene-expression profiles. Biol Direct. 2020;15:1. https://doi.org/10.1186/s13062-019-0257-6.

  39. Mihaylov I, Kandula M, Krachunov M, Vassilev D. A novel framework for horizontal and vertical data integration in cancer studies with application to survival time prediction models. Biol Direct. 2019;14:22. https://doi.org/10.1186/s13062-019-0249-6.

  40. Carroll PA, Diolaiti D, McFerrin L, Gu H, Djukovic D, Du J, et al. Deregulated Myc requires MondoA/Mlx for metabolic reprogramming and tumorigenesis. Cancer Cell. 2015;27:271–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Amelio I, Bertolo R, Bove P, Buonomo OC, Candi E, Chiocchi M, et al. Liquid biopsies and cancer omics. Cell Death Discov. 2020;6:131.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Harris ZN, Dhungel E, Mosior M, Ahn TH. Massive metagenomic data analysis using abundance-based machine learning. Biol Direct. 2019;14:12.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Liu L, Wang G, Wang L, Yu C, Li M, Song S, et al. Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling. Biol Direct. 2020;15:10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Chen JC, Tyler AD. Systematic evaluation of supervised machine learning for sample origin prediction using metagenomic sequencing data. Biol Direct. 2020;15:29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Chierici M, Francescatto M, Bussola N, Jurman G, Furlanello C. Predictability of drug-induced liver injury by machine learning. Biol Direct. 2020;15:3.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Bellomaria A, Barbato G, Melino G, Paci M, Melino S. Recognition mechanism of p63 by the E3 ligase Itch Novel strategy in the study and inhibition of this interaction. Cell Cycle. 2012;11:3638–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ciocci M, Iorio E, Carotenuto F, Khashoggi HA, Nanni F, Melino S. H2S-releasing nanoemulsions: a new formulation to inhibit tumor cells proliferation and improve tissue repair. Oncotarget. 2016;7:84338–58.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Nicolai S, Pieraccioli M, Smirnov A, Pitolli C, Anemona L, Mauriello A, et al. ZNF281/Zfp281 is a target of miR-1 and counteracts muscle differentiation. Mol Oncol. 2020;14:294–308.

    Article  CAS  PubMed  Google Scholar 

  49. Lamastra FR, De Angelis R, Antonucci A, Salvatori D, Prosposito P, Casalboni M, et al. Polymer composite random lasers based on diatom frustules as scatterers. Rsc Adv. 2014;4:61809–16.

    Article  CAS  Google Scholar 

  50. Pallucca R, Visconti S, Camoni L, Cesareni G, Melino S, Panni S, et al. Specificity of epsilon and Non-epsilon Isoforms of Arabidopsis 14-3-3 Proteins Towards the H+-ATPase and Other Targets. Plos ONE. 2014;9:e90764. https://doi.org/10.1371/journal.pone.0090764.

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Acknowledgements

We would like to thank Dott. Butera Alessio for technical assistance and Professor Manfred Schwab for SHEP Tet21N cell line.

Funding

This research was funded by Associazione Italiana per la Ricerca contro il Cancro (AIRC) to GM (IG#20473; 2018–2022), Ministry of Health and MAECI Italy-China Science and Technology Cooperation (#PGR00961) to GM. Work has been also supported by Regione Lazio through LazioInnova Progetto Gruppo di Ricerca n 85-2017-14986.

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Authors and Affiliations

  1. Department of Experimental Medicine, TOR, University of Rome Tor Vergata, Rome, Italy

    Francesco Rugolo, Gerry Melino & Massimiliano Agostini

  2. Neuroscience Center of Excellence, School of Medicine, Louisiana State University Health New Orleans, New Orleans, LA, USA

    Nicolas G. Bazan, Jorgelina Calandria & Bokkyoo Jun

  3. Laboratory of Health and Environment, Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), Rome, Italy

    Giuseppe Raschellà

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Contributions

FR, MA, NGB, GR, and GM designed the experiments. MA, GR, and GM supervised the project. FR and JC performed the biochemical experiments. BJ and JC performed LC-MS/MS. FR performed the bioinformatic analysis. FR, MA, GR, and GM analyzed the data. MA, GR, and GM wrote the manuscript and all the authors commented and edited the manuscript.

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Correspondence to Gerry Melino or Massimiliano Agostini.

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Rugolo, F., Bazan, N.G., Calandria, J. et al. The expression of ELOVL4, repressed by MYCN, defines neuroblastoma patients with good outcome. Oncogene 40, 5741–5751 (2021). https://doi.org/10.1038/s41388-021-01959-3

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