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Discovery of a ZIP7 inhibitor from a Notch pathway screen

Nature Chemical Biology volume 15, pages 179–188 (2019)Cite this article

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Abstract

The identification of activating mutations in NOTCH1 in 50% of T cell acute lymphoblastic leukemia has generated interest in elucidating how these mutations contribute to oncogenic transformation and in targeting the pathway. A phenotypic screen identified compounds that interfere with trafficking of Notch and induce apoptosis via an endoplasmic reticulum (ER) stress mechanism. Target identification approaches revealed a role for SLC39A7 (ZIP7), a zinc transport family member, in governing Notch trafficking and signaling. Generation and sequencing of a compound-resistant cell line identified a V430E mutation in ZIP7 that confers transferable resistance to the compound NVS-ZP7-4. NVS-ZP7-4 altered zinc in the ER, and an analog of the compound photoaffinity labeled ZIP7 in cells, suggesting a direct interaction between the compound and ZIP7. NVS-ZP7-4 is the first reported chemical tool to probe the impact of modulating ER zinc levels and investigate ZIP7 as a novel druggable node in the Notch pathway.

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Fig. 1: Identification and characterization of molecules that inhibit Notch signaling.
Fig. 2: NVS-ZP7-3 treatment induces apoptosis and ER stress in Notch pathway-active T-ALL cell lines.
Fig. 3: Generation and characterization of a NVS-ZP7-4 compound-resistant cell line.
Fig. 4: Genetic screens reveal increased ER stress and decreased Notch signaling following ZIP7 siRNA knockdown.
Fig. 5: Genetic validation of ZIP7 as the target of NVS-ZP7-4.
Fig. 6: NVS-ZP7-4 interacts with ZIP7, increases ER Zn2+ levels in the ER and modulates Notch signaling.

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Data availability

The microarray data has been deposited in GEO (GSE115690). Other datasets that were generated during the current study are provided as Supplementary Information or are available from the corresponding author upon reasonable request.

References

  1. Imming, P., Sinning, C. & Meyer, A. Drugs, their targets and the nature and number of drug targets. Nat. Rev. Drug Discov. 5, 821–834 (2006).

    Article  CAS  Google Scholar 

  2. Overington, J. P., Al-Lazikani, B. & Hopkins, A. L. How many drug targets are there? Nat. Rev. Drug Discov. 5, 993–996 (2006).

    Article  CAS  Google Scholar 

  3. Whitfield, J. R., Beaulieu, M. E. & Soucek, L. Strategies to inhibit Myc and their clinical applicability. Front. Cell Dev. Biol. 5, 10 (2017).

    Article  Google Scholar 

  4. Hori, K., Sen, A. & Artavanis-Tsakonas, S. Notch signaling at a glance. J. Cell Sci. 126, 2135–2140 (2013).

    Article  CAS  Google Scholar 

  5. Weng, A. P. et al. Activating mutations of NOTCH1 in human T cell acute lymphoblastic leukemia. Science 306, 269–271 (2004).

    Article  CAS  Google Scholar 

  6. Espinoza, I., Pochampally, R., Xing, F., Watabe, K. & Miele, L. Notch signaling: targeting cancer stem cells and epithelial-to-mesenchymal transition. Onco Targets Ther. 6, 1249–1259 (2013).

    PubMed  PubMed Central  Google Scholar 

  7. Aster, J. C., Pear, W. S. & Blacklow, S. C. The varied roles of Notch in cancer. Annu. Rev. Pathol. 12, 245–275 (2017).

    Article  CAS  Google Scholar 

  8. Kopan, R. & Ilagan, M. X. The canonical Notch signaling pathway: unfolding the activation mechanism. Cell 137, 216–233 (2009).

    Article  CAS  Google Scholar 

  9. Guruharsha, K. G., Kankel, M. W. & Artavanis-Tsakonas, S. The Notch signalling system: recent insights into the complexity of a conserved pathway. Nat. Rev. Genet. 13, 654–666 (2012).

    Article  CAS  Google Scholar 

  10. Andersson, E. R., Sandberg, R. & Lendahl, U. Notch signaling: simplicity in design, versatility in function. Development 138, 3593–3612 (2011).

    Article  CAS  Google Scholar 

  11. Shih, Ie. M. & Wang, T. L. Notch signaling, gamma-secretase inhibitors, and cancer therapy. Cancer Res. 67, 1879–1882 (2007).

    Article  CAS  Google Scholar 

  12. Aster, J. C. & Blacklow, S. C. Targeting the Notch pathway: twists and turns on the road to rational therapeutics. J. Clin. Oncol. 30, 2418–2420 (2012).

    Article  CAS  Google Scholar 

  13. Imbimbo, B. P. Therapeutic potential of gamma-secretase inhibitors and modulators. Curr. Top. Med. Chem. 8, 54–61 (2008).

    Article  CAS  Google Scholar 

  14. Ran, Y. et al. γ-Secretase inhibitors in cancer clinical trials are pharmacologically and functionally distinct. EMBO Mol. Med. 9, 950–966 (2017).

    Article  CAS  Google Scholar 

  15. Wagner, B. K. & Schreiber, S. L. The power of sophisticated phenotypic screening and modern mechanism-of-action methods. Cell Chem. Biol. 23, 3–9 (2016).

    Article  CAS  Google Scholar 

  16. Moffat, J. G., Vincent, F., Lee, J. A., Eder, J. & Prunotto, M. Opportunities and challenges in phenotypic drug discovery: an industry perspective. Nat. Rev. Drug Discov. 16, 531–543 (2017).

    Article  CAS  Google Scholar 

  17. Schenone, M., Dančík, V., Wagner, B. K. & Clemons, P. A. Target identification and mechanism of action in chemical biology and drug discovery. Nat. Chem. Biol. 9, 232–240 (2013).

    Article  CAS  Google Scholar 

  18. Schirle, M. & Jenkins, J. L. Identifying compound efficacy targets in phenotypic drug discovery. Drug Discov. Today 21, 82–89 (2016).

    Article  CAS  Google Scholar 

  19. Palacino, J. et al. SMN2 splice modulators enhance U1-pre-mRNA association and rescue SMA mice. Nat. Chem. Biol. 11, 511–517 (2015).

    Article  CAS  Google Scholar 

  20. Huang, S. M. et al. Tankyrase inhibition stabilizes axin and antagonizes Wnt signalling. Nature 461, 614–620 (2009).

    Article  CAS  Google Scholar 

  21. Garbaccio, R. M. & Parmee, E. R. The impact of chemical probes in drug discovery: a pharmaceutical industry perspective. Cell Chem. Biol. 23, 10–17 (2016).

    Article  CAS  Google Scholar 

  22. Huang, L., Kirschke, C. P., Zhang, Y. & Yu, Y. Y. The ZIP7 gene (Slc39a7) encodes a zinc transporter involved in zinc homeostasis of the Golgi apparatus. J. Biol. Chem. 280, 15456–15463 (2005).

    Article  CAS  Google Scholar 

  23. Taylor, K. M., Morgan, H. E., Johnson, A. & Nicholson, R. I. Structure-function analysis of HKE4, a member of the new LIV-1 subfamily of zinc transporters. Biochem. J. 377, 131–139 (2004).

    Article  CAS  Google Scholar 

  24. Hojyo, S. & Fukada, T. Zinc transporters and signaling in physiology and pathogenesis. Arch. Biochem. Biophys. 611, 43–50 (2016).

    Article  CAS  Google Scholar 

  25. Jeong, J. & Eide, D. J. The SLC39 family of zinc transporters. Mol. Aspects Med. 34, 612–619 (2013).

    Article  CAS  Google Scholar 

  26. Groth, C., Sasamura, T., Khanna, M. R., Whitley, M. & Fortini, M. E. Protein trafficking abnormalities in Drosophila tissues with impaired activity of the ZIP7 zinc transporter Catsup. Development 140, 3018–3027 (2013).

    Article  CAS  Google Scholar 

  27. Lindsell, C. E., Shawber, C. J., Boulter, J. & Weinmaster, G. Jagged: a mammalian ligand that activates Notch1. Cell 80, 909–917 (1995).

    Article  CAS  Google Scholar 

  28. Wu, Y. et al. Therapeutic antibody targeting of individual Notch receptors. Nature 464, 1052–1057 (2010).

    Article  CAS  Google Scholar 

  29. Bernasconi-Elias, P. et al. Characterization of activating mutations of NOTCH3 in T-cell acute lymphoblastic leukemia and anti-leukemic activity of NOTCH3 inhibitory antibodies. Oncogene 35, 6077–6086 (2016).

    Article  CAS  Google Scholar 

  30. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    Article  CAS  Google Scholar 

  31. Hoepfner, D. et al. High-resolution chemical dissection of a model eukaryote reveals targets, pathways and gene functions. Microbiol. Res. 169, 107–120 (2014).

    Article  CAS  Google Scholar 

  32. Huang, Z. et al. A functional variomics tool for discovering drug-resistance genes and drug targets. Cell Rep. 3, 577–585 (2013).

    Article  CAS  Google Scholar 

  33. Bill, A. et al. Variomics screen identifies the re-entrant loop of the calcium-activated chloride channel ANO1 that facilitates channel activation. J. Biol. Chem. 290, 889–903 (2015).

    Article  CAS  Google Scholar 

  34. Zhang, T. et al. Crystal structures of a ZIP zinc transporter reveal a binuclear metal center in the transport pathway. Sci. Adv. 3, e1700344 (2017).

    Article  Google Scholar 

  35. Qin, Y., Dittmer, P. J., Park, J. G., Jansen, K. B. & Palmer, A. E. Measuring steady-state and dynamic endoplasmic reticulum and Golgi Zn2+with genetically encoded sensors. Proc. Natl Acad. Sci. USA 108, 7351–7356 (2011).

    Article  CAS  Google Scholar 

  36. Park, J. G. & Palmer, A. E. Quantitative measurement of Ca2+ and Zn2+ in mammalian cells using genetically encoded fluorescent biosensors. Methods Mol. Biol. 1071, 29–47 (2014).

    Article  CAS  Google Scholar 

  37. Carter, K. P., Carpenter, M. C., Fiedler, B., Jimenez, R. & Palmer, A. E. Critical comparison of FRET-sensor functionality in the cytosol and endoplasmic reticulum and implications for quantification of ions. Anal. Chem. 89, 9601–9608 (2017).

    Article  CAS  Google Scholar 

  38. Fiedler, B. L. et al. Droplet microfluidic flow cytometer for sorting on transient cellular responses of genetically-encoded sensors. Anal. Chem. 89, 711–719 (2017).

    Article  CAS  Google Scholar 

  39. Fortini, M. E. Notch signaling: the core pathway and its posttranslational regulation. Dev. Cell 16, 633–647 (2009).

    Article  CAS  Google Scholar 

  40. Takeuchi, H. & Haltiwanger, R. S. Significance of glycosylation in Notch signaling. Biochem. Biophys. Res. Commun. 453, 235–242 (2014).

    Article  CAS  Google Scholar 

  41. Walter, P. & Ron, D. The unfolded protein response: from stress pathway to homeostatic regulation. Science 334, 1081–1086 (2011).

    Article  CAS  Google Scholar 

  42. Roti, G. et al. Complementary genomic screens identify SERCA as a therapeutic target in NOTCH1 mutated cancer. Cancer Cell 23, 390–405 (2013).

    Article  CAS  Google Scholar 

  43. Kapoor, T. M. & Miller, R. M. Leveraging chemotype-specific resistance for drug target identification and chemical biology. Trends Pharmacol. Sci. 38, 1100–1109 (2017).

    Article  CAS  Google Scholar 

  44. Thomas, J. R. et al. A photoaffinity labeling-based chemoproteomics strategy for unbiased target deconvolution of small molecule drug candidates. Methods Mol. Biol. 1647, 1–18 (2017).

    Article  Google Scholar 

  45. Hara, T. et al. Physiological roles of zinc transporters: molecular and genetic importance in zinc homeostasis. J. Physiol. Sci. 67, 283–301 (2017).

    Article  CAS  Google Scholar 

  46. Bin, B. H. et al. Requirement of zinc transporter SLC39A7/ZIP7 for dermal development to fine-tune endoplasmic reticulum function by regulating protein disulfide isomerase. J. Invest. Dermatol. 137, 1682–1691 (2017).

    Article  CAS  Google Scholar 

  47. Ohashi, W. et al. Zinc transporter SLC39A7/ZIP7 promotes intestinal epithelial self-renewal by resolving ER stress. PLoS Genet. 12, e1006349 (2016).

    Article  Google Scholar 

  48. Woodruff, G. et al. The zinc transporter SLC39A7 (ZIP7) is essential for regulation of cytosolic zinc levels. Mol. Pharmacol. 94, 1092–1100 (2018).

    Article  CAS  Google Scholar 

  49. Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25, 402–408 (2001).

    Article  CAS  Google Scholar 

  50. König, R. et al. A probability-based approach for the analysis of large-scale RNAi screens. Nat. Methods 4, 847–849 (2007).

    Article  Google Scholar 

  51. Sigoillot, F. D. et al. A bioinformatics method identifies prominent off-targeted transcripts in RNAi screens. Nat. Methods 9, 363–366 (2012).

    Article  CAS  Google Scholar 

  52. Yilmazel, B. et al. Online GESS: prediction of miRNA-like off-target effects in large-scale RNAi screen data by seed region analysis. BMC Bioinformatics 15, 192 (2014).

    Article  Google Scholar 

  53. Kauffmann, A., Gentleman, R. & Huber, W. arrayQualityMetrics–a Bioconductor package for quality assessment of microarray data. Bioinformatics 25, 415–416 (2009).

    Article  CAS  Google Scholar 

  54. Wu, Z., Irizarry, R. A., Gentleman, R., Martinez-Murillo, F. & Spencer, F. A model-based background adjustment for oligonucleotide expression arrays. J. Am. Stat. Assoc. 99, 909–917 (2004).

    Article  Google Scholar 

  55. Smyth, G. in Bioinformatics and Computational Biology Solutions Bioinformatics and Computational Biology Solutions Using R and Bioconductor (eds. Gentleman, R. et al.) Ch. 23 (Springer, 2005).

  56. Tripathi, S. et al. Meta- and orthogonal integration of influenza “OMICs” data defines a role for UBR4 in virus budding. Cell Host Microbe 18, 723–735 (2015).

    Article  CAS  Google Scholar 

  57. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  Google Scholar 

  58. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  Google Scholar 

  59. Wacker, S. A., Houghtaling, B. R., Elemento, O. & Kapoor, T. M. Using transcriptome sequencing to identify mechanisms of drug action and resistance. Nat. Chem. Biol. 8, 235–237 (2012).

    Article  CAS  Google Scholar 

  60. McLaren, W. et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics 26, 2069–2070 (2010).

    Article  CAS  Google Scholar 

  61. Abagyan, R. et al. ICM-A new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J. Comput. Chem. 15, 488–506 (1994).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The JAGGED1 and DLL1 expressing cell lines were kindly provide by G. Weinmaster (UCLA). HPB-ALL cells were kindly provided by A. Stasser (Walter and Eliza Hall Institute for Medical Research). The authors thank J. Paulk for his insights on the manuscript and A. Abrams for his artwork in schematic diagrams. This work was financially supported in part by NIH Director’s Pioneer Award GM114863 (to A.E.P.).

Author information

Author notes

  1. These authors contributed equally: Erin Nolin, Sara Gans.

  2. These authors jointly supervised this work: Simon M. Bushell, Christy J. Fryer.

Authors and Affiliations

  1. Novartis Institutes for Biomedical Research, Cambridge, MA, USA

    Erin Nolin, Sara Gans, Luis Llamas, Somnath Bandyopadhyay, Scott M. Brittain, Paula Bernasconi-Elias, Joseph J. Loureiro, Jason R. Thomas, Markus Schirle, Yi Yang, Ning Guo, Alicia Lindeman, Frederic Sigoillot, Amy Chen, Kevin X. Xie, Samuel Ho, John Reece-Hoyes, Wilhelm A. Weihofen, Kayla Tyskiewicz, Richard I. McDonald, Nicolette Guthrie, Abhishek Dogra, Jian Shao, Jian Ding, Stephen M. Canham, Geoff Boynton, Elizabeth L. George, Zhao B. Kang, Christophe Antczak, Jeffery A. Porter, Owen Wallace, John A. Tallarico, Jeremy L. Jenkins, Rishi K. Jain, Simon M. Bushell & Christy J. Fryer

  2. Department of Chemistry and Biochemistry and BioFrontiers Institute, University of Colorado, Boulder, CO, USA

    Kyle P. Carter & Amy E. Palmer

  3. Novartis Institutes for Biomedical Research, Basel, Switzerland

    Guglielmo Roma, Sven Schuierer, Martin Beibel & Dominic Hoepfner

  4. Novartis Institutes for Biomedical Research, Shanghai, China

    Haibing Guo

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Contributions

E.N., S.G., P.B.-E. and C.J.F. developed and/or performed cell-based assays. L.L, R.I.M., N.G., A.D., H.G., J.S., J.D., S.M.C., R.K.J. and S.M. Bushell synthesized compounds and/or directed the medicinal chemistry strategy. S.B., J.J.L., G.R., S.S. and M.B. analyzed/interpreted genomic data. S.H. performed high-throughput screens. J.L.J. and R.K.J. triaged compounds from screens for further characterization. E.N., K.P.C. and A.E.P. performed and interpreted results from zinc FRET sensor experiments. K.P.C. and A.E.P. provided input on zinc and zinc transporter biology. Z.B.K. and C.A. developed assays for zinc quantitation. K.X.X., A.C. and F.S. performed or analyzed the siRNA screen. S.M. Brittain, J.R.T. and M.S. performed or directed the photoaffinity labeling experiments. A.L., N.G. and Y.Y. designed targeting strategy for CRISPR experiments or analyzed data. J.R.-H., W.A.W., K.T., P.B.-E. and E.N. performed the variomics experiments and characterized mutants. J.A.P., O.W., D.H., E.L.G., G.B., R.K.J. and J.A.T. provided intellectual input to the mechanism of action studies. All authors contributed to writing of the work. C.J.F. and S.M. Bushell designed the experimental strategy, wrote the manuscript and held overall responsibility for the study.

Corresponding authors

Correspondence to Simon M. Bushell or Christy J. Fryer.

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Competing interests

E.N., S.G., L.L., S.B., S.M. Brittain, P.B.-E., J.J.L., J.R.T., M.S., Y.Y., N.G., G.R., S.S., M.B., A.L., F.S., A.C., K.X.X., S.H., J.R.-H., W.A.W., K.T., D.H., R.I.M., N.G., A.D., H.G., J.S., J.D., S.M.C., G.B., E.L.G., Z.B.K., C.A., J.A.P., O.W., J.A.T., J.L.J., R.K.J., S.M. Bushell, and C.J.F. are (or were at the time the research was conducted) employees of Novartis.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–16 and Supplementary Tables 1–6

Reporting Summary

Supplementary Note 1

Chemical Synthesis

Supplementary Note 2

Chemistry Spectra

Supplementary Dataset 1

133 gene probe sets identified from microarray that are significantly changed in both TALL-1 and RPMI-8402 cells following NVS-ZP7-3 treatment (adjusted P < 0.001 and a fold change greater than two)

Supplementary Dataset 2

33 genes with single nucleotide polymorphisms (SNPs)

Supplementary Dataset 3

Chemoproteomics data

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Nolin, E., Gans, S., Llamas, L. et al. Discovery of a ZIP7 inhibitor from a Notch pathway screen. Nat Chem Biol 15, 179–188 (2019). https://doi.org/10.1038/s41589-018-0200-7

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