Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

A druggable redox switch on SHP1 controls macrophage inflammation

Nature Chemical Biology (2026)Cite this article

Subjects

Abstract

Immunological proteins are major disease targets, yet most remain undrugged. Post-translational redox modification of cysteine residues has emerged as an important mode of immune cell regulation, particularly in macrophage cytokine responses. Here we develop a strategy for systematic discovery and small-molecule functionalization of redox-regulated cysteines on immunological proteins. Using deep redox proteomics, we annotate 788 in vivo redox-regulated cysteines across diverse immune-relevant protein domains. We demonstrate how these sites enable cysteine-directed pharmacology through discovery of a novel cysteine activation site on the immune regulator SHP1. Targeting C102, we develop a highly selective covalent agonist, SCA, which binds the N-SH2 domain to relieve autoinhibition and activate SHP1. In mouse and human macrophages, SCA selectively engages SHP1 C102, antagonizing interleukin-1 receptor-associated kinase signaling and lipopolysaccharide-induced proinflammatory cytokine production. Together, this work identifies a druggable cysteine redox switch controlling macrophage cytokine responses and provides a compendium of redox-regulated sites for therapeutic development.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Systematic discovery of redox-regulated cysteines on immunological proteins.
Fig. 2: Deep chemoproteomics functionalizes the redox-sensitive C102 on SHP1.
Fig. 3: SCA1 selectively engages C102 on SHP1.
Fig. 4: Structural consequence for SHP1 C102 modification by SCA1 and analogs.
Fig. 5: Covalent SHP1 agonists antagonize TLR4 signaling in macrophages.
Fig. 6: Covalent SHP1 agonists inhibit TLR4-mediated proinflammatory cytokine response in macrophages and in vivo.

Similar content being viewed by others

Data availability

The MS proteomics data were deposited to the ProteomeXchange Consortium through the PRIDE partner repository with dataset identifiers PXD072062 and PXD055006. The OxImmune compendium of redox-regulated cysteines on immune proteins is provided as an online resource (https://oximmune-chouchani-lab.dfci.harvard.edu/). Source data are provided with this paper.

References

  1. O’Neill, L. A. & Pearce, E. J. Immunometabolism governs dendritic cell and macrophage function. J. Exp. Med. 213, 15–23 (2016).

    Article  PubMed  Google Scholar 

  2. Tran, N. & Mills, E. L. Redox regulation of macrophages. Redox Biol. 72, 103123 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lennicke, C. & Cochemé, H. M. Redox metabolism: ROS as specific molecular regulators of cell signaling and function. Mol. Cell 81, 3691–3707 (2021).

    Article  CAS  PubMed  Google Scholar 

  4. Schieber, M. & Chandel, N. S. ROS function in redox signaling and oxidative stress. Curr. Biol. 24, R453–R462 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. West, A. P., Shadel, G. S. & Ghosh, S. Mitochondria in innate immune responses. Nat. Rev. Immunol. 11, 389–402 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Murphy, M. P. & O’Neill, L. A. J. Krebs cycle reimagined: the emerging roles of succinate and itaconate as signal transducers. Cell 174, 780–784 (2018).

    Article  CAS  PubMed  Google Scholar 

  7. Mills, E. L. et al. Succinate dehydrogenase supports metabolic repurposing of mitochondria to drive inflammatory macrophages. Cell 167, 457–470 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Garaude, J. et al. Mitochondrial respiratory-chain adaptations in macrophages contribute to antibacterial host defense. Nat. Immunol. 17, 1037–1045 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Mills, E. L. et al. Itaconate is an anti-inflammatory metabolite that activates Nrf2 via alkylation of KEAP1. Nature 556, 113–117 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bambouskova, M. et al. Electrophilic properties of itaconate and derivatives regulate the IκBζ–ATF3 inflammatory axis. Nature 556, 501–504 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Vinogradova, E. V. et al. An activity-guided map of electrophile–cysteine interactions in primary human T cells. Cell 182, 1009–1026 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Boike, L., Henning, N. J. & Nomura, D. K. Advances in covalent drug discovery. Nat. Rev. Drug Discov. 21, 881–898 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Weerapana, E. et al. Quantitative reactivity profiling predicts functional cysteines in proteomes. Nature 468, 790–795 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Xiao, H. et al. A quantitative tissue-specific landscape of protein redox regulation during aging. Cell 180, 968–983 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Kisty, E. A., Saart, E. C. & Weerapana, E. Identifying redox-sensitive cysteine residues in mitochondria. Antioxidants 12, 992 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Carroll, K. Defining and refining the cysteine redoxome with sulfur chemical biology. Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv-2022-pmk47 (2022).

  17. Duan, J. et al. Stochiometric quantification of the thiol redox proteome of macrophages reveals subcellular compartmentalization and susceptibility to oxidative perturbations. Redox Biol. 36, 101649 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Christophi, G. P. et al. Interferon-β treatment in multiple sclerosis attenuates inflammatory gene expression through inducible activity of the phosphatase SHP-1. Clin. Immunol. 133, 27–44 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Christophi, G. P. et al. Macrophages of multiple sclerosis patients display deficient SHP-1 expression and enhanced inflammatory phenotype. Lab Invest. 89, 742–759 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Breuer, K. et al. InnateDB: systems biology of innate immunity and beyond–recent updates and continuing curation. Nucleic Acids Res. 41, D1228–D1233 (2013).

    Article  CAS  PubMed  Google Scholar 

  21. Fu, L. et al. Nucleophilic covalent ligand discovery for the cysteine redoxome. Nat. Chem. Biol. 19, 1309–1319 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Scheer, J. M., Romanowski, M. J. & Wells, J. A. A common allosteric site and mechanism in caspases. Proc. Natl Acad. Sci. USA 103, 7595–7600 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Bozonet, S. M. et al. Oxidation of caspase-8 by hypothiocyanous acid enables TNF-mediated necroptosis. J. Biol. Chem. 299, 104792 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Yang, J. et al. Mechanism of gasdermin D recognition by inflammatory caspases and their inhibition by a gasdermin D-derived peptide inhibitor. Proc. Natl Acad. Sci. USA 115, 6792–6797 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Brady, K. D. et al. A catalytic mechanism for caspase-1 and for bimodal inhibition of caspase-1 by activated aspartic ketones. Bioorg. Med. Chem. 7, 621–631 (1999).

    Article  CAS  PubMed  Google Scholar 

  26. Xu, J. H. et al. Integrative X-ray structure and molecular modeling for the rationalization of procaspase-8 inhibitor potency and selectivity. ACS Chem. Biol. 15, 575–586 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Xu, G. et al. Covalent inhibition revealed by the crystal structure of the caspase-8/p35 complex. Nature 410, 494–497 (2001).

    Article  CAS  PubMed  Google Scholar 

  28. Wang, Z. et al. Kinetic and structural characterization of caspase-3 and caspase-8 inhibition by a novel class of irreversible inhibitors. Biochim. Biophys. Acta 1804, 1817–1831 (2010).

    Article  CAS  PubMed  Google Scholar 

  29. Smith, J. K. et al. Identification of a redox-sensitive switch within the JAK2 catalytic domain. Free Radic. Biol. Med. 52, 1101–1110 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Truong, T. H. & Carroll, K. S. Redox regulation of protein kinases. Crit. Rev. Biochem. Mol. Biol. 48, 332–356 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Castelo-Soccio, L. et al. Protein kinases: drug targets for immunological disorders. Nat. Rev. Immunol. 23, 787–806 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ghoreschi, K., Laurence, A. & O’Shea, J. J. Janus kinases in immune cell signaling. Immunol. Rev. 228, 273–287 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Singh, S. et al. The reduced activity of PP-1α under redox stress condition is a consequence of GSH-mediated transient disulfide formation. Sci. Rep. 8, 17711 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Chu, Y. et al. Effect of activation of protein phosphatase 1 on sulfhydryl reactivity. Arch. Biochem. Biophys. 334, 83–88 (1996).

    Article  CAS  PubMed  Google Scholar 

  35. Kim, H. S. et al. Constitutive induction of p-Erk1/2 accompanied by reduced activities of protein phosphatases 1 and 2A and MKP3 due to reactive oxygen species during cellular senescence. J. Biol. Chem. 278, 37497–37510 (2003).

    Article  CAS  PubMed  Google Scholar 

  36. Gu, M. et al. Protein phosphatase PP1 negatively regulates the Toll-like receptor- and RIG-I-like receptor-triggered production of type I interferon by inhibiting IRF3 phosphorylation at serines 396 and 385 in macrophage. Cell Signal. 26, 2930–2939 (2014).

    Article  CAS  PubMed  Google Scholar 

  37. Yang, T. et al. Macrophage PTEN controls STING-induced inflammation and necroptosis through NICD/NRF2 signaling in APAP-induced liver injury. Cell Commun. Signal. 21, 160 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Kwon, J. et al. Reversible oxidation and inactivation of the tumor suppressor PTEN in cells stimulated with peptide growth factors. Proc. Natl Acad. Sci. USA 101, 16419–16424 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lee, S. R. et al. Reversible inactivation of the tumor suppressor PTEN by H2O2. J. Biol. Chem. 277, 20336–20342 (2002).

    Article  CAS  PubMed  Google Scholar 

  40. Teruya, T. et al. Phoslactomycin targets cysteine-269 of the protein phosphatase 2A catalytic subunit in cells. FEBS Lett. 579, 2463–2468 (2005).

    Article  CAS  PubMed  Google Scholar 

  41. Visscher, M., Arkin, M. R. & Dansen, T. B. Covalent targeting of acquired cysteines in cancer. Curr. Opin. Chem. Biol. 30, 61–67 (2016).

    Article  CAS  PubMed  Google Scholar 

  42. Shannon, D. A. & Weerapana, E. Covalent protein modification: the current landscape of residue-specific electrophiles. Curr. Opin. Chem. Biol. 24, 18–26 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. Stanford, S. M. & Bottini, N. Targeting protein phosphatases in cancer immunotherapy and autoimmune disorders. Nat. Rev. Drug Discov. 22, 273–294 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Nandan, D. & Reiner, N. E. Leishmania donovani engages in regulatory interference by targeting macrophage protein tyrosine phosphatase SHP-1. Clin. Immunol. 114, 266–277 (2005).

    Article  CAS  PubMed  Google Scholar 

  45. Croker, B. A. et al. Inflammation and autoimmunity caused by a SHP1 mutation depend on IL-1, MyD88, and a microbial trigger. Proc. Natl Acad. Sci. USA 105, 15028–15033 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Singh, K. et al. ERK-dependent T cell receptor threshold calibration in rheumatoid arthritis. J. Immunol. 183, 8258–8267 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Markovics, A., Toth, D. M., Glant, T. T. & Mikecz, K. Regulation of autoimmune arthritis by the SHP-1 tyrosine phosphatase. Arthritis Res. Ther. 22, 160 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. An, H. et al. Phosphatase SHP-1 promotes TLR- and RIG-I-activated production of type I interferon by inhibiting the kinase IRAK1. Nat. Immunol. 9, 542–550 (2008).

    Article  CAS  PubMed  Google Scholar 

  49. Abram, C. L. & Lowell, C. A. SHP1 function in myeloid cells. J. Leukoc. Biol. 102, 657–675 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Wang, W. et al. Crystal structure of human protein tyrosine phosphatase SHP-1 in the open conformation. J. Cell. Biochem. 112, 2062–2071 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Takahashi, M. et al. DrugMap: a quantitative pan-cancer analysis of cysteine ligandability. Cell 187, 2536–2556 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Darabedian, N. et al. Depletion of creatine phosphagen energetics with a covalent creatine kinase inhibitor. Nat. Chem. Biol. 19, 815–824 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Burger, N. et al. The human zinc-binding cysteine proteome. Cell 188, 832–850 (2025).

    Article  CAS  PubMed  Google Scholar 

  54. Yang, F. et al. IKKβ plays an essential role in the phosphorylation of RelA/p65 on serine 536 induced by lipopolysaccharide. J. Immunol. 170, 5630–5635 (2003).

    Article  CAS  PubMed  Google Scholar 

  55. Zhang, Z. et al. The role of C-terminal tyrosine phosphorylation in the regulation of SHP-1 explored via expressed protein ligation. J. Biol. Chem. 278, 4668–4674 (2003).

    Article  CAS  PubMed  Google Scholar 

  56. Jones, M. L. et al. Regulation of SHP-1 tyrosine phosphatase in human platelets by serine phosphorylation at its C terminus. J. Biol. Chem. 279, 40475–40483 (2004).

    Article  CAS  PubMed  Google Scholar 

  57. Xu, S. et al. Phospho-Tyr705 of STAT3 is a therapeutic target for sepsis through regulating inflammation and coagulation. Cell Commun. Signal. 18, 104 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Balic, J. J. et al. STAT3 serine phosphorylation is required for TLR4 metabolic reprogramming and IL-1β expression. Nat. Commun. 11, 3816 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Lu, H. C. et al. STAT3 signaling in myeloid cells promotes pathogenic myelin-specific T cell differentiation and autoimmune demyelination. Proc. Natl Acad. Sci. USA 117, 5430–5441 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Myers, D. R. et al. SHP1 loss enhances macrophage effector function and promotes anti-tumor immunity. Front. Immunol. 11, 576310 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Lorenz, U. SHP-1 and SHP-2 in T cells: two phosphatases functioning at many levels. Immunol. Rev. 228, 342–359 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Pao, L. I. et al. Nonreceptor protein-tyrosine phosphatases in immune cell signaling. Annu. Rev. Immunol. 25, 473–523 (2007).

    Article  CAS  PubMed  Google Scholar 

  63. Bodenhofer, U. et al. msa: an R package for multiple sequence alignment. Bioinformatics 31, 3997–3999 (2015).

    Article  CAS  PubMed  Google Scholar 

  64. Damle, N. P. & Köhn, M. The human DEPhOsphorylation Database DEPOD: 2019 update. Database 2019, baz133 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Hu, R. et al. KinaseMD: kinase mutations and drug response database. Nucleic Acids Res. 49, D552–D561 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Uhlén, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    Article  PubMed  Google Scholar 

  67. Liska, O. et al. TFLink: an integrated gateway to access transcription factor-target gene interactions for multiple species. Database 2022, baac083 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Corcoran, C. C. et al. From 20th century metabolic wall charts to 21st century systems biology: database of mammalian metabolic enzymes. Am. J. Physiol. Renal Physiol. 312, F533–F542 (2017).

    Article  PubMed  Google Scholar 

  69. Evavold, C. L. et al. Control of gasdermin D oligomerization and pyroptosis by the Ragulator–Rag–mTORC1 pathway. Cell 184, 4495–4511 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Eng, J. K., Jahan, T. A. & Hoopmann, M. R. Comet: an open-source MS/MS sequence database search tool. Proteomics 13, 22–24 (2013).

    Article  CAS  PubMed  Google Scholar 

  71. Bowers, K. J. et al. Scalable algorithms for molecular dynamics simulations on commodity clusters. In Proc. 2006 ACM/IEEE Conference on Supercomputing (ed. Horner-Miller, B.) 43 (ACM, 2006).

  72. Wales, T. E. & Engen, J. R. Hydrogen exchange mass spectrometry for the analysis of protein dynamics. Mass Spectrom. Rev. 25, 158–170 (2006).

    Article  CAS  PubMed  Google Scholar 

  73. Perez-Riverol, Y. et al. The PRIDE database at 20 years: 2025 update. Nucleic Acids Res. 53, D543–D553 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Mills, E. L. et al. Cysteine 253 of UCP1 regulates energy expenditure and sex-dependent adipose tissue inflammation. Cell Metab. 34, 140–157 (2022).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the Claudia Adams Barr Program (E.T.C.), the Lavine Family Fund (E.T.C.), the Pew Charitable Trust (E.T.C.), the National Institutes of Health (DK123095, AG071966 and AI175317, E.T.C.), the Smith Family Foundation (E.T.C.), the American Federation for Aging Research (E.T.C.) and the Dana-Farber Cancer Institute Innovation Research Fund (E.T.C.). E.T.C. is a Howard Hughes Medical Institute investigator. The NMR instrumentation was supported by the High-End Instrumentation Grant (S10OD028697-01) awarded to Stanford Chemistry, Engineering and Medicine for Human Health. M.N.N. was supported by the David L. Sze and Kathleen Donahue Interdisciplinary Fellowship.

Author information

Author notes

  1. These authors contributed equally: Mei Ying Ng, Meredith N. Nix.

Authors and Affiliations

  1. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA

    Mei Ying Ng, Nils Burger, Sanghee Shin, Haruna Takeda, Megan Cheah Xin Yan, Bingsen Zhang, Haopeng Xiao, Shelley M. Wei, Hyuk-Soo Seo, Sirano Dhe-Paganon, Jianwei Che & Edward T. Chouchani

  2. Department of Cell Biology, Harvard Medical School, Boston, MA, USA

    Mei Ying Ng, Nils Burger, Sanghee Shin, Haruna Takeda, Megan Cheah Xin Yan, Bingsen Zhang, Haopeng Xiao, Shelley M. Wei & Edward T. Chouchani

  3. Department of Chemical and Systems Biology, CHEM-H and SCI, Stanford Medical School, Stanford University, Stanford, CA, USA

    Meredith N. Nix, Guangyan Du, Ivan Davidek, Sean Toenjes, Tinghu Zhang & Nathanael S. Gray

  4. Department of Chemistry, Stanford University, Stanford, CA, USA

    Meredith N. Nix

  5. Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA

    Thomas E. Wales & John R. Engen

  6. Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA

    Evanna L. Mills

  7. Department of Immunology, Harvard Medical School, Boston, MA, USA

    Evanna L. Mills

  8. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Edward T. Chouchani

Authors
  1. Mei Ying Ng
    View author publications

    Search author on:PubMed Google Scholar

  2. Meredith N. Nix
    View author publications

    Search author on:PubMed Google Scholar

  3. Guangyan Du
    View author publications

    Search author on:PubMed Google Scholar

  4. Ivan Davidek
    View author publications

    Search author on:PubMed Google Scholar

  5. Nils Burger
    View author publications

    Search author on:PubMed Google Scholar

  6. Sanghee Shin
    View author publications

    Search author on:PubMed Google Scholar

  7. Sean Toenjes
    View author publications

    Search author on:PubMed Google Scholar

  8. Haruna Takeda
    View author publications

    Search author on:PubMed Google Scholar

  9. Megan Cheah Xin Yan
    View author publications

    Search author on:PubMed Google Scholar

  10. Bingsen Zhang
    View author publications

    Search author on:PubMed Google Scholar

  11. Haopeng Xiao
    View author publications

    Search author on:PubMed Google Scholar

  12. Shelley M. Wei
    View author publications

    Search author on:PubMed Google Scholar

  13. Hyuk-Soo Seo
    View author publications

    Search author on:PubMed Google Scholar

  14. Sirano Dhe-Paganon
    View author publications

    Search author on:PubMed Google Scholar

  15. Thomas E. Wales
    View author publications

    Search author on:PubMed Google Scholar

  16. John R. Engen
    View author publications

    Search author on:PubMed Google Scholar

  17. Evanna L. Mills
    View author publications

    Search author on:PubMed Google Scholar

  18. Jianwei Che
    View author publications

    Search author on:PubMed Google Scholar

  19. Tinghu Zhang
    View author publications

    Search author on:PubMed Google Scholar

  20. Nathanael S. Gray
    View author publications

    Search author on:PubMed Google Scholar

  21. Edward T. Chouchani
    View author publications

    Search author on:PubMed Google Scholar

Contributions

M.Y.N., J.C., T.Z., N.S.G. and E.T.C. conceptualized and designed the study. M.Y.N. and M.C.X.Y. performed the cellular experiments and analyzed the data. M.N.N., G.D., I.D., S.T., J.C. and T.Z. designed and conducted the chemical syntheses. M.Y.N., N.B. and S.M.W. developed and designed the OxImmune resource and web application. H.X. provided the Oximouse dataset. M.Y.N., S.S., B.Z. and H.X. carried out and analyzed the data from CPT-MS experiments. H.X. developed the CPT-MS technology. M.Y.N. and S.S. carried out and analyzed the data from SHP1 intact protein and SHP1 cysteine site engagement MS experiments. H.-S.S. and S.D.-P. carried out and oversaw the SHP1 protein expression and purification. J.C. performed the molecular modeling. M.Y.N., T.E.W. and J.R.E. designed and carried out the HDX-MS experiments and analyzed the data. M.Y.N., H.T. and E.L.M. performed and analyzed the data from in vivo experiments. M.Y.N., J.C., T.Z., N.S.G. and E.T.C. directed the research, oversaw the experiments and wrote the paper with assistance from the other authors.

Corresponding authors

Correspondence to Jianwei Che, Tinghu Zhang, Nathanael S. Gray or Edward T. Chouchani.

Ethics declarations

Competing interests

E.T.C. is cofounder of Matchpoint Therapeutics and Aevum Therapeutics. J.C. is a cofounder of Matchpoint Therapeutics, scientific cofounder of M3 Bioinformatics and Technology and consultant and equity holder for Soltego and Allorion. N.S.G. is a founder, science advisory board member and equity holder for Syros, C4, Allorion, Lighthorse, Inception, Matchpoint, Shenandoah (board member), Larkspur (board member) and Soltego (board member). The N.S.G. lab receives or has received research funding from Novartis, Takeda, Astellas, Taiho, Jansen, Kinogen, Arbella, Deerfield, Springworks, Interline and Sanofi. M.Y.N., M.N.N., G.D., E.T.C., J.C., T.Z. and N.S.G. are inventors on a patent WO/2025/109475 for the SHP1 compounds described in this work. The other authors declare no competing interests.

Peer review

Peer review information

Nature Chemical Biology thanks Oliver Hantschel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Redox sensitivity of SHP1 Cys102 and selectivity of engagement by SCA1 in macrophage cysteine proteome.

a, Schematic of Oximouse/OxImmune workflow for quantification of cysteine redox state, illustrating CPT labeling, TMT multiplexing, IMAC enrichment, and MS analysis. Created in BioRender. Ng, M. (2025) https://BioRender.com/e74b682. b, Relative gene expression of differentiation markers in THP-1 monocytes treated with either 10 ng/ml PMA for 24 h or 100 ng/ml PMA for 48 h (n = 4 biological replicates). c, Relative cysteine oxidation level of SHP1 Cys102 in THP-1 MDMs treated with N-acetylcysteine (NAC, 10 mM), cell permeable TCEP (tmTCEP, 1 mM) (n = 5) or lipopolysaccharide (LPS, 100 ng/ml) (n = 3) for 1 h. d, Relative cysteine oxidation level of SHP1 Cys102 in THP-1 MDMs (n = 3) treated with LPS (100 ng/ml) for the indicated time periods. e, Pairwise comparisons of R for each cysteine quantified in iBMDM replicates treated with 5, 10, 20, or 40 μM SCA1 for 3 h (37 °C), followed by CPT enrichment. R = (S/N of DMSO) / (S/N of SCA1), where S/N is signal-to-noise ratio. f, SHP1 Cys102 site-specific percent modification by SCA1 in iBMDMs at the indicated concentrations. Data are mean ± s.d. of n = 2 biological replicates. g,h, SHP1 (g) or SHP2 (h) cysteines site-specific percent modification by SCA1 in iBMDMs (n = 2 biological replicates) at the indicated concentrations. Data are mean ± s.e.m. (in b) or s.d. (in c-h). P values calculated using one-way or two-way ANOVA for multiple comparisons.

Source data

Extended Data Fig. 2 Structural modeling of SCA1 binding to SHP1.

a, Covalent docking model of SCA1 with auto-inhibited form of SHP1 (PDB ID: 2B3O) showing shallow binding surface for anchorage of SCA1. N-SH2, C-SH2 and PTP domains represented in orange, gray and blue respectively. b, (Left) Crystal structures alignment of auto-inhibited SHP2 with (green, PDB ID: 6MD9) or without allosteric inhibitor isoxazolo-pyridinone 3 (yellow, inhibitor as stick-ball mode, PDB ID: 6CMP), showing similar conformation. (Right) Crystal structures alignment of apo auto-inhibited SHP2 (yellow, PDB ID: 6CMP) and apo auto-inhibited SHP1 (cyan, PDB ID: 2B3O) showing significant difference in the C-SH2 domain orientation and lack of allosteric site in inactive SHP1. c, Covalent docking of SCA1 to inactive SHP1 (represented in orange, gray and blue) aligned with auto-inhibited SHP2 (yellow, PDB ID: 6CMP) showing a different SCA1 occupancy location. d, Structural alignment of hypothetical binding mode of SCA1 to inactive SHP1 (represented in orange, gray and blue) with crystal structure of inactive SHP2 with allosteric inhibitor isoxazolo-pyridinone 3 (green, PDB ID: 6MD9), showing incompatibility with C-SH2 of SHP2 assuming binding to hinge cysteine on SHP2. e, Sequence alignment of SHP1 (PTN6) and SHP2 (PTN11) showing SCA1 binding site residues (orange thin lines) on SHP1 and their corresponding residues on SHP2.

Extended Data Fig. 3 Characterization of SHP1 binding and phosphatase activation kinetics by SCA1 and analogs in vitro.

a, Percent SHP1 engagement of human recombinant SHP1 (2 μM) incubated with the full breadth of SCA1 analogs at 5 molar equivalents (10 μM) for 24 h (4 °C) and measured by intact protein MS. Percent SHP1 engagement represents relative abundance of small molecule engaged SHP1 to relative abundance of unlabeled SHP1. Data are mean ± s.d. of two independent experiments. b, Full list of structures of SCA1 analogs at the indicated molar equivalents and their respective percent SHP1 engagement as measured by intact SHP1 protein (2 μM) MS are shown. c, Differential backbone amine proton solvent accessible surface area (BB NH proton differential SASA) between SCA9 bound and apo SHP1 calculated from molecular dynamics simulations. Highlighted peptide regions that showed extensive uptake of deuterium were also more solvent exposed upon SCA9 binding during molecular simulations. For full list of molecular simulations peptides, refer to Supplementary Table 6. d, Recombinant human SHP1 phosphatase enzymatic activity following 30 min pre-incubation (room temperature) with DMSO or SCA1 (10 μM) measured at 37 °C and represented as phosphate equivalents released over a time course of 2 h (n = 4). e, Intact protein MS of human recombinant SHP1 (2 μM) incubated with 10, 15, 20, 25, 30, 35, 40, 45, or 50 molar equivalents of SCA1 for 48 h (4 °C). Percent SHP1 labeling represents relative abundance of SCA1 engaged SHP1 to relative abundance of unlabeled SHP1. Data are mean ± s.e.m. P values calculated using two-tailed Student’s t-tests for unpaired comparisons.

Source data

Extended Data Fig. 4 Characterization of SCA and pY-ITIM peptide binding to SHP1.

a, Crystal structure alignment of SHP1 phosphatase domain bound to phosphorylated Tyr469 signal regulatory protein alpha (SIRPα) ITIM peptide (PDB ID: 1FPR) with SCA1-SHP1 binding model, showing distant binding sites between phospho-ITIM peptide and SCA1. Orange ribbon indicates the ITIM peptide, and gray cartoon represents the SHP1 phosphatase domain in the PDB structure. SCA1-bound SHP1 is represented in marine cartoon with SCA1 in stick-ball mode. b, Crystal structures alignment of SHP1 C-SH2 domain bound to phosphorylated tyrosine NKG2A peptide (PDB ID: 2YU7, NMR structure) and SHP1 N-SH2 bound to phosphorylated tyrosine peptide (RLNpYAQLWHR) (PDB ID: 3TL0) with our full-length model, showing distant SCA1 binding site from all peptides binding clefts. Orange ribbons indicate the ITIM peptides, gray cartoon represents the C-SH2 domain in PDB structure (PDB ID: 2YU7), green cartoon represents the N-SH2 domain in PDB structure (PDB ID: 3TL0). SCA1-bound SHP1 is represented in marine cartoon with SCA1 in stick-ball mode. c, Fluorescence polarization of phospho-tyrosine ITIM peptide of IRAK1 in the presence of catalytically dead SHP1 (C453S SHP1) at the indicated concentrations from 10 nM to 25 μM, pre-incubated 3 h at room temperature with SCA9, SCA7, SCA25 or SHP1/SHP2 inhibitor NSC-87877 (50 μM). Normalized fluorescence polarization of pY-ITIM peptide expressed in millipolarization (mP) as a function of SHP1 concentration is fitted using non-linear regression model to calculate binding affinity IC50. d, Fluorescence polarization of phospho-tyrosine ITIM peptide of IRAK1 in the presence of SHP1 C453S pre-incubated 1 h at room temperature with SCA9, SCA7, SCA25 or NSC-87877 at the indicated concentrations from 20 nM to 50 μM. Fluorescence polarization of pY-ITIM peptide expressed in mP as a function of ligand concentration is fitted using non-linear regression model to calculate binding affinity IC50.

Source data

Extended Data Fig. 5 Characterization of SCA1 and analogs binding to SHP1 in cells.

a,b, SHP1 binding by SCA1 derivatives in iBMDMs treated at 20 μM (a) or 5 μM (b) followed by competition for SHP1 binding with biotinylated SCA1 (10 μM). Experiments were repeated three times in e, two times in f with similar results. c-e, SHP1 binding by SCA1 derivatives in primary BMDMs (c), THP-1 MDMs (d), and THP-1 monocytes (e) treated at 5 μM followed by competition for SHP1 binding with biotinylated SCA1 (10 μM). Experiments were repeated three times in c,e, two times in d with similar results. f, Pairwise comparisons of R for each cysteine quantified in iBMDM replicates treated with 50 μM SCA9, SCA5 or SCA1-NC for 3 h (37 °C), followed by CPT enrichment. R = (S/N of DMSO) / (S/N of SCA), where S/N is signal-to-noise ratio.

Source data

Extended Data Fig. 6 SCA effects on SHP1-dependent signaling pathways and pro-inflammatory cytokine response in macrophages.

a, LPS-induced (100 ng/ml) SHP1 Tyr536, Tyr564 and Ser591 phosphorylation in iBMDMs over a time course of 2 h in iBMDMs pre-treated with DMSO or SCA1 (50 μM) for 3 h. Immunoblots shown are representative of three independent experiments. b, LPS-induced (100 ng/ml) STAT3 Tyr705 and Ser727 phosphorylation in iBMDMs over a time course of 2 h in iBMDMs pre-treated with DMSO or SCA1 (50 μM) for 3 h. Immunoblots shown are representative of three independent experiments. c,e,f, Pro-inflammatory cytokine IL-6 and TNF levels in cell supernatants of iBMDMs pre-incubated 3 h with SCA1, SCA1-NC or the respective derivatives over a dose response (0.313-80 μM) followed by 6 h LPS stimulation (100 ng/ml) (n = 4). For measurement of pro-inflammatory cytokine IL-1β levels in cell supernatants, iBMDMs were pre-treated with LPS (100 ng/ml) for 3 h followed by treatment with DMSO or SCA9 at the indicated concentrations for 45 min and primed with adenosine triphosphate (ATP, 5 mM) for 45 min (n = 3). Absolute cytokine production as a function of concentration is used to calculate IC50 values. d, Pro-inflammatory cytokine IL-6 and TNF levels in cell supernatants of iBMDMs treated 3 h with SCA1 (20 μM) alone or 6 h with LPS (100 ng/ml) alone (n = 3). g, Phagocytic activity of iBMDMs pre-treated 3 h with SCA1, SCA9, SCA7 or SCA25 (10 μM) followed by 6 h LPS stimulation (100 ng/ml) (n = 6). h, Relative fold changes in protein abundance between iBMDMs treated with SCA1 (50 μM) for 3 h followed by LPS (100 ng/ml, 15 min) versus LPS alone (n = 3). Data are mean ± s.e.m. (in d,h) or s.d. (in c,e-g). P values calculated using one-way or two-way ANOVA for multiple comparisons or two-tailed Student’s t-tests for unpaired comparisons.

Source data

Extended Data Fig. 7 Flow cytometry analysis and cytokine response of monocytes from healthy and RA or MS patient donors.

a, Gating strategy for flow cytometry analysis of healthy donors or RA and MS patients PBMCs isolated monocytes using CD45, CD14 and CD16 staining (n = 3 for each healthy and patient group). b, Percentage of CD45 + CD14 + CD16- monocyte population of interest (n = 3 for each healthy and patient group). c, Pro-inflammatory cytokine TNF, IL-6 and IL-1β levels in cell supernatants of healthy donors and RA and MS patients monocytes pre-incubated 3 h with SCA1, SCA9, SCA7 and SCA25 (20 μM) followed by 6 h LPS stimulation (100 ng/ml) (n = 3 for each healthy and patient group). For measurement of pro-inflammatory cytokine IL-1β levels in cell supernatants, cells were additionally treated with adenosine triphosphate (ATP, 5 mM) for 45 min following LPS treatment. Data are mean ± s.e.m. P values calculated using two-tailed Student’s t-test for unpaired comparison or two-way ANOVA for multiple comparisons.

Source data

Supplementary information

Supplementary Information (download PDF )

Supplementary Note, Methods and Figs. 1–35.

Reporting Summary (download PDF )

Supplementary Table 1 (download XLSX )

Redox modification state of immunological cysteines and annotation to protein structure and functional domains.

Supplementary Table 2 (download XLSX )

Oligonucleotide sequences for gene expression analysis of THP-1.

Supplementary Table 3 (download XLSX )

Stoichiometric engagement score (R) of SCA1 titration with THP-1 MDM cysteine proteome.

Supplementary Table 4 (download XLSX )

Stoichiometric engagement score (R) of SCA1 titration with iBMDM cysteine proteome.

Supplementary Table 5 (download XLSX )

HDX-MS of SHP1 in the presence and absence of SCA9.

Supplementary Table 6 (download XLSX )

Differential backbone amine proton solvent-accessible surface area between apo and SCA9-bound SHP1.

Supplementary Table 7 (download XLSX )

Stoichiometric engagement score (R) of SCA9, SCA5 and SCA1-NC with iBMDM cysteine proteome.

Source data

Source Data Fig. 3 (download XLSX )

Statistical source data.

Source Data Fig. 3 (download PDF )

Unprocessed western blots.

Source Data Fig. 4 (download XLSX )

Statistical source data.

Source Data Fig. 5 (download XLSX )

Statistical source data.

Source Data Fig. 5 (download PDF )

Unprocessed western blots.

Source Data Fig. 6 (download XLSX )

Statistical source data.

Source Data Fig. 6 (download PDF )

Unprocessed western blots.

Source Data Extended Data Fig. 1 (download XLSX )

Statistical source data.

Source Data Extended Data Fig. 3 (download XLSX )

Statistical source data.

Source Data Extended Data Fig. 4 (download XLSX )

Statistical source data.

Source Data Extended Data Fig. 5 (download PDF )

Unprocessed western blots.

Source Data Extended Data Fig. 6 (download XLSX )

Statistical source data.

Source Data Extended Data Fig. 6 (download PDF )

Unprocessed western blots.

Source Data Extended Data Fig. 7 (download XLSX )

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ng, M.Y., Nix, M.N., Du, G. et al. A druggable redox switch on SHP1 controls macrophage inflammation. Nat Chem Biol (2026). https://doi.org/10.1038/s41589-026-02163-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s41589-026-02163-8

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing