A druggable copper-signalling pathway that drives inflammation.
Animals
Mice
Copper
/ metabolism
Inflammation
/ drug therapy
Macrophages
/ drug effects
NAD
/ metabolism
Signal Transduction
/ drug effects
Mitochondria
/ drug effects
Hydrogen Peroxide
/ metabolism
Epigenesis, Genetic
/ drug effects
Metformin
/ analogs & derivatives
Oxidation-Reduction
Cell Plasticity
/ drug effects
Macrophage Activation
/ drug effects
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
May 2023
May 2023
Historique:
received:
21
03
2022
accepted:
27
03
2023
medline:
12
5
2023
pubmed:
27
4
2023
entrez:
26
4
2023
Statut:
ppublish
Résumé
Inflammation is a complex physiological process triggered in response to harmful stimuli
Identifiants
pubmed: 37100912
doi: 10.1038/s41586-023-06017-4
pii: 10.1038/s41586-023-06017-4
pmc: PMC10131557
doi:
Substances chimiques
Copper
789U1901C5
NAD
0U46U6E8UK
Cd44 protein, mouse
0
Hydrogen Peroxide
BBX060AN9V
Metformin
9100L32L2N
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
386-394Commentaires et corrections
Type : CommentIn
Type : CommentIn
Type : CommentIn
Type : CommentIn
Informations de copyright
© 2023. The Author(s).
Références
Lopez-Otin, C. & Kroemer, G. Hallmarks of health. Cell 184, 1929–1939 (2021).
pubmed: 33798440
doi: 10.1016/j.cell.2021.03.033
Annane, D., Bellissant, E. & Cavaillon, J. M. Septic shock. Lancet 365, 63–78 (2005).
pubmed: 15639681
doi: 10.1016/S0140-6736(04)17667-8
Moore, J. B. & June, C. H. Cytokine release syndrome in severe COVID-19. Science 368, 473–474 (2020).
pubmed: 32303591
doi: 10.1126/science.abb8925
Park, M. D., Silvin, A., Ginhoux, F. & Merad, M. Macrophages in health and disease. Cell 185, 4259–4279 (2022).
pubmed: 36368305
pmcid: 9908006
doi: 10.1016/j.cell.2022.10.007
Horby, P. et al. Dexamethasone in hospitalized patients with Covid-19. N. Engl. J. Med. 384, 693–704 (2021).
pubmed: 32678530
doi: 10.1056/NEJMoa2021436
Annane, D. et al. Hydrocortisone plus fludrocortisone for adults with septic shock. N. Engl. J. Med. 378, 809–818 (2018).
pubmed: 29490185
doi: 10.1056/NEJMoa1705716
Rudd, K. E. et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. Lancet 395, 200–211 (2020).
pubmed: 31954465
pmcid: 6970225
doi: 10.1016/S0140-6736(19)32989-7
Aruffo, A., Stamenkovic, I., Melnick, M., Underhill, C. B. & Seed, B. CD44 is the principal cell surface receptor for hyaluronate. Cell 61, 1303–1313 (1990).
pubmed: 1694723
doi: 10.1016/0092-8674(90)90694-A
Hua, Q., Knudson, C. B. & Knudson, W. Internalization of hyaluronan by chondrocytes occurs via receptor-mediated endocytosis. J. Cell Sci. 106, 365–375 (1993).
pubmed: 7505784
doi: 10.1242/jcs.106.1.365
Toole, B. P. Hyaluronan: from extracellular glue to pericellular cue. Nat. Rev. Cancer 4, 528–539 (2004).
pubmed: 15229478
doi: 10.1038/nrc1391
Ponta, H., Sherman, L. & Herrlich, P. A. CD44: from adhesion molecules to signalling regulators. Nat. Rev. Mol. Cell Biol. 4, 33–45 (2003).
pubmed: 12511867
doi: 10.1038/nrm1004
Brabletz, T., Kalluri, R., Nieto, M. A. & Weinberg, R. A. EMT in cancer. Nat. Rev. Cancer 18, 128–134 (2018).
pubmed: 29326430
doi: 10.1038/nrc.2017.118
Guilliams, M. & Svedberg, F. R. Does tissue imprinting restrict macrophage plasticity? Nat. Immunol. 22, 118–127 (2021).
pubmed: 33462453
doi: 10.1038/s41590-020-00849-2
Puré, E. & Cuff, C. A. A crucial role for CD44 in inflammation. Trends Mol. Med. 7, 213–221 (2001).
pubmed: 11325633
doi: 10.1016/S1471-4914(01)01963-3
Teder, P. et al. Resolution of lung inflammation by CD44. Science 296, 155–158 (2002).
pubmed: 11935029
doi: 10.1126/science.1069659
Bartolazzi, A., Peach, R., Aruffo, A. & Stamenkovic, I. Interaction between CD44 and hyaluronate is directly implicated in the regulation of tumor development. J. Exp. Med. 180, 53–66 (1994).
pubmed: 7516417
doi: 10.1084/jem.180.1.53
Zoltan-Jones, A., Huang, L., Ghatak, S. & Toole, B. P. Elevated hyaluronan production induces mesenchymal and transformed properties in epithelial cells. J. Biol. Chem. 278, 45801–45810 (2003).
pubmed: 12954618
doi: 10.1074/jbc.M308168200
Zöller, M. CD44: can a cancer-initiating cell profit from an abundantly expressed molecule? Nat. Rev. Cancer 11, 254–267 (2011).
pubmed: 21390059
doi: 10.1038/nrc3023
Müller, S. et al. CD44 regulates epigenetic plasticity by mediating iron endocytosis. Nat. Chem. 12, 929–938 (2020).
pubmed: 32747755
pmcid: 7612580
doi: 10.1038/s41557-020-0513-5
McKee, C. M. et al. Hyaluronan (HA) fragments induce chemokine gene expression in alveolar macrophages. The role of HA size and CD44. J. Clin. Invest. 98, 2403–1243 (1996).
pubmed: 8941660
pmcid: 507693
doi: 10.1172/JCI119054
Kruidenier, L. et al. A selective jumonji H3K27 demethylase inhibitor modulates the proinflammatory macrophage response. Nature 488, 404–408 (2012).
pubmed: 22842901
pmcid: 4691848
doi: 10.1038/nature11262
Saeed, S. et al. Epigenetic programming of monocyte-to-macrophage differentiation and trained innate immunity. Science 345, 1251086 (2014).
pubmed: 25258085
pmcid: 4242194
doi: 10.1126/science.1251086
Netea, M. G. et al. Trained immunity: A program of innate immune memory in health and disease. Science 352, aaf1098 (2016).
pubmed: 27102489
pmcid: 5087274
doi: 10.1126/science.aaf1098
Menke-van der Houven van Oordt, C. W. et al. First-in-human phase I clinical trial of RG7356, an anti-CD44 humanized antibody, in patients with advanced, CD44-expressing solid tumors. Oncotarget 7, 80046–80058 (2016).
pubmed: 27507056
pmcid: 5346770
doi: 10.18632/oncotarget.11098
Madau, M. et al. A mild and straightforward one-pot hyaluronic acid functionalization through termination of poly-(2-alkyl-2-oxazoline). Polymer 230, 124059 (2021).
doi: 10.1016/j.polymer.2021.124059
Ren, M., Deng, B., Wang, J. Y., Liu, Z. R. & Lin, W. A dual-emission fluorescence-enhanced probe for imaging copper(II) ions in lysosomes. J. Mater. Chem. B 3, 6746–6752 (2015).
pubmed: 32262467
doi: 10.1039/C5TB01184A
Slotta, K. H. & Tschesche, R. Über Biguanide, I.: Zur Konstitution der Schwermetall‐Komplexverbindungen des Biguanids. Ber. dt. chem. Ges. 62, 1390–1398 (1929).
doi: 10.1002/cber.19290620604
Zhu, M., Lu, L., Yang, P. & Jin, X. Bis(1,1-dimethylbiguanido)copper(II) octahydrate. Acta. Cryst. E58, m217–m219 (2002).
Zhou, G. et al. Role of AMP-activated protein kinase in mechanism of metformin action. J. Clin. Invest. 108, 1167–1174 (2001).
pubmed: 11602624
pmcid: 209533
doi: 10.1172/JCI13505
Ge, E. J. et al. Connecting copper and cancer: from transition metal signalling to metalloplasia. Nat. Rev. Cancer 22, 102–113 (2022).
pubmed: 34764459
doi: 10.1038/s41568-021-00417-2
Wang, J. et al. Inhibition of human copper trafficking by a small molecule significantly attenuates cancer cell proliferation. Nat. Chem. 7, 968–979 (2015).
pubmed: 26587712
pmcid: 4725056
doi: 10.1038/nchem.2381
Cui, L. et al. Mitochondrial copper depletion suppresses triple-negative breast cancer in mice. Nat. Biotechnol. 39, 357–367 (2021).
pubmed: 33077961
doi: 10.1038/s41587-020-0707-9
Rodriguez, R., Schreiber, S. L. & Conrad, M. Persister cancer cells: Iron addiction and vulnerability to ferroptosis. Mol. Cell 82, 728–740 (2022).
pubmed: 34965379
doi: 10.1016/j.molcel.2021.12.001
Cañeque, T., Müller, S. & Rodriguez, R. Visualizing biologically active small molecules in cells using click chemistry. Nat. Rev. Chem. 2, 202–215 (2018).
doi: 10.1038/s41570-018-0030-x
Tornoe, C. W., Christensen, C. & Meldal, M. Peptidotriazoles on solid phase: [1,2,3]-triazoles by regiospecific copper(i)-catalyzed 1,3-dipolar cycloadditions of terminal alkynes to azides. J. Org. Chem. 67, 3057–3064 (2002).
pubmed: 11975567
doi: 10.1021/jo011148j
Rostovtsev, V. V., Green, L. G., Fokin, V. V. & Sharpless, K. B. A stepwise huisgen cycloaddition process: copper(I)-catalyzed regioselective “ligation” of azides and terminal alkynes. Angew. Chem. Int. Ed. 41, 2596–2599 (2002).
doi: 10.1002/1521-3773(20020715)41:14<2596::AID-ANIE2596>3.0.CO;2-4
Sletten, E. M. & Bertozzi, C. R. Bioorthogonal chemistry: fishing for selectivity in a sea of functionality. Angew. Chem. Int. Ed. 48, 6974–6998 (2009).
doi: 10.1002/anie.200900942
Tsvetkov, P. et al. Copper induces cell death by targeting lipoylated TCA cycle proteins. Science 375, 1254–1261 (2022).
pubmed: 35298263
pmcid: 9273333
doi: 10.1126/science.abf0529
Wang, L. et al. Fluorescence imaging mitochondrial copper(II) via photocontrollable fluorogenic probe in live cells. Chinese Chem. Lett. 28, 1965–1968 (2017).
doi: 10.1016/j.cclet.2017.07.016
Chan, P. C. & Kesner, L. Copper (II) complex-catalyzed oxidation of NADH by hydrogen peroxide. Biol. Trace Elem. Res. 2, 159–174 (1980).
pubmed: 24271266
doi: 10.1007/BF02785352
Robbins, M. H. & Drago, R. S. Activation of hydrogen peroxide for oxidation by copper(II) complexes. J. Cat. 170, 295–303 (1997).
doi: 10.1006/jcat.1997.1754
Dai, Z., Ramesh, V. & Locasale, J. W. The evolving metabolic landscape of chromatin biology and epigenetics. Nat. Rev. Genet. 21, 737–753 (2020).
pubmed: 32908249
pmcid: 8059378
doi: 10.1038/s41576-020-0270-8
Chua, R. L. et al. COVID-19 severity correlates with airway epithelium-immune cell interactions identified by single-cell analysis. Nat. Biotechnol. 38, 970–979 (2020).
pubmed: 32591762
doi: 10.1038/s41587-020-0602-4
Pai, A. A. et al. Widespread shortening of 3′ untranslated regions and increased exon inclusion are evolutionarily conserved features of innate immune responses to infection. PLoS Genet. 12, e1006338 (2016).
pubmed: 27690314
pmcid: 5045211
doi: 10.1371/journal.pgen.1006338
Fernandes, M. C. et al. Dual transcriptome profiling of Leishmania-infected human macrophages reveals distinct reprogramming signatures. mBio 7, e00027-16 (2016).
pubmed: 27165796
pmcid: 4959658
doi: 10.1128/mBio.00027-16
Gonçalves, S. M. et al. Phagosomal removal of fungal melanin reprograms macrophage metabolism to promote antifungal immunity. Nat. Commun. 11, 2282 (2020).
pubmed: 32385235
pmcid: 7210971
doi: 10.1038/s41467-020-16120-z
Hoffmann, J. A., Kafatos, F. C., Janeway, C. A. & Ezekowitz, R. A. Phylogenetic perspectives in innate immunity. Science 284, 1313–1318 (1999).
pubmed: 10334979
doi: 10.1126/science.284.5418.1313
Buras, J. A., Holzmann, B. & Sitkovsky, M. Animal models of sepsis: setting the stage. Nat. Rev. Drug Discov. 4, 854–865 (2005).
pubmed: 16224456
doi: 10.1038/nrd1854
Kulkarni, A. S., Gubbi, S. & Barzilai, N. Benefits of metformin in attenuating the hallmarks of aging. Cell Metab. 32, 15–30 (2020).
pubmed: 32333835
pmcid: 7347426
doi: 10.1016/j.cmet.2020.04.001
Bharath, L. P. et al. Metformin enhances autophagy and normalizes mitochondrial function to alleviate aging-associated inflammation. Cell Metab. 32, 44–55.e6 (2020).
pubmed: 32402267
pmcid: 7217133
doi: 10.1016/j.cmet.2020.04.015
Messaoudii, C., Boudier, T., Sanchez Sorzano, C. O. & Marco, S. TomoJ: tomography software for three-dimensional reconstruction in transmission electron microscopy. BMC Bioinf. 8, 288–296 (2007).
doi: 10.1186/1471-2105-8-288
Lechene, C. et al. High-resolution quantitative imaging of mammalian and bacterial cells using stable isotope mass spectrometry. J. Biol. 5, 20 (2006).
pubmed: 17010211
pmcid: 1781526
doi: 10.1186/jbiol42
Gräber, M. et al. Oral disinfectants inhibit protein-protein interactions mediated by the anti-apoptotic protein Bcl-xL and induce apoptosis in human oral tumor cells. Angew. Chem. Int. Ed. Engl. 52, 4487–4491 (2013).
pubmed: 23512547
doi: 10.1002/anie.201208889
Giguère, J.-B. et al. Synthesis of [2]-and [3] rotaxanes through Sonogashira coupling. Tetrahedron Lett. 50, 5497–5500 (2009).
doi: 10.1016/j.tetlet.2009.07.101
Valle, F. D. & Romeo, A. Esters of hyaluronic acid. US patent US-4851521-A (1986).
Zhu, M., Lu, L., Yang, P. & Jin, X. Bis(1,1-dimethylbiguanido)copper(II) octahydrate. Acta Crystallogr. E 58, m217–m219 (2002).
doi: 10.1107/S1600536802007092
Allouche, A.-R. Gabedit-A graphical user interface for computational chemistry software. J. Comput. Chem. 32, 174–182 (2011).
pubmed: 20607691
doi: 10.1002/jcc.21600
Lindorff-Larsen, K. et al. Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 78, 1950–1958 (2010).
pubmed: 20408171
pmcid: 2970904
doi: 10.1002/prot.22711
Stewart, J. J. Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and re-optimization of parameters. J. Mol. Model. 19, 1–32 (2013).
pubmed: 23187683
doi: 10.1007/s00894-012-1667-x
Klamt, A. & Schüürmann, G. COSMO: a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient. J. Chem. Soc. Perkin. Trans. 2, 799–805 (1993).
doi: 10.1039/P29930000799
Neese, F. Software update: the ORCA program system, version 4.0. WIREs Comput. Mol. Sci. 8, e1327 (2018).
doi: 10.1002/wcms.1327
Stephens, P. J., Devlin, F. J., Chabalowski, C. F. & Frisch, M. J. Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields. J. Phys. Chem. 98, 11623–11627 (1994).
doi: 10.1021/j100096a001
Zhao, Y. & Truhlar, D. G. The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals. Theor. Chem. Acc. 120, 215–241 (2007).
doi: 10.1007/s00214-007-0310-x
Becke, A. D. A new mixing of Hartree–Fock and local density‐functional theories. J. Chem. Phys. 98, 1372–1377 (1993).
doi: 10.1063/1.464304
Lee, C., Yang, W. & Parr, R. G. Development of the Colle–Salvetti correlation-energy formula into a functional of the electron density. Phys. Rev. B 37, 785–789 (1988).
doi: 10.1103/PhysRevB.37.785
Weigend, F. & Ahlrichs, R. Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: design and assessment of accuracy. Phys. Chem. Chem. Phys. 7, 3297 (2005).
pubmed: 16240044
doi: 10.1039/b508541a
Grimme, S., Ehrlich, S. & Goerigk, L. Effect of the damping function in dispersion corrected density functional theory. J. Comput. Chem. 32, 1456–1465 (2011).
pubmed: 21370243
doi: 10.1002/jcc.21759
Galván-García, E. A., Agacino-Valdés, E., Franco-Pérez, M. & Gómez-Balderas, R. [Cu(H
doi: 10.1007/s00214-017-2056-4
Schäfer, A., Horn, H. & Ahlrichs, R. Fully optimized contracted gaussian basis sets for atoms Li to Kr. J. Chem. Phys. 97, 2571–2577 (1992).
doi: 10.1063/1.463096
Eichkorn, K., Weigend, F., Treutler, O. & Ahlrichs, R. Auxiliary basis sets for main row atoms and transition metals and their use to approximate Coulomb potentials. Theor. Chem. Acc. 97, 119–124 (1997).
doi: 10.1007/s002140050244
Marenich, A. V., Cramer, C. J. & Truhlar, D. G. Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions. J. Phys. Chem. B 113, 6378–6396 (2009).
pubmed: 19366259
doi: 10.1021/jp810292n
Ribeiro, R. F., Marenich, A. V., Cramer, C. J. & Truhlar, D. G. Use of solution-phase vibrational frequencies in continuum models for the free energy of solvation. J. Phys. Chem. B 115, 14556–14562 (2011).
pubmed: 21875126
doi: 10.1021/jp205508z
Viltard, M. et al. The metabolomic signature of extreme longevity: naked mole rats versus mice. Aging 11, 4783–4800 (2019).
pubmed: 31346149
pmcid: 6682510
doi: 10.18632/aging.102116
Poullet, P., Carpentier, S. & Barillot, E. myProMS, a web server for management and validation of mass spectrometry-based proteomic data. Proteomics 7, 2553–2556 (2007).
pubmed: 17610305
doi: 10.1002/pmic.200600784
The, M., MacCoss, M. J., Noble, W. S. & Kall, L. Fast and accurate protein false discovery rates on large-scale proteomics data sets with Percolator 3.0. J. Am. Soc. Mass. Spectrom. 27, 1719–1727 (2016).
pubmed: 27572102
pmcid: 5059416
doi: 10.1007/s13361-016-1460-7
Valot, B., Langella, O., Nano, E. & Zivy, M. MassChroQ: a versatile tool for mass spectrometry quantification. Proteomics 11, 3572–3577 (2011).
pubmed: 21751374
doi: 10.1002/pmic.201100120
Liao, M. et al. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat. Med. 26, 842–844 (2020).
pubmed: 32398875
doi: 10.1038/s41591-020-0901-9
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
pubmed: 19910308
doi: 10.1093/bioinformatics/btp616
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
pubmed: 25605792
pmcid: 4402510
doi: 10.1093/nar/gkv007
Perez-Riverol, Y. et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 50, D543–D552 (2022).
pubmed: 34723319
doi: 10.1093/nar/gkab1038