Environmental arginine controls multinuclear giant cell metabolism and formation.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
22 01 2020
22 01 2020
Historique:
received:
22
03
2019
accepted:
16
12
2019
entrez:
24
1
2020
pubmed:
24
1
2020
medline:
14
4
2020
Statut:
epublish
Résumé
Multinucleated giant cells (MGCs) are implicated in many diseases including schistosomiasis, sarcoidosis and arthritis. MGC generation is energy intensive to enforce membrane fusion and cytoplasmic expansion. Using receptor activator of nuclear factor kappa-Β ligand (RANKL) induced osteoclastogenesis to model MGC formation, here we report RANKL cellular programming requires extracellular arginine. Systemic arginine restriction improves outcome in multiple murine arthritis models and its removal induces preosteoclast metabolic quiescence, associated with impaired tricarboxylic acid (TCA) cycle function and metabolite induction. Effects of arginine deprivation on osteoclastogenesis are independent of mTORC1 activity or global transcriptional and translational inhibition. Arginine scarcity also dampens generation of IL-4 induced MGCs. Strikingly, in extracellular arginine absence, both cell types display flexibility as their formation can be restored with select arginine precursors. These data establish how environmental amino acids control the metabolic fate of polykaryons and suggest metabolic ways to manipulate MGC-associated pathologies and bone remodelling.
Identifiants
pubmed: 31969567
doi: 10.1038/s41467-020-14285-1
pii: 10.1038/s41467-020-14285-1
pmc: PMC6976629
doi:
Substances chimiques
RANK Ligand
0
Interleukin-4
207137-56-2
Arginine
94ZLA3W45F
Mechanistic Target of Rapamycin Complex 1
EC 2.7.11.1
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
431Subventions
Organisme : Austrian Science Fund FWF
ID : P 30026
Pays : Austria
Commentaires et corrections
Type : CommentIn
Références
McInnes, I. B. & Schett, G. The pathogenesis of rheumatoid arthritis. N. Engl. J. Med. 365, 2205–2219 (2011).
pubmed: 22150039
doi: 10.1056/NEJMra1004965
Kong, Y. Y. et al. OPGL is a key regulator of osteoclastogenesis, lymphocyte development and lymph-node organogenesis. Nature 397, 315–323 (1999).
pubmed: 9950424
doi: 10.1038/16852
Lavin, Y. et al. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159, 1312–1326 (2014).
pubmed: 25480296
pmcid: 25480296
doi: 10.1016/j.cell.2014.11.018
O’Neill, L. A. & Pearce, E. J. Immunometabolism governs dendritic cell and macrophage function. J. Exp. Med. 213, 15–23 (2016).
pubmed: 26694970
pmcid: 4710204
doi: 10.1084/jem.20151570
Geiger, R. et al. L-arginine modulates T cell metabolism and enhances survival and anti-tumor activity. Cell 167, 829–842.e813 (2016).
pubmed: 27745970
pmcid: 5075284
doi: 10.1016/j.cell.2016.09.031
Murray, P. J. Amino acid auxotrophy as a system of immunological control nodes. Nat. Immunol. 17, 132 (2016).
pubmed: 26784254
pmcid: 4893777
doi: 10.1038/ni.3323
Caputa, G., Castoldi, A. & Pearce, E. J. Metabolic adaptations of tissue-resident immune cells. Nat. Immunol. 20, 793–801 (2019).
pubmed: 31213715
doi: 10.1038/s41590-019-0407-0
pmcid: 31213715
Vincent, E. E. et al. Mitochondrial phosphoenolpyruvate carboxykinase regulates metabolic adaptation and enables glucose-independent tumor growth. Mol. Cell 60, 195–207 (2015).
pubmed: 26474064
doi: 10.1016/j.molcel.2015.08.013
pmcid: 26474064
Ali, U. et al. L-asparaginase as a critical component to combat Acute Lymphoblastic Leukaemia (ALL): a novel approach to target ALL. Eur. J. Pharmacol. 771, 199–210 (2016).
pubmed: 26698391
doi: 10.1016/j.ejphar.2015.12.023
pmcid: 26698391
Yau, T. et al. Preliminary efficacy, safety, pharmacokinetics, pharmacodynamics and quality of life study of pegylated recombinant human arginase 1 in patients with advanced hepatocellular carcinoma. Invest. New Drugs 33, 496–504 (2015).
pubmed: 25666409
doi: 10.1007/s10637-014-0200-8
pmcid: 25666409
Qiu, F., Huang, J. & Sui, M. Targeting arginine metabolism pathway to treat arginine-dependent cancers. Cancer Lett. 364, 1–7 (2015).
pubmed: 25917076
doi: 10.1016/j.canlet.2015.04.020
pmcid: 25917076
Ditzel, H. J. The K/BxN mouse: a model of human inflammatory arthritis. Trends Mol. Med. 10, 40–45 (2004).
pubmed: 14720585
doi: 10.1016/j.molmed.2003.11.004
pmcid: 14720585
Keffer, J. et al. Transgenic mice expressing human tumour necrosis factor: a predictive genetic model of arthritis. EMBO J. 10, 4025–4031 (1991).
pubmed: 1721867
pmcid: 453150
doi: 10.1002/j.1460-2075.1991.tb04978.x
Courtenay, J. S., Dallman, M. J., Dayan, A. D., Martin, A. & Mosedale, B. Immunisation against heterologous type II collagen induces arthritis in mice. Nature 283, 666–668 (1980).
pubmed: 6153460
doi: 10.1038/283666a0
pmcid: 6153460
Morris, S. M. Jr. Arginine metabolism: boundaries of our knowledge. J. Nutr. 137, 1602s–1609s (2007).
pubmed: 17513435
doi: 10.1093/jn/137.6.1602S
pmcid: 17513435
Maruyama, K. et al. The transcription factor Jdp2 controls bone homeostasis and antibacterial immunity by regulating osteoclast and neutrophil differentiation. Immunity 37, 1024–1036 (2012).
pubmed: 23200825
doi: 10.1016/j.immuni.2012.08.022
pmcid: 23200825
El Kasmi, K. C. et al. Toll-like receptor-induced arginase 1 in macrophages thwarts effective immunity against intracellular pathogens. Nat. Immunol. 9, 1399–1406 (2008).
pubmed: 18978793
pmcid: 2584974
doi: 10.1038/ni.1671
Wang, S. et al. The amino acid transporter SLC38A9 is a key component of a lysosomal membrane complex that signals arginine sufficiency to mTORC1. Science 347, 188–194 (2015).
pubmed: 25567906
pmcid: 4295826
doi: 10.1126/science.1257132
Weichhart, T., Hengstschläger, M. & Linke, M. Regulation of innate immune cell function by mTOR. Nat. Rev. Immunol. 15, 599–614 (2015).
pubmed: 26403194
pmcid: 6095456
doi: 10.1038/nri3901
Rebsamen, M. et al. SLC38A9 is a component of the lysosomal amino acid sensing machinery that controls mTORC1. Nature 519, 477–481 (2015).
pubmed: 25561175
pmcid: 4376665
doi: 10.1038/nature14107
Pakos-Zebrucka, K. et al. The integrated stress response. EMBO Rep. 17, 1374–1395 (2016).
pubmed: 27629041
pmcid: 5048378
doi: 10.15252/embr.201642195
Zeng, R., Faccio, R. & Novack, D. V. Alternative NF-kappaB regulates RANKL-induced osteoclast differentiation and mitochondrial biogenesis via independent mechanisms. J. Bone Miner. Res. 30, 2287–2299 (2015).
pubmed: 26094846
pmcid: 4834842
doi: 10.1002/jbmr.2584
Ishii, K. A. et al. Coordination of PGC-1beta and iron uptake in mitochondrial biogenesis and osteoclast activation. Nat. Med. 15, 259–266 (2009).
pubmed: 19252502
doi: 10.1038/nm.1910
pmcid: 19252502
Jha, A. K. et al. Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity 42, 419–430 (2015).
pubmed: 25786174
doi: 10.1016/j.immuni.2015.02.005
pmcid: 25786174
Shambaugh, G. E. 3rd Urea biosynthesis I. The urea cycle and relationships to the citric acid cycle. Am. J. Clin. Nutr. 30, 2083–2087 (1977).
pubmed: 337792
doi: 10.1093/ajcn/30.12.2083
pmcid: 337792
Broer, S. & Broer, A. Amino acid homeostasis and signalling in mammalian cells and organisms. Biochem. J. 474, 1935–1963 (2017).
pubmed: 28546457
pmcid: 5444488
doi: 10.1042/BCJ20160822
Qualls, J. E. et al. Sustained generation of nitric oxide and control of mycobacterial infection requires argininosuccinate synthase 1. Cell Host Microbe 12, 313–323 (2012).
pubmed: 22980328
pmcid: 3444824
doi: 10.1016/j.chom.2012.07.012
Yeon, J. T. et al. Natural polyamines inhibit the migration of preosteoclasts by attenuating Ca2+-PYK2-Src-NFATc1 signaling pathways. Amino Acids 46, 2605–2614 (2014).
pubmed: 25216923
doi: 10.1007/s00726-014-1797-9
pmcid: 25216923
Metcalf, B. W. et al. Catalytic irreversible inhibition of mammalian ornithine decarboxylase (E C 4 1 1 17) by substrate and product analogs. J. Am. Chem. Soc. 100, 2551–2553 (1978).
doi: 10.1021/ja00476a050
Fletcher, M. et al. l-Arginine depletion blunts antitumor T-cell responses by inducing myeloid-derived suppressor cells. Cancer Res. 75, 275–283 (2014).
pubmed: 25406192
pmcid: 4297565
doi: 10.1158/0008-5472.CAN-14-1491
Chen, C. et al. TSC-mTOR maintains quiescence and function of hematopoietic stem cells by repressing mitochondrial biogenesis and reactive oxygen species. J. Exp. Med. 205, 2397–2408 (2008).
pubmed: 18809716
pmcid: 2556783
doi: 10.1084/jem.20081297
Buck, M. D., Sowell, R. T., Kaech, S. M. & Pearce, E. L. Metabolic Instruction of Immunity. Cell 169, 570–586 (2017).
pubmed: 28475890
pmcid: 5648021
doi: 10.1016/j.cell.2017.04.004
Manoli, I. & Venditti, C. P. Disorders of branched chain amino acid metabolism. Transl. Sci. Rare Dis. 1, 91–110 (2016).
pubmed: 29152456
pmcid: 5685199
Milde, R. et al. Multinucleated giant cells are specialized for complement-mediated phagocytosis and large target destruction. Cell Rep. 13, 1937–1948 (2015).
pubmed: 26628365
pmcid: 4675895
doi: 10.1016/j.celrep.2015.10.065
Pereira, M. et al. Common signalling pathways in macrophage and osteoclast multinucleation. J. Cell Sci. 131, jcs216267 (2018).
pubmed: 29871956
doi: 10.1242/jcs.216267
pmcid: 29871956
Yeon, J. T., Choi, S. W. & Kim, S. H. Arginase 1 is a negative regulator of osteoclast differentiation. Amino Acids 48, 559–565 (2016).
pubmed: 26475291
doi: 10.1007/s00726-015-2112-0
pmcid: 26475291
Cejka, D. et al. Mammalian target of rapamycin signaling is crucial for joint destruction in experimental arthritis and is activated in osteoclasts from patients with rheumatoid arthritis. Arthritis Rheumatol. 62, 2294–2302 (2010).
doi: 10.1002/art.27504
Huynh, H. & Wan, Y. mTORC1 impedes osteoclast differentiation via calcineurin and NFATc1. Commun. Biol. 1, 29 (2018).
pubmed: 30271915
pmcid: 6123628
doi: 10.1038/s42003-018-0028-4
Salahudeen, A. A. et al. An E3 ligase possessing an iron-responsive hemerythrin domain is a regulator of iron homeostasis. Science 326, 722–726 (2009).
pubmed: 19762597
pmcid: 3582197
doi: 10.1126/science.1176326
Lendahl, U., Lee, K. L., Yang, H. & Poellinger, L. Generating specificity and diversity in the transcriptional response to hypoxia. Nat. Rev. Genet. 10, 821–832 (2009).
pubmed: 19884889
doi: 10.1038/nrg2665
pmcid: 19884889
Haslacher, H et al. Usage Data and Scientific Impact of the Prospectively Established Fluid Bioresources at the Hospital-Based MedUni Wien Biobank. Biopreservation and biobanking 16, 477–482 (2018).
pmcid: 6308288
doi: 10.1089/bio.2018.0032
Bluml, S. et al. Essential role of microRNA-155 in the pathogenesis of autoimmune arthritis in mice. Arthritis Rheumatol. 63, 1281–1288 (2011).
doi: 10.1002/art.30281
Bluml, S. et al. Loss of phosphatase and tensin homolog (PTEN) in myeloid cells controls inflammatory bone destruction by regulating the osteoclastogenic potential of myeloid cells. Ann. Rheum. Dis. 74, 227–233 (2015).
pubmed: 24078675
doi: 10.1136/annrheumdis-2013-203486
pmcid: 24078675
Van de Velde, L. A. et al. Stress kinase GCN2 controls the proliferative fitness and trafficking of cytotoxic T cells independent of environmental amino acid sensing. Cell Rep. 17, 2247–2258 (2016).
pubmed: 27880901
pmcid: 5131879
doi: 10.1016/j.celrep.2016.10.079
Copp, J., Manning, G. & Hunter, T. TORC-specific phosphorylation of mTOR: phospho-Ser2481 is a marker for intact mTORC2. Cancer Res. 69, 1821–1827 (2009).
pubmed: 19244117
pmcid: 2652681
doi: 10.1158/0008-5472.CAN-08-3014
Linke, M. et al. Chronic signaling via the metabolic checkpoint kinase mTORC1 induces macrophage granuloma formation and marks sarcoidosis progression. Nat. Immunol. 18, 293–302 (2017).
pubmed: 28092373
pmcid: 5321578
doi: 10.1038/ni.3655
Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).
pubmed: 17076895
pmcid: 1794559
doi: 10.1186/gb-2006-7-10-r100
Galili, T., O’Callaghan, A., Sidi, J. & Sievert, C. heatmaply: an R package for creating interactive cluster heatmaps for online publishing. Bioinformatics 34, 1600–1602 (2018).
pubmed: 29069305
doi: 10.1093/bioinformatics/btx657
pmcid: 29069305
Tuncbag, N. et al. Network-based interpretation of diverse high-throughput datasets through the omics integrator software package. PLoS Comput. Biol. 12, e1004879 (2016).
pubmed: 27096930
pmcid: 4838263
doi: 10.1371/journal.pcbi.1004879
Rosvall, M. & Bergstrom, C. T. Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems. PLoS ONE 6, e18209 (2011).
pubmed: 21494658
pmcid: 3072965
doi: 10.1371/journal.pone.0018209
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
pubmed: 14597658
pmcid: 403769
doi: 10.1101/gr.1239303
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
pubmed: 23618408
pmcid: 23618408
doi: 10.1186/gb-2013-14-4-r36
Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).
pubmed: 23558742
pmcid: 3664803
doi: 10.1093/nar/gkt214
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
pubmed: 24485249
pmcid: 4053721
doi: 10.1186/gb-2014-15-2-r29
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: 25605792
doi: 10.1093/nar/gkv007
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).
doi: 10.1093/bioinformatics/btp616
pubmed: 19910308
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
pubmed: 22455463
pmcid: 3339379
doi: 10.1089/omi.2011.0118
Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).
pubmed: 10802651
pmcid: 3037419
doi: 10.1038/75556
Consortium, T. G. O. Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res. 45, D331–D338 (2017).
Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
pubmed: 10592173
pmcid: 10592173
doi: 10.1093/nar/28.1.27
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).
Kelstrup, C. D. et al. Performance evaluation of the Q exactive HF-X for shotgun proteomics. J. Proteome Res. 17, 727–738 (2018).
pubmed: 29183128
doi: 10.1021/acs.jproteome.7b00602
pmcid: 29183128
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
pubmed: 19029910
pmcid: 19029910
doi: 10.1038/nbt.1511
Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794–1805 (2011).
pubmed: 21254760
doi: 10.1021/pr101065j
pmcid: 21254760
Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).
pubmed: 27348712
doi: 10.1038/nmeth.3901
pmcid: 27348712
Chatr-Aryamontri, A. et al. The BioGRID interaction database: 2017 update. Nucleic Acids Res. 45, D369–D379 (2017).
pubmed: 27980099
pmcid: 5210573
doi: 10.1093/nar/gkw1102
Bohlin L., Edler D., Lancichinetti A., Rosvall M. Community Detection and Visualization of Networks with the Map Equation Framework. In: Ding Y., Rousseau R., Wolfram D. (eds) Measuring Scholarly Impact. Springer, Cham 3–34 (2014)
Vizcaino, J. A. et al. 2016 update of the PRIDE database and its related tools. Nucleic Acids Res. 44, 11033 (2016).
pubmed: 27683222
pmcid: 5159556
doi: 10.1093/nar/gkw880