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, 11 (1), 431

Environmental Arginine Controls Multinuclear Giant Cell Metabolism and Formation

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Environmental Arginine Controls Multinuclear Giant Cell Metabolism and Formation

Julia S Brunner et al. Nat Commun.

Abstract

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.

Conflict of interest statement

The authors declare the following competing interests: P.C. is the founder of Biocancer Treatment International Ltd. P.C., G.S. and S.B. are listed as inventors on a patent (US9789169B2) covering recArg1/BCT-100. The remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Recombinant arginase 1 (recArg1) improves outcome in diverse murine arthritis models and arginase 1 is elevated in erosive RA patients.
a, b Paw histology, osteoclast numbers per hind paw (N. Oc), total scores, weight and histology inflammation area of mice suffering from serum transfer arthritis (a K/BxN, NaCl n = 12, recArg1 n = 13 animals) or the hTNFtg/+ mouse model (b NaCl n = 13, recArg1 n = 14 animals). Scale bar represents 1 mm. c Ctsk IVIS and quantification, total scores and weights of mice suffering from collagen-induced arthritis (CIA, NaCl n = 13, recArg1 n = 14 animals, Ctsk IVIS n = 6 animals). d RecArg1 depletes serum arginine; K/BxN (n = 13 animals per group), CIA (NaCl n = 13, recArg1 n = 14 animals). NaCl in ad represents saline vehicle control group. e Serum crosslaps in the indicated patient groups (erosive RA n = 30, non-erosive RA n = 29 patients). f Serum arginine levels of all patient groups (healthy n = 19, erosive RA n = 29, non-erosive RA n = 30 patients). g Arginase 1 levels in all patient groups and correlation between arginase 1 and erosion score of patients suffering from erosive RA (healthy n = 19, erosive RA n = 29, non-erosive RA n = 30 patients). Data are mean ± SEM, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, unpaired t-test (ac, eg), one-way ANOVA (d) and two-way ANOVA post-hoc pairwise comparisons with Bonferroni correction (ac), linear regression (g). Source data are provided as a Source Data file.
Fig. 2
Fig. 2. Extracellular arginine is essential for RANKL-induced osteoclastogenesis.
a Schematic of systems-wide approaches performed to understand the effect of recArg1 on RANKL dependent osteoclastogenesis. b KEGG metabolic enrichment of differentially expressed genes upon RANKL treatment. c Representative TRAP stainings depicting that recArg1 abolishes murine and human RANKL-induced osteoclastogenesis. d Quantification of murine data depicted in c (n = 6). e qRT-PCR time course of Nfatc1 and Fos (n = 3). f Quantification of viable cells 24 h post RANKL/recArg1 treatment (n = 4). g, h Osteoclastogenesis is unchanged by Arg1 deficiency. TRAP stainings (g) and Western blots of ARG1 in preosteoclasts (Pre-OC) and osteoclasts (OC) (h). i qRT-PCR time course of Arg1 (n = 3). Data are mean ± SEM, *P < 0.05, ***P < 0.001, ****P < 0.0001, one-way ANOVA (d) and two-way ANOVA post-hoc pairwise comparisons with Bonferroni correction (e). Scale bar represents 200 µm (c, g). Source data are provided as a Source Data file.
Fig. 3
Fig. 3. Arginine presence specifically sustains RANKL gene and protein expression.
a Workflow dissecting recArg1 specificity on RANKL signalling. Arg-sufficient (pink) and Arg-deficient (grey) conditions in arginine sufficient (aMEM) or deficient media (Arg-Free) depicted. b MDS of transcriptomic datasets in a, keeping all genes expressed in at least three of the samples. c Linear regression of average gene expression (n = 4) for RANKL/Arg-Depletion against RANKL/Arg-Starvation. d Heatmap of proteomics data in a, showing Z-score of abundance level per protein across all conditions (RANKL-conditions shown only, full heatmap in Supplementary Fig. 5d). e Volcano plot showing difference between M-CSF/Arg-Rescue versus RANKL/Arg-Rescue (n = 4). Highlighted are proteins modulated by RANKL in an arginine-dependent manner. f Volcano plot showing difference between RANKL/Arg-Rescue versus RANKL/Arg-Depletion (n = 4). Highlighted are proteins uniquely influenced by recArg1.
Fig. 4
Fig. 4. Dynamic changes in metabolism to decreased extracellular arginine during RANKL-dependent osteoclastogenesis occur mTOR independently.
a RecArg1 exerts negligible effects on RANKL-mediated mTOR-related pathways. b Deficiency in mTOR/AA-starvation components cannot rescue effects of Arg-Depletion. Scale bar represents 200 µm. ce Oxygen consumption rate (OCR), spare respiratory capacity (SRC) and basal glycolytic rate (ECAR) of preosteoclasts ± recArg1 (c, d n = 8). Negligible effects of arginine deprivation on preosteoclast 2-NBDG (glucose) uptake (e n = 4). All data 48 h post stimulation. f RANKL differentially changed metabolites intra-/extracellularly ± recArg1. Data normalized against M-CSF (left) and represent mean (n = 4, RANKL intracellular n = 3; see Supplementary Fig. 7c, d) g Transcriptional profiles of selected KEGG pathways. TCA cycle and serine/purine biosynthetic enzymes indicated. Data represent mean (n = 4). Significance levels according to empirical permutation test, empirical P-values reported. Data are mean ± SEM, *P < 0.05, ****P < 0.0001, one-way ANOVA post-hoc pairwise comparisons with Bonferroni correction (c–e) and two-way ANOVA post Sidak’s multiple comparisons test (c). Source data are provided as a Source Data file.
Fig. 5
Fig. 5. Metabolic tracing of arginine and aspartate reveal arginine withdrawal instigates a dysregulated TCA cycle.
a Schematic of the fate of the 13C-labelled atoms deriving from arginine (pink) or aspartate (turquoise). b Ratio of intracellular 13C6-labelled arginine to unlabelled 12C6 arginine pool and percent m + 5 citrulline and m + 5 arginine normalized to the labelled 13C6 arginine pool (n = 4). c Intracellular m + 5 ornithine, m + 4 putrescine and m + 4 spermidine normalized to 13C6-labelled arginine input (n = 4). d Excess of recArg1 degradation products ornithine and urea do not impact osteoclastogenesis (n = 6). e Effect of extracellular polyamines and polyamine synthesis inhibitor difluoromethylornithine (DFMO) on osteoclastogenesis (n = 4). f Intracellular m + 4 fumarate and m + 4 malate normalized to 13C4-labelled aspartate input (n = 4). Data are mean ± SEM, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, one-way ANOVA post-hoc pairwise comparisons with Bonferroni correction (bf). Source data are provided as a Source Data file.
Fig. 6
Fig. 6. Intermediates or precursors of amino acids including arginine can compensate for amino acid absence in osteoclastogenesis.
a Urea cycle metabolites can/cannot rescue arginine requirements in osteoclastogenesis in Arg-Free media (n = 4). b Schematic demonstrating which amino acids are essential (white) versus non-essential (pink) for osteoclastogenesis. Star relates to TRAP-positivity with decreased occurrence of MGCs (n = 4). c Immediate intermediates of leucine, isoleucine and phenylalanine degradation compensate for the lack of these amino acids and rescue osteoclastogenesis. Representative TRAP stainings are shown (n = 4). aKIC alpha-ketoisocaproate, Ile isoleucine, KIle ketoisoleucine, Leu leucine, Phe phenylalanine, PP phenylpyruvate. Scale bar represents 200 µm. Data are mean ± SEM. Source data are provided as a Source Data file.
Fig. 7
Fig. 7. Osteoclasts and IL-4-induced multinucleated giant cells (MGCs) share mechanisms of metabolic adaptation in nutrient scarcity.
a Representative H&E stainings of MGCs ± recArg1. b Representative H&E stainings of MGCs from Arg1Tie/+ and WT littermate controls. c qRT-PCR of Pck2, Sdhd and Dcstamp in IL-4- (M-CSF + IL-4) and RANKL (M-CSF + RANKL)-induced MGCs ± recArg1. All data correspond to day 7, # corresponds to P = 0.01 assessed by t-test (n = 4). d, e Representative H&E stainings (d) and quantifications (e) of nuclei per cell of IL-4-generated MGCs in the absence of arginine (Arg-Free Media), re-supplemented with arginine, citrulline or argininosuccinate. Data represent cells counted in 50 random frames (n = 50). f IL-4-induced MGC oxygen consumption rate (OCR) in the absence of arginine (Arg-Free Media), re-supplemented with arginine, citrulline and argininosuccinate. Significance was calculated by unpaired t-test between area under the curve (AUC) of indicated conditions (Arg-Free n = 10, arginine n = 12, citrulline n = 11, argininosuccinate n = 11). Data are mean ± SEM, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, one-way ANOVA Tukey post-hoc test and t-test (c), one-way ANOVA post-hoc pairwise comparisons with Bonferroni correction (e) and two-way ANOVA post Sidak’s multiple comparisons test (f). Scale bar represents 200 µm (10×), 50 µm (40×) (a, d) and 200 µm (RANKL) and 100 µm (IL-4) (b). Source data are provided as a Source Data file.

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