Abstract
Glucocorticoids display remarkable anti-inflammatory activity, but their use is limited by on-target adverse effects including insulin resistance and skeletal muscle atrophy. We used a chemical systems biology approach, ligand class analysis, to examine ligands designed to modulate glucocorticoid receptor activity through distinct structural mechanisms. These ligands displayed diverse activity profiles, providing the variance required to identify target genes and coregulator interactions that were highly predictive of their effects on myocyte glucose disposal and protein balance. Their anti-inflammatory effects were linked to glucose disposal but not muscle atrophy. This approach also predicted selective modulation in vivo, identifying compounds that were muscle-sparing or anabolic for protein balance and mitochondrial potential. Ligand class analysis defined the mechanistic links between the ligand–receptor interface and ligand-driven physiological outcomes, a general approach that can be applied to any ligand-regulated allosteric signaling system.
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Data availability
The raw and processed nascent RNA sequencing dataset generated in this study has been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO), with the accession code GSE149453. Other messenger RNA sequencing data cited in this work was downloaded from the GEO with accession code GSE12463654. Sample R scripts used are available at https://github.com/jnwachuk/ML_in_GR_signaling_networks. Compound profiling assay data, results of correlation and machine learning analyses are in Supplemental_Data1.xls.
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Acknowledgements
N.E.B. was supported by the BallenIsles Men’s Golf Association. K.W.N. was supported by the G.S. Humane Corporation. A W.M. Keck Foundation grant was awarded to E.A.O. D.J.K. was supported by National Institutes of Health (NIH) grant no. DK124870. T.I. is supported by grants from the NIH, the Department of Defense, the National Science Foundation and by start-up funds provided to The Scripps Research Institute from the State of Florida.
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N.E.B. and K.W.N. designed the research. N.E.B., K.W.N., S.V.B., J.C.N., G.L.H., M.D.C. and R.H. designed and developed the methods. N.E.B., S.S., S.V.B., J.C.N., C.C.N., J.N., D.A.S., R.H., D.J.K., X.L., O.E., Z.J. and T.M.K. performed the experiments under the supervision of K.W.N., N.E.B., G.L.H., T.M.K., E.A.O., D.G.P., and T.I. M.D.C. performed the mouse pharmacokinetics study. Z.J. and T.M.K. provided the compounds. K.W.N., N.E.B., J.C.N., X.L., E.A.O., D.J.K. and E.A.O. wrote the manuscript.
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Extended data
Extended Data Fig. 1 Structure-based design of GR ligands.
a) Chemical structure of the selective GR modulator, PF802. b) Crystal structure of the dexamethasone (Dex) bound GR ligand-binding domain (LBD). Helix 12 is colored red and the NCOA2 coregulator peptide binding in the AF2 binding surface is colored coral. Carbons-3, -11, and -17 are indicated in the chemical structure (pdb entry 1M2Z). c) The bulky dimethylanaline group attached at C11 in RU-486 displaces h12 from the agonist position to disrupt the AF2 surface and generate antagonism (pdb entry 1NHZ). d) Substitutions at C3, as seen with the furoate group in momethasone furoate or the propyne in RU-486 target a small internal pocket to increase affinity (pdb entry 4P6W). e) Substitutions at C3 of the steroidal A-ring enter the solvent channel underneath the AF2 surface, potentially changing the shape of the surface and the ensemble of interacting coregulators (pdb entry 3BQD). f) Model of SR11466 (15) bound to the GR LBD.
Extended Data Fig. 2 Quantitative phenotyping assays for GC action in skeletal muscle.
a-e) Myotubes were nutrient-deprived, pre-treated with DMSO, RU-486, or Dex, and treated with insulin as outlined in Methods. a) Effect of insulin on pAKT levels in C2C12 myotubes were compared by In-Cell Western assay (ICW) 48 h after treatment with RU-486 or Dex. b) Quantitation of pAKT in C2C12 myotubes compared by ICW. Bars represent the mean; n = 3, except for DMSO where n = 36 biologically independent samples. c) ICW for surface expression of Glut4 on L6 myotubes. Bars represent the mean; n = 3 biologically independent samples. d) C2C12 myotubes were assayed for protein degradation by release of tritiated phenylalanine. Bars represent the mean; for DMSO, n = 6, and for Dex, n = 4 biologically independent samples. e) ICW for protein synthesis by insulin-induced incorporation of puromycin into C2C12 myotube surface proteins. Bars represent the mean; n = 3 biologically independent samples. f-g) High-content imaging and analysis of C2C12 myoblasts stained with MitoTracker™ dye. Images are representative of 18 images per condition; that is 3 fields x 3 biologically independent samples per condition in each of 2 independent experiments. h) Assay reproducibility from screening 22 compounds on two separate occasions. The Pearson correlation coefficient, r, and its associated p-value is indicated. Each datapoint represents the mean effect of a distinct compound. Also see Methods. i–j) Linear regression demonstrates the predictive power (r2), and associated p-value for the indicated variables. i) Ψm and pAKT predict Synthesis (p = 6 × 10−5 and 8 × 10−5, respectively). j) GR nuclear translocation selectively predicts Glut4 (p = 2 × 10−5) but not pAKT. Each datapoint represents the effects of a distinct ligand.
Extended Data Fig. 3 Relationships among specific genes, peptide interaction assays, and GR-mediated phenotypes.
a-e) Linear regression was performed for the indicated assay pairs, where each point represents a different compound. a) Effects of the ligands on Socs2 expression predicts Glut4 translocation, ψm, and insulin-stimulated protein synthesis. b) Ligand-dependent expression of Bcl2l1, which encodes the mitochondrial anti-apoptotic protein, Bcl-xL does not predict effects of on ψm. c) Fkbp5 expression as a predictor of Glut4 translocation shows an inflection point (arrow). d) The Glut4 data was truncated below the inflection point shown in c). e) GR interaction with an NCOR2 peptide predicts protein synthesis in the C11 subset of ligands. f) The C11- and C3-substituted compounds showed similar variance in the skeletal muscle profiling assays. Blue dashed line, vehicle; red dashed line, Dex. Each datapoint represents the mean effect of a distinct compound, n = 3 biologically independent samples. The error bars represent the mean ± SD of each compound series. For C11, n = 8 distinct compounds; for C3, n = 7 distinct compounds. See also Fig. 2a-e.
Extended Data Fig. 4 Compound structure-activity relationships.
a) Individual compound data for protein degradation, insulin-stimulated protein synthesis, and Glut4 translocation in myotubes, as well as effects on IL-1β-stimulated secretion of IL-6 by A549 cells. Lead compounds are indicated with arrows. Among the 3 compounds with full suppression of IL-6 (13,14,15), only 15 did not inhibit protein synthesis or stimulate protein degradation. 18 showed the greatest anabolic effects, with stimulation of protein synthesis and inhibition of protein degradation. b) Protein degradation in myotubes assayed as described in Extended Data Fig. 2 and Methods. Bars represent the mean, n = 4, except for DMSO where n = 6 biologically independent samples. c) 293 T cells were co-transfected with a GR expression plasmid and MMTV-luciferase reporter. The next day cells were treated with the indicated compounds for 24 h and probed for luciferase activity. For SR16024, was the cells were cotreated with 1 nM Dex. Data are mean ± SEM, n = 3 biologically independent samples. d) Mitochondrial potential of myotubes assayed as described in Extended Data Fig. 2 and Methods. Bars represent the mean; n = 3, except for 15 where n = 2 biologically independent samples.
Extended Data Fig. 5 On-target mechanism of action studies.
a) Reporter activity in steroid-deprived 293 T cells co-transfected with an androgen-responsive ARR3-tk-luc reporter and an androgen receptor (AR) expression plasmid or empty vector control, and then treated with the indicated compounds for 24 h. Dose curves for compounds that stimulated AR activity (left) and the indicated compounds (right) are shown. None of the compounds showed activity with the empty vector control. 18 and 19 are isomers differing only in the position of the chlorine on the benzyl substitution. Datapoints are mean ± SEM; n = 3 biologically independent samples. b) Linear regression demonstrating that ligand-specific AR activity profiles do not correlate with protein synthesis. c) Expression of steroid receptor mRNAs in A549 cells. Only Ar which encodes AR, and Nr3c1 which encodes GR were detected by qPCR. Bars represent the mean; n = 3 independent samples. Also see Supplementary Fig. 3. d) Representative qPCR amplification plots for Pgr, Ar, and Nr3c1 in A549 versus MCF7 cells. Pgr, which encodes the progesterone receptor, is not expressed in A549 cells. e) AR antagonists do not reverse the effects of Dex on IL-6 secretion. IL-6 levels in A549 cell media were measured by AlphaLISA after overnight exposure to the indicated conditions. DHT, 5α-dihydrotestosterone; BIC, bicalutamide; ENZ, enzalutamide. For the controls (left), bars represent the mean; n = 6 biologically independent samples. For dose curves, datapoints are mean ± SEM; n = 3 biologically independent samples. f) Luciferase assay showing the effects of 1 nM DHT, 1 µM BIC and 1 µM ENZ on AR activity, demonstrating that the antagonists have cellular activity. Bars represent the mean; n = 3 biologically independent samples.
Extended Data Fig. 6 In vivo compound profiling.
a) Mouse pharmacokinetics studies of the indicated compounds. Data are mean ± SEM; n = 3 biologically independent samples. b-c) Changes in the lean mass and body weights of male C57BL/6 mice treated with (10 mg/kg Dex or SR16024, or 50 mg/kg SR11466) and fasted overnight. Bars represent the mean; n = 5 mice per group (in each of 2 independent experiments). 1-way ANOVA, Sidak’s multiple comparisons test, adjusted p-values are indicated.
Extended Data Fig. 7 Docking and molecular dynamics simulations.
a) Ribbon diagram of GR LBD bound to the indicated ligands. 7 and 9 were docked with Autodock Vina. b) Differential analysis of correlated motion between Cα atoms from the simulations with the indicated ligands subtracted from Dex. c) Formation of a hydrogen bond with R611 differentially shifts the position of the ligands. d) Formation of the hydrogen bond R611-induced changes in surface structure (red arrows). With Dex, there was a shift in h12 and the C-terminus of h3. With 7, the C-terminus of h11 and N-terminus of h3 were shifted further apart, and away from h12. This destabilization of the h12 interface with h3 and h11 explains why this compound is an antagonist, a mechanism we have called ‘indirect antagonism.’ With 9, there was a rotation of both ends of h3. e) Usage of amino acid residue and edge in the suboptimal pathways between h12 E755 and h5 R614, demonstrating that Dex preferentially utilized R611 instead of W610 as a pathway for correlated motion. Also see Methods.
Supplementary information
Supplementary Information
Supplementary Tables 1–3, Figs. 1–3, Note and references.
Supplementary Data 1
Compound profiling data, correlation results, machine learning results.
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Bruno, N.E., Nwachukwu, J.C., Srinivasan, S. et al. Chemical systems biology reveals mechanisms of glucocorticoid receptor signaling. Nat Chem Biol 17, 307–316 (2021). https://doi.org/10.1038/s41589-020-00719-w
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DOI: https://doi.org/10.1038/s41589-020-00719-w
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