1. Biochemistry and Chemical Biology
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A chemical probe of CARM1 alters epigenetic plasticity against breast cancer cell invasion

  1. Xiao-Chuan Cai
  2. Tuo Zhang
  3. Eui-jun Kim
  4. Ming Jiang
  5. Ke Wang
  6. Junyi Wang
  7. Shi Chen
  8. Nawei Zhang
  9. Hong Wu
  10. Fengling Li
  11. Carlo C dela Seña
  12. Hong Zeng
  13. Victor Vivcharuk
  14. Xiang Niu
  15. Weihong Zheng
  16. Jonghan P Lee
  17. Yuling Chen
  18. Dalia Barsyte
  19. Magda Szewczyk
  20. Taraneh Hajian
  21. Glorymar Ibáñez
  22. Aiping Dong
  23. Ludmila Dombrovski
  24. Zhenyu Zhang
  25. Haiteng Deng
  26. Jinrong Min
  27. Cheryl H Arrowsmith
  28. Linas Mazutis
  29. Lei Shi
  30. Masoud Vedadi
  31. Peter J Brown
  32. Jenny Xiang
  33. Li-Xuan Qin
  34. Wei Xu
  35. Minkui Luo  Is a corresponding author
  1. Memorial Sloan Kettering Cancer Center, United States
  2. Weill Cornell Medical College, Cornell University, United States
  3. McArdle Laboratory for Cancer Research, University of Wisconsin-Madison, United States
  4. Weill Cornell Medical College of Cornell University, United States
  5. Chaoyang Hospital, Affiliation Hospital of Capital Medical University, China
  6. University of Toronto, Canada
  7. School of Life Sciences, Tsinghua University, China
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Cite this article as: eLife 2019;8:e47110 doi: 10.7554/eLife.47110

Abstract

CARM1 is a cancer-relevant protein arginine methyltransferase that regulates many aspects of transcription. Its pharmacological inhibition is a promising anti-cancer strategy. Here SKI-73 (6a in this work) is presented as a CARM1 chemical probe with pro-drug properties. SKI-73 (6a) can rapidly penetrate cell membranes and then be processed into active inhibitors, which are retained intracellularly with 10-fold enrichment for several days. These compounds were characterized for their potency, selectivity, modes of action, and on-target engagement. SKI-73 (6a) recapitulates the effect of CARM1 knockout against breast cancer cell invasion. Single-cell RNA-seq analysis revealed that the SKI-73(6a)-associated reduction of invasiveness acts by altering epigenetic plasticity and suppressing the invasion-prone subpopulation. Interestingly, SKI-73 (6a) and CARM1 knockout alter the epigenetic plasticity with remarkable difference, suggesting distinct modes of action for small-molecule and genetic perturbations. We therefore discovered a CARM1-addiction mechanism of cancer metastasis and developed a chemical probe to target this process.

eLife digest

Drugs that are small molecules have the potential to block the individual proteins that drive the spread of cancer, but their design is a challenge. This is because they need to get inside the cell and find their target without binding to other proteins on the way. However, small molecule drugs often have an electric charge, which makes it hard for them to cross the cell membrane. Additionally, most proteins are not completely unique, making it harder for the drugs to find the correct target.

CARM1 is a protein that plays a role in the spread of breast cancer cells, and scientists are currently looking for a small molecule that will inhibit its action. The group of enzymes that CARM1 belongs to act by taking a small chemical group, called a methyl group, from a molecule called SAM, and transferring it to proteins that switch genes on and off. In the case of CARM1, this changes cell behavior by turning on genes involved in cell movement. Genetically modifying cells so they will not produce any CARM1 stops the spread of breast cancer cells, but developing a drug with the same effects has proved difficult. Existing drugs that can inhibit CARM1 in a test tube struggle to get inside cells and to distinguish between CARM1 and its related enzymes.

Now, Cai et al. have modified and tested a CARM1 inhibitor to address these problems, and find out how these small molecules work. At its core, the inhibitor has a structure very similar to a SAM molecule, so it can fit into the SAM binding pocket of CARM1 and its related enzymes. To stop the inhibitor from binding to other proteins, Cai et al. made small changes to its structure until it only interacted with CARM1.Then, to get the inhibitor inside breast cancer cells, Cai et al. cloaked its charged area with a chemical shield, allowing it to cross the cell membrane. Inside the cell, the chemical shield broke away, allowing the inhibitor to attach to CARM1. Analysis of cells showed that this inhibition only affected the cancer cells most likely to spread. Blocking CARM1 switched off genes involved in cell movement and stopped cancer cells from travelling through 3D gels.

This work is a step towards making a drug that can block CARM1 in cancer cells, but there is still further work to be done. The next stages will be to test whether the new inhibitor works in other types of cancer cells, in living animals, and in human patient samples.

Introduction

Numerous biological events are orchestrated epigenetically upon defining cellular fates (Atlasi and Stunnenberg, 2017; Berdasco and Esteller, 2019). Among the key epigenetic regulators are protein methyltransferases (PMTs), which can render downstream signals by modifying specific Arg or Lys residues of their substrates with S-adenosyl-L-methionine (SAM) as a methyl donor cofactor (Luo, 2018). Significant efforts have been made to identify the PMT-dependent epigenetic cues that are dysregulated or addicted under specific disease settings such as cancer (Berdasco and Esteller, 2019). Many PMTs are implicated as vulnerable targets against cancer malignancy (Kaniskan et al., 2018; Luo, 2018). The pro-cancerous mechanism of these PMTs can be attributed to their methyltransferase activities, which act individually or in combination to upregulate oncogenes, downregulate tumor suppressors, and maintain cancer-cell-addicted homeostasis (Berdasco and Esteller, 2019; Blanc and Richard, 2017). Pharmacological inhibition of these epigenetic events thus presents promising anti-cancer strategies (Berdasco and Esteller, 2019), as exemplified by the development of the clinical inhibitors of DOT1L (Bernt et al., 2011; Daigle et al., 2011), EZH2 (Kim et al., 2013; Konze et al., 2013; McCabe et al., 2012; Qi et al., 2012; Qi et al., 2017), and PRMT5 (Bonday et al., 2018; Chan-Penebre et al., 2015).

Protein arginine methyltransferases (PRMTs) act on their substrates to yield three different forms of methylated arginine: asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), and monomethylarginine (MMA), which are the terminal products of Type I, II and III PRMTs, respectively (Blanc and Richard, 2017; Yang and Bedford, 2013). Among the important Type I PRMTs is CARM1 (PRMT4), which regulates multiple aspects of transcription by methylating diverse targets including RNAPII, SRC3, C/EBPβ, PAX3/7, SOX2/9, RUNX1, Notch1, p300, CBP, p/CIP, Med12, and BAF155 (Blanc and Richard, 2017; Hein et al., 2015; Vu et al., 2013; Wang et al., 2015; Wang et al., 2014a; Yang and Bedford, 2013). The physiological function of CARM1 has been linked to the differentiation and maturation of embryonic stem cells to form immune cells, adipocytes, chondrocytes, myocytes, and lung tissues (Blanc and Richard, 2017; Yang and Bedford, 2013). The requirement of CARM1 is implicated in multiple cancers, with its methyltransferase activity particularly addicted by hematopoietic malignancies and metastatic breast cancer (Drew et al., 2017; Greenblatt et al., 2018; Nakayama et al., 2018; Wang et al., 2014a). Our prior efforts using in vivo mouse and in vitro cell models uncovered the role of CARM1 in promoting breast cancer metastasis (Wang et al., 2014a). Mechanistically, CARM1 methylates Arg1064 of BAF155 and thus facilitates the recruitment of the BAF155-containing SWI/SNF complex to a specific subset of gene loci that are essential for breast cancer metastasis. CARM1 thus emerges as a novel anti-cancer target (Wang et al., 2014a).

Although this cancer relevance inspired the development of CARM1 inhibitors (Kaniskan et al., 2018; Scheer et al., 2019), many small-molecule CARM1 inhibitors lack target selectivity or cellular activity (Kaniskan et al., 2018), two essential criteria of chemical probes (Frye, 2010). To the best of our knowledge, EZM2302 (Drew et al., 2017; Greenblatt et al., 2018), TP-064 (Nakayama et al., 2018) and SKI-73 (6a in this work, www.thesgc.org/chemical-probes/SKI-73), which were developed by Epizyme, Takeda/SGC(Structural Genomic Consortium), and our team, respectively, are the only selective and cell-active CARM1 chemical probes. EZM2302 and TP-064 were developed from conventional small-molecule scaffolds occupying the substrate-binding pocket of CARM1 (Drew et al., 2017; Greenblatt et al., 2018; Nakayama et al., 2018). The potential utility of EZM2302 and TP-064 is implicated by their selective anti-proliferative effects on hematopoietic cancer cells, in particular multiple myeloma cells (Drew et al., 2017; Greenblatt et al., 2018; Nakayama et al., 2018). However, definitive molecular mechanisms of the CARM1 addiction in these contexts remain elusive (Greenblatt et al., 2018).

Here, we report the characterization and novel utility of SKI-73, a chemical probe of CARM1 with pro-drug properties. SKI-73 (6a in this work) can readily penetrate cell membranes and then be processed into two active CARM1 inhibitors that contain 6′−homosinefungin (HSF) as their core scaffold (Scheer et al., 2019; Wu et al., 2016). Notably, the two inhibitors can accumulate inside cells to remarkably high concentrations and for a prolonged period. The potency, selectivity, modes of action, on-target engagement, and off-target effects of these compounds were characterized with multiple orthogonal assays in vitro and under cellular settings. The pharmacological inhibition of CARM1 by SKI-73 (6a) recapitulates the anti-invasion effect of the genetic perturbation of CARM1. In the context of cellular heterogeneity, we developed a cell-cycle-aware algorithm for single-cell RNA-seq (scRNA-seq) analysis and dissected the invasion-prone subset of breast cancer cells that is sensitive to SKI-73 (6a) treatment. Our scRNA-seq analysis provides the unprecedented insight that pharmacological inhibition of CARM1 alters epigenetic plasticity and suppresses invasion by suppressing the most invasive subpopulation of breast cancer cells.

Results

Development of 6′−homosinefungin derivatives as potent and selective CARM1 inhibitors

Upon developing cofactor-competitive PMT inhibitors (Wu et al., 2016; Zheng et al., 2012), we tailored the SAM analog sinefungin (Figure 1a) around its 6′-amino moiety to potentially engage CARM1’s substrate-binding pocket. 6′-homosinefungin (HSF, i.e., 1), a sinefungin analog with the insertion of 6′−methylene moiety, was discovered for its general high affinity to Type I PRMTs (Figure 1a,b, Figure 1—figure supplement 1, Supplementary file 1-Table A). As a SAM mimic, 1 binds to the Type I PRMTs (namely PRMT1, CARM1, PRMT6 and PRMT8) with IC50 of 13‒300 nM (Figure 1a,c, Supplementary file 1-Table A). Its relative affinity to Type I PRMTs aligns with that of the SAM mimics SAH and SNF (around 20-fold lower IC50 of 1 versus SAH and SNF, Figure 1a,c, Supplementary file 1-Table A). This observation argues that 1 retains the structural features of SAH and SNF to engage PRMTs and meanwhile leverages its 6′-methyleneamine group for additional interaction.

Figure 1 with 3 supplements see all
Structures, synthesis and target inhibition of SAM analogs.

(a) Structures of SAM, SAH, sinefungin (SNF) and 6′-homosinefungin (HSF, 1). (b) Structures and synthetic outline of HSF derivatives 2a and 5a, and their structurally related control compounds 2b and 5b. (c) IC50 heat-map of SAM analogs against 34 methyltransferases. HSF derivatives 2a and 5a were identified as potent and selective inhibitors of CARM1; 2b and 5b as their respective control compounds.

To further explore the 6′-region of HSF, we synthesized HSF derivatives from the same precursor 3 (Figure 1b, Figure 1—figure supplement 2), by further expanding the 6′-methylene amine moiety with different substituents. The HSF derivative 2a (Figure 1b) was identified for its preferential binding to CARM1 with IC50 = 30 ± 3 nM and >10 fold selectivity over other seven human PRMTs and 26 methyltransferases of other classes (Figure 1c, Supplementary file 1-Table A). The structural difference between 2a and 1 (Figure 1b) suggests that the N-benzyl substituent enables 2a to engage CARM1 through a distinct mechanism (see results below).

With 2a as a lead, we then explored its α-amino carboxylate moiety with different amides from the common precursor three and then the intermediate 4 (Figure 1—figure supplement 2), which led to the discovery of 5a. This engagement of CARM1 with 2a is expected to be largely maintained by 5a. Here, 5a shows an IC50 of 43 ± 7 nM against CARM1 and a >10-fold selectivity over the panel of 33 diverse methyltransferases (Figure 1c, Supplementary file 1-Table A). In comparison, the negative control compounds 2b (Bn-SNF) (Zheng et al., 2012) and 5b (Figure 1b, Figure 1—figure supplement 3), which differ from 2a and 5a only by the 6′-methylene group, poorly inhibit CARM1 (IC50 = 22 ± 1 µM and 1.91 ± 0.03 µM) (Figure 1c, Supplementary file 1-Table A). The dramatic increase of the potency of 2a and 5a in contrast to 2b and 5b supports an essential role of the 6′-methylene moiety upon binding CARM1. Distinguished from the SAM mimics SAH, SNF and 1 as nonspecific PMT inhibitors, 2a and 5a were developed as potent and selective SAM analogs.

Modes of interaction of 6′-homosinefungin derivatives as CARM1 inhibitors

With 2a and 5a characterized as CARM1 inhibitors, we leveraged orthogonal in vitro assays to explore their modes of interaction (Figure 2a). To examine whether 2a and 5a are SAM- or substrate-competitive, CARM1 inhibition by 2a and 5a was assessed in the presence of various concentrations of SAM cofactor and H3 peptide substrate (Figure 2b,c). IC50 values of 2a and 5a showed a linear positive correlation with SAM concentrations, as expected for SAM-competitive inhibitors (Daigle et al., 2011; Luo, 2018; Zheng et al., 2012) .The Kd values of 2a and 5a (Kd,2a = 17 ± 8 nM; Kd,5a = 9 ± 5 nM) were extrapolated from the y-axis intercepts upon fitting the equation IC50 = [SAM]×Kd/Km,SAM+Kd (Figure 2b) (Segel, 1993). Km,SAM of 0.21 ± 0.09 µM and 0.28 ± 0.14 µM (an averaged Km,SAM = 0.25 µM) for competition with 2a and 5a, respectively, can also be derived through the ratio of the y-axis intercepts to the slopes (Figure 2b and Materials and methods) (Segel, 1993). By contrast, the presence of the H3 peptide substrate had negligible effect on the binding of 2a and 5a, indicating their substrate-noncompetitive character (Figure 2c). The SAM analogs 2a and 5a were thus characterized as SAM-competitive, substrate-noncompetitive inhibitors of CARM1.

In vitro characterization of CARM1 inhibitors 2a and 5a.

(a) Schematic description of CARM1 in complex with SAM, 2a and 5a in the absence or presence of a substrate peptide. (b, c) IC50 of 2a and 5a in the presence of varied concentrations of SAM and H3 peptide substrate. IC50 data were obtained and presented as the mean of replicates ± standard errors. The IC50 values of 2a and 5a show a linear increase relative to the SAM concentration but remain near constant as the the substrate concentration increases. Given the SAM competitive character, the Kd values of 2a and 5a as well as Kd,SAM can be obtained according to IC50 = [SAM]×Kd/Kd,SAM+Kd. (d) SPR assay for the binding of CARM1 by 2a and 5a. Processed sensorgrams upon ligand binding (black dots) were fitted with a biphasic binding model (green line) with Ki1,2a = 56 nM (0.06 ± 0.02 µM) and Ki2,2a = 404 nM (0.4 ± 0.1 µM); Ki1,5a = 105 nM (0.10 ± 0.01 µM) and Ki2,5a = 541 nM (0.54 ± 0.07). (e) Thermal shift assay of CARM1 in the absence or presence of SAM, 1, 2a, and 5a. Tm values of 39.8 ± 0.2°C, 40.1 ± 0.5°C, 42.8 ± 0.3°C, 44.2 ± 0.6°C and 46.3 ± 0.3°C (means of triplicates ± standard derivatives) were obtained for apo-CARM1 and CARM1 complexes with 5 μM SAM, 1, 2a, and 5a, respectively.

For the direct binding of 2a and 5a to CARM1, the CARM1-binding kinetics of 2a and 5a were examined using surface plasmon resonance (SPR) (Figure 2d). The SPR signal progression of 2a and 5a fits with a biphasic rather than a mono-phasic binding mode, with the lower Ki1,2a = 0.06 ± 0.02 µM, Ki1,5a = 0.10 ± 0.01 µM, and the higher Ki2,5b = 0.54 ± 0.07 µM, Ki2,2a = 0.4 ± 0.1 µM, probably because of the multi-phase binding kinetics of 2a and 5a (Figure 2d). To cross validate the binding of 2a and 5a to CARM1, we conducted an in vitro thermal shift assay, for which ligand binding is expected to increase CARM1’s thermal stability (Blum et al., 2014). The binding of 2a and 5a (at 5 μM concentration) increased the melting temperature (Tm) of CARM1 by 4.4°C and 6.5°C, respectively (Figure 2e, Tm, 2a = 44.2 ± 0.4 °C and Tm, 5a = 46.3 ± 0.3 °C versus Tm, DMSO = 39.8 ± 0.3 °C as control). By contrast, the binding of SAM and 1 show much reduced effects on Tm of CARM1 (Figure 2e, Tm, SAM = 40.1 ± 0.3 °C and Tm, 1 = 42.8 ± 0.4 °C versus Tm, DMSO = 39.8 ± 0.3 °C). Therefore, although the affinities of 1, 2a and 5a to CARM1 are comparable (IC50 = 13‒43 nM, Figure 1c), their well-separated effects on Tm suggest that these inhibitors engage CARM1 differentially (see results below). The two orthogonal biochemical assays thus verified the tight binding of 2a and 5a with CARM1.

Structural rationale of 6′-homosinefungin derivatives 5a and 2a as CARM1 inhibitors

To further seek a structural rationale for 5a and 2a for CARM1 inhibition, we solved the X-ray structure of CARM1 in complex with 5a with resolution of 2.00 Å and modeled the CARM1 binding of 2a (Figure 3, Materials and methods). The overall topology of the CARM1–5a complex is indistinguishable with the V-shaped subunit of the CARM1 dimer in complex with SNF and 1 (details in the next section), which is typical of the Rossmann fold of Class I methyltransferases (Figure 3a,b, Table 1) (Luo, 2018). However, 5a adopts a noncanonical pose with its 6′-N-benzyl moiety in a binding pocket that used to be occupied by the α-amino carboxylate moiety of canonical ligands such as SAH, SNF and 1 (Figures 3c and 4), while the α-amino methoxyphenethyl amide moiety of 5a protrudes into the substrate-binding pocket (Boriack-Sjodin et al., 2016; Sack et al., 2011). This noncanonical mode is consistent with the SAM-competitive character of 5a (Figure 2b). In contrast to the noncanonical mode, Arg168 in the CARM1–5a complex adopts an alternative orientation (two possible configurations), accompanied by an altered conformation of Glu257, to accommodate the 6′-N-benzyl moiety of 5a (Figure 3d). The α-amino amide moiety of 5a also engages CARM1 through the combined outcomes of a hydrogen-bond network and hydrophobic interactions with nearby resides (Figure 3e). Interestingly, the overlaid structures of CARM1 in complex with 5a and a substrate peptide implicate a steric clash and thus a potential for binding competition between 5a and a CARM1 substrate (Figure 3f). However, the apparent substrate-noncompetitive character of 5a (Figure 2c) suggests that this steric clash might be avoided if there is no significant energy penalty when the substrate Arg adopts alternative conformation(s).

Figure 3 with 1 supplement see all
Crystal structure or molecular modeling of CARM1 in complex with 5a and 2a.

(a) Overview of the Rossmann fold in the X-ray structure of CARM1 with 5a. (b) Total omission electron density map of 5a in the CARM1–5a complex. The total omission electron density map was calculated using SFCHECK, as described in the Materials and methods. The electron density contoured at 1.0 σ is shown for the ligands. (c) Comparison of the binding modes between 5a (noncanonical) and SNF (canonical). The structure of SNF)was extracted from a CARM1–SNF–H3R17 complex (PDB 5D × 0). (d) Key interactions between CARM1 and ligands in canonical and noncanonical binding modes. The differentiated interactions are highlighted in gray (CARM1) and blue (SNF) for the canonical mode; and in green (CARM1) and orange (5a) for the noncanonical mode. (e) Additional interactions in which the α-amino amide moiety of 5a forms hydrogen bonds with Glu266 and His414 and hydrophobic interactions with Phe152 and Tyr261. (f) Steric clash between the α-amino amide moiety of 5a and an Arg substrate. The structure of the Arg substrate was extracted from a CARM1–SNF–H3R17 complex (PDB 5D × 0). (g) Two modeled binding poses (BP1 and BP2) of 2a upon binding CARM1 with the Cβ-Cγ-Cδ-Nε dihedral angle χ3 = 180°; the C4'-C5'-C6'-C7' dihedral angle of −50o for BP1 versus χ3 = −65° C4'-C5'-C6'-C7' dihedral −170o for BP2.

Figure 4 with 1 supplement see all
Structure of CARM1 in complex with 1 (HSF) in multiple configurations (PDB: 4IKP).

(a) Overall structures of the CARM1–1 complex featuring V-shape subunits in a tetramer (Chain A–D). (b) Multiple configurations of 1 upon occupying the SAM-cofactor-binding site of CARM1. (c) Representative interaction network of 1 (Configuration III, IV in Chain A) upon binding human CARM1, and its comparison with SNF and SAH. Here, we highlight the conserved hydrogen bonds with adenine ring (blue), the 2′,3′-ribosyl hydroxyl/α-amino groups (black), and the distinct interaction network in Chain A for carboxylic moieties/6′-methyleneamine (pink and red). (d) Representative interaction network of 1 (Configuration I in Chain B) upon binding human CARM1, and its comparison with SNF and SAH. Here, we highlight the conserved hydrophobic interactions with adenine ring (blue), the conserved hydrogen bond interactions with 2′,3′-ribosyl hydroxyl (black), the α-amino, carboxylic moieties and the distinct interaction network of 6′-methyleneamine (red). The images of SNF and SAH were generated on the basis of PDB files 2Y1W and 2Y1X.

Table 1
Crystallography data and refinement statistics of the X-ray structures of CARM1 in complex with 5a.
Ligands5a
PDB Code6D2L
Data collection
Wavelength (Å)0.97918
Space groupP21
Cell dimensions
a, b, c (Å)75.6, 155.6, 95.3
α, β, γ (°)90.0, 101.0, 90.0
Resolution (Å)50.0–2.00
Unique reflections142, 452
Redundancy4.5
Completeness (%)97.0
I/σ(I)10.4
Rsyma0.155
Rpim0.081
Refinement
No. protein molecules/ASU6
Resolution (Å)50.0–2.00
Reflections used or used/free139,748/1400
Rwork(%)18.7
Rfree(%)23.6
Average B value (Å2)30.8
Protein30.5
Compound34.8
Othern/a
Water35.5
Number of Atoms16,868
Protein15,819
Compound253
Othern/a
Water766
RMS bonds (Å)0.008
RMS angles (°)1.273
Wilson B (Å2)29.6
Ramachandran plot
Most favored (%)97.1
Additional allowed (%)2.9
Outliers (%)0.0

The binding mode of the CARM1–2a complex was modeled via molecular docking followed by molecular dynamics (MD) simulation (Materials and methods). Here, we uncovered two distinct poses of 2a (Binding Pose 1/2 or BP1/2, Figure 3g, Figure 3—figure supplement 1). BP1 was characterized by the direct interaction between the α-amino carboxylate moiety of 2a and the guanidinium of Arg168, whereas BP2 features a titled orientation of Arg168 to accommodate the 6′-N-benzyl moiety of 2a (Figure 3g, Figure 3—figure supplement 1). The BP1 and BP2 of 2a closely resemble those of 1 and 5a, respectively, in terms of the orientations of Arg168 and the α-amino carboxylate moiety of the ligands. When the same modeling protocol was applied to the CARM1–SNF complex, only the canonical pose was identified (Materials and methods). Energy calculation indicated that both BP1 and BP2 are stable with comparable binding free energies. Interestingly, the side chain configurations of His414 in both BP1 and BP2 are different from those in the CARM1–5a complex and the CARM1–SNF complex (Figure 3g). Collectively, 5a and 2a, though structurally related to the SAM analogs 1 and SNF, engage CARM1 via distinct modes of interaction.

Upon comparing the CARM1 structure in complex with 5a and 2a, we observed the additional hydrogen-bond and hydrophobic interactions of 5a that involve its α-amino amide moiety (Figure 3e). Interestingly, these interactions do not increase but rather decrease the affinity of 5a to CARM1 by two-fold (Kd,2a = 17 ± 8 nM versus Kd,5a = 9 ± 5 nM, Figure 2b). By contrast, there is a significant 10-fold increase of affinity between 2b and 5b (Figure 1c, Supplementary file 1-Table A). These observations suggest that, although 5b facilitates CARM1’s engagement better than 2b via the former’s α-amino amide moiety, such an effect is dispensed with in the presence of the 6′-methylene (N-benzyl)amine moiety of 5a and 2a.

The X-ray structure of the CARM1–1(HSF) complex

Given the tight CARM1 binding by 1, we solved the X-ray structure of CARM1 in complex with 1 (HSF) with a resolution of 2.00 Å (Figure 4). The overall folding of the CARM1–1 complex (PDB: 4IKP) is similar to those of 1 in complex with SNF and SAH (PDB: 2Y1X, 2Y1W) with a V-shape subunit in a dimer of dimers (Figure 4a, Table 2) (Sack et al., 2011). However, the CARM1–1 complex is distinct for multiple configurations of its ligand and interactions via its 6′-methyleneamine moiety (Figure 4b–d, Figure 4—figure supplement 1). In the CARM1–1 complex, 1 can adopt four alternative configurations (Configuration I in Chains B, D; Configuration II in Chain C; Configuration III and IV in Chain A) accompanied with the structural accommodation of the adjacent residues and water hydrogen bonds (Figure 4b–d, Supplementary file 1-Table B–D). By contrast, only a single configuration of the ligands was observed in SAH- or SNF-bound CARM1 (Figure 4c,d, PDB: 2Y1X, 2Y1W) (Sack et al., 2011).

Table 2
Crystallography data and refinement statistics for the X-ray structures of CARM1 in complex with 1.
Ligands1
PDB code4IKP
Data collection
Wavelength (Å)1.03321
Space groupP212121
Cell dimensions
a, b, c (Å)75.1, 98.8, 206.6
α, β, γ (°)90.0, 90.0, 90.0
Resolution (Å)50.0–2.00
Unique reflections104, 330
Redundancy8.1
Completeness (%)99.8
I/σ(I)30.4
Rsyma0.086
Rpim0.032
Refinement
No. protein molecules/ASU4
Resolution (Å)48.1–2.00
Reflections used or used/free103,958
Rwork(%)20.3
Rfree(%)23.1
Average B value (Å2)33.9
Protein33.4
Compound29.4
Othern/a
Water42.1
Number of atoms11,635
Protein10,770
Compound117
Othern/a
Water748
RMS bonds (Å)0.007
RMS angles (°)1.127
Wilson B (Å2)33.9
Ramachandran plot
Most favored (%)96.9
Additional allowed (%)3.03
Outliers (%)0.07

Detailed structural comparison of CARM1 in complex with SNF, SAH and 1 further revealed that 1 maintains common interactions observed in the CARM1–SNF and CARM1–SAH complexes with several exceptions (Figure 4c,d, Supplementary file 1-Table B–D). Most noticeably, the CARM1–1 complex gains the strong hydrogen bonds via the 6′-methyleneamine moiety of the ligand with (i) the backbone carbonyl of CARM1’s Glu258 (Configuration I, II in Chains B, C, D and Configuration III in Chain A) or (ii) the side chain of Tyr154 (Configuration IV in Chain A), together with several less conserved water hydrogen bonds (Figure 4c,d, Supplementary file 1-Table B–D). By contrast, the 6′-amine of SNF in the CARM1–SNF complex forms weaker hydrogen bonds with Glu258 and may fewer water hydrogen bonds (Figure 4c, Supplementary file 1-Table B, C, PDB: 2Y1W). Comparable interactions are completely absent from the CARM1–SAH complex (Figure 4d, Supplementary file 1-Table B–D, PDB: 2Y1X). The desired hydrogen-bond networks of the 6′-methyleneamine moiety of 1 with CARM1, which are present in the CARM1-1 complex but absent from the CARM1–SNF and CARM1–SAH complexes, can rationalize the significant decrease of IC50 from SNF and SAH to 1.

Another key difference among CARM1–1, CARM1–SNF and CARM1–SAH complexes lies in the region around the carboxylate moiety of these ligands. In Chains A and C of the CARM1–1 complex, the carboxylate moiety of the ligand forms an ionic bond with Arg169 and a hydrogen bond with Gln160 (Figure 4b,c,d, Supplementary file 1-Table B, C). Such interactions are absent from CARM1–SNF and CARM1–SAH complexes (Figure 4d). By contrast, in Chains B and D of the CARM1–1 complex, the same carboxylate moiety forms the ionic bonds with Arg169 and a water hydrogen bond (Figure 4b,c,d, Supplementary file 1-Table B, C). To accommodate the latter conformation, the Gln160 residue flips toward the 3′-ribosyl hydroxyl moiety of 1 to form a new hydrogen bond (Figure 4b,c, Supplementary file 1-Table B, C). Similar interaction patterns can also be found in the CARM1–SNF and CARM1–SAH complexes (Figure 4, Supplementary file 1-Table B–D).

With regards to the rest of the CARM1–ligand interactions, the CARM1 complexes with 1, SAH and SNF are nearly identical except for slightly altered water hydrogen bonds (Figure 4, Supplementary file 1-Table S2–S4). Here, the α-amino moiety of these ligands forms hydrogen bonds with the carbonyl backbone of Gly193, as well as two water hydrogen bonds; their 2′,3′-ribosyl hydroxyl groups form two hydrogen bonds with the side chain of CARM1’s Glu215; adenine’s N1 and N6 form the hydrogen bonds with Asn243 and Glu244/Ser272, respectively; and the adenine ring of these ligands is buried within a hydrophobic pocket. By contrast, there are fewer conserved water hydrogen bonds, such as those involved with the carboxylate and 3′-ribosyl hydroxyl moieties of 1 in Chain A of the CARM1–1 complex (Figure 4, Supplementary file 1-Table C). By contrast, adenine-N7 in the CARM1–SNF and CARM1–SAH complexes forms water hydrogen bonds bridged to Ser272, which are absent from the CARM1–1 complex (Figure 4, Supplementary file 1-Table C, D). Collectively, the general high affinity of 1, SNF and SAH (Figure 4, Supplementary file 1-Table B–D) arises from the combined hydrophilic and hydrophobic interactions of these ligands with CARM1. However, in comparison with SNF and SAH, 1 gains the extra interactions via its 6′-methyleneamine moiety (Figure 4c,d, Supplementary file 1-Table B–D). In addition, 1 adopts the canonical pose with its α-amino carboxylate moiety interacting with Arg168, which is similar to that of SNF and SAH but different from the noncanonical pose of 2a and 5a, upon binding CARM1 (Figure 4d).

A pro-drug-like 6′-homosinefungin derivative as a cell-active CARM1 inhibitor

Although the in vitro characterization demonstrated the potency and selectivity of 2a and 5a against CARM1, we anticipated their poor membrane permeability as observed for structurally related analogs such as SAH and SNF (Figure 1a) (Boriack-Sjodin et al., 2016; Sack et al., 2011). The lack of membrane penetration is probably due to their primary amine moiety, which has pKa of ~10 and is fully protonated at a physiological pH of 7.4. Given the essential roles of the 9′−amine moiety of 2a and 5a in CARM1 binding (Figure 3e), we envisioned overcoming the membrane permeability issue through a pro-drug strategy by cloaking this amine moiety with a redox-triggered trimethyl-locked quinone butanoate moiety (TML, Figure 5a) (Levine and Raines, 2012). We thus prepared 6a and its control compound 6b by derivatizing 5a and 5b with the TML moiety (Figure 1b, Figure 1—figure supplements 1 and 2). To assess the cellular activity of 6a, we relied on our prior knowledge that CARM1 methylates the Arg1064 of BAF155, a core component of the SWI/SNF chromatin-remodeling complex, and CARM1 knockout abolishes this posttranslational modification in MCF-7 cells (Wang et al., 2014a). Treatment of MCF-7 cells with 10 µM of 6a fully suppressed this methylation mark, whereas treatment with 2a and 5a did not affect this mark (Figure 5b). We thus demonstrated the prodrug-like cellular activity of 6a.

Figure 5 with 6 supplements see all
Characterization of cellular activity of 6a as a chemical probe.

(a) Schematic description of the extracellular and intracellular fates of 2a, 5a and 6a. Extracellularly, 2a, 5a and 6a are stable; only 6a can readily penetrate cell membrane. Intracellularly, 6a can be processed into 5a and 2a. Given the poor membrane permeability of 2a and 5a, they are accumulated within cells at high concentrations. (b) CARM1 inhibition of 2a, 5a and 6a in MCF-7 cells with BAF155 methylation as a mark. MCF-7 cells were treated with 10 μM of 2a, 5a and 6a for 48 hr. The ratios between me-BAF155 and BAF155 were quantified as a cellular reporter of CARM1 inhibition. DMSO-treated MCF-7 cells and MCF-7 CARM1-KO cells were used as negative and positive controls, respectively. (c) MS-based quantification of intracellular concentrations of 2a, 5a, 6a and SAM. These compounds were accumulated within MDA-MB-231 cells in a dose- and time-dependent manner. In comparison, the intracellular concentration of SAM remains a constant at 89 ± 16 µM. (d) Schematic description of intracellular engagement of CARM1 by 6a, 5a and 2a in the presence of the SAM cofactor (Equations 5–7). (e) Modeled ligand occupancy of CARM1 with 2a, 5a and 6a as ligands in competition with the SAM cofactor. Percentage of competitive CARM1 occupancy was calculated on the basis of the concentrations of ligands (SAM, 2a, 5a and 6a, Figure 5c) and their Kd values (Figure 2c). (f) Cellular thermal shift assay (CETSA) of CARM1. Representative western blots of CARM1 in MDA-MB-231 cells upon the treatment of 15 μM 6a or its negative control 6b for 48 hr with DMSO treatment as reference. The relative intensity of CARM1 was quantified. The Tm values were determined at the 50% loss of the relative intensity signals with Tm,6a = 63.9 ± 0.3 °C, Tm,6b = 60.2 ± 0.6 °C and Tm,DMSO = 59.6 ± 0.2 °C.

Characterization of 6a (SKI-73) as a chemical probe of CARM1

To further evaluate 6a as a chemical probe against CARM1, we quantified the efficiency by which 6a engages CARM1 in a cellular context and thus suppresses the CARM1-dependent invasion by breast cancer cells. Because of the pro-drug character of 6a and its control compound 6b, we first developed quantitative LC-MS/MS methods to examine their cellular fates for CARM1 engagement (see Materials and methods). Upon treatment of MDA-MB-231 cells with 6a, we observed its time- and dose-dependent intracellular accumulation (Figure 5c, Figure 5—figure supplements 1 and 2). We anticipated the conversion of the pro-drug 6a into 5a, but a striking finding is that 6a can also be readily processed into 2a inside cells (Figure 5c, Figure 5—figure supplements 1 and 2). Remarkably, >100 μM of 2a can be accumulated inside cells for 2 days after 6 hr treatment with a single dose of 5‒10 µM 6a. This observation probably reflects a slow efflux and thus effective intracellular retention of 2a due to its polar α-amino acid zwitterion moiety. Given that cellular CARM1 inhibition is involved with multiple species (2a, 5a and 6a) in competition with SAM, we modeled the ligand occupancy of cellular CARM1 on the basis of their Kd values (Kd,2a=17 nM, Kd,5a=9 nM, Kd,6a=0.28 µM and Kd,SAMKm,SAM=0.25 µM) and MS-quantified intracellular concentrations (Figure 5d, Figure 5—figure supplement 3, Equations 5–7, see Materials and methods). The SAM cofactor, whose intracellular concentration was determined to be 89 ± 16 µM (Figure 5c,d), is expected to occupy >99.5% CARM1 with residual <0.5% as the apo-enzyme under a native setting. With single doses of 6a of 2.5‒10 µM, the combined CARM1 occupancy by 2a, 5a and their pro-drug precursor 6a rapidly reached the plateau of >95% within 6 hr, and was maintained at this level for at least 48 hr (Figure 5e, Figure 5—figure supplements 1 and 2). Notably, treatment with 6a concentrations as low as 0.5 µM is sufficient to reach 60% target engagement within 10 hr and to maintain this occupancy for 48 hr (Figure 5e, Figure 5—figure supplements 1 and 2). The time- and dose-dependent progression of the CARM1 occupancy by these ligands thus provides quantitative guidance upon the treatment of MDA-MB-231 cells with 6a.

Given that 2a is the predominant metabolic product of 6a within cells (Figure 5c,e) and also shows certain affinity to SMYD2 (~10 fold higher IC50 in comparison with CARM1, Figure 1c, Supplementary file 1-Table A), we evaluated SMYD2 engagement of 2a for its potential off-target effect. In a similar manner to that described for the ligand occupancy of cellular CARM1, we modeled the occupancy of cellular SMYD2 by 2a on the basis of Kd,2a=150 nM and Kd,SAM=60 nM for SMYD2 (Figure 5e, Figure 5—figure supplement 4, Materials and methods). Largely because of the high affinity of 2a to SAM and thus a 37-fold larger Kd,2a/Kd,SAM ratio of SMYD2 relative to CARM1 (Kd,2a/Kd,SAM of 2.5 and 0.068 for SMYD2 and CARM1, respectively), the occupancy of SMYD2 by 2a is below 20% (Figure 5e, Figure 5—figure supplement 4) under the efficacy doses of 6a (see cellular data for 6a below). We were thus less concerned about the SMYD2-associated off-target effect under our assay conditions.

The metabolic stability of 6a was also evaluated with a microsomal stability assay (Figure 5c,e, Figure 5—figure supplement 5, see Materials and methods). In the presence of rat liver microsomes, 6a showed decent stability with 24% residual 6a after one-hour incubation. Here, the conversion of 6a into 5a accounted for 40% of the microsome-processed 6a; no production of 2a was detected. Such observation suggests that NQO1, the putative enzyme candidate to reduce the TML moiety in 6a or 6b, is present in microsomes as well as in tumor cells (Dias et al., 2018; Huang et al., 2016). By contrast, peptidase enzymes that are expected to process 5a into 2a are absent from microsomes but rich in tumor cells.

We then conducted a cellular thermal shift assay (CETSA), in which ligand binding is expected to increase CARM1’s thermal stability in a cellular context (Jafari et al., 2014). Our data showed that the treatment of MDA-MB-231 cells with 6a but with not the control compound 6b increased cellular Tm and thus the thermal stability of CARM1 by 4.3 ± 0.6°C (Figure 5f). The distinct effect of 6a in contrast to 6b on the cellular Tm of CARM1 aligns well with the 4.1‒6.2°C difference in the in vitro Tm of CARM1 upon binding 2a and 5a versus SAM (Figure 2e). Here, 6b can penetrate cell membranes and be processed into 5b and 2b in a similar manner as 6a (Figure 5—figure supplement 6). These observations thus present the cellular evidence to show that CARM1 engages 2a and 5a.

To further characterize 6a as a CARM1 chemical probe, we examined the Arg1064 methylation of BAF155 and the Arg455/Arg460 methylation of PABP1, two well-characterized cellular methylation marks of CARM1, upon treating MDA-MB-231 cells with 6a (Lee and Bedford, 2002; Wang et al., 2014a). These methylation marks can be fully suppressed by 6a in a dose-dependent manner (Figure 6a). The resultant EC50 values of 0.45‒0.75 µM (Figure 6b) are well correlated with the modeled 60% cellular occupancy of CARM1 upon treatment with 0.5 µM 6a for 48 hr (Figure 5e). By contrast, the treatment of the negative control compound 6b showed no effect on these methylation marks (Figure 6a). We therefore demonstrated the robust use of 6a (SKI-73) as a CARM1 chemical probe and of 6b (SKI-73N) as its control compound.

Figure 6 with 1 supplement see all
Biological outcomes of CARM1 inhibition by 6a in MDA-MB-231 cells.

(a) Dose-dependent depletion of BAF155 methylation and PABP1 methylation by 6a. BAF155 methylation and PABP1 methylation, two marks of the CARM1-specific methyltransferase activity, were examined upon the treatment of 6a and its structural analog 6b (negative control compound) for 48 hr. Western Blot analysis was then conducted to quantify the relative intensities of the methylated versus total proteins (BAF155 and PABP1, two replicates with a representative one shown). (b) EC50 of the methylation depletion of BAF155 and PABP1. The relative intensity of the methylated versus total BAF155 or PABP1 was plotted against log[6a], with the resultant EC50obtained upon fitting a standard sigmoid curve using GraphPad Prism. (c) Inhibition of cell invasion by 6a. Representative images of the trans-well migration of MDA-MB-231 cells a shown upon treatment with various concentrations of 6a (SKI-73) or its control compound 6b (SKI-73N) for 16 hr. Invasive cells were fixed and stained with crystal violet. The invasiveness ratios were determined using the relative cell invasion of the treatment of 6a or 6b versus DMSO treatment. (d) EC50 of invasion inhibition by 6a. The invasiveness ratios were plotted as a function of the concentration of 6a. EC50 of 1.3 ± 0.2 µM was obtained upon fitting a standard sigmoid curve using GraphPad Prism. (e) Effect of 6a on cell invasion in combination with CARM1-KO. Representative images of the trans-well migration of parent and CARM1-KO MDA-MB-231 cells are shown upon treatment with DMSO, 6a or 6b for 16 hr. The results were analyzed in a similar manner to that described for Figure 5c,d. Statistical analysis was carried out to calculate mean ± standard derivation (N = 5) and to perform two-tailed paired t-tests *, p=0.05.

Inhibition of in vitro invasion but not proliferation of breast cancer cells by SKI-73 (6a)

After demonstrating the utility of SKI-73 (6a) as a chemical probe for CARM1, we examined whether chemical inhibition of CARM1 can recapitulate biological outcomes that are associated with CARM1 knockout (CARM1-KO) (Wang et al., 2014a) . Our prior work showed that CARM1’s methyltransferase activity is required for invasion of MDA-MB-231 cells (Wang et al., 2014a). We thus conducted a matrigel invasion assay with MDA-MB-231 cells in the presence of 6a. Relative to the control treatment with DMSO, treatment with SKI-73 (6a) but not its negative control compound SKI-73N (6b) suppressed the invasion of MDA-MB-231 cells (EC50 = 1.3 µM) (Figure 6c,d). The treatment with ≥10 µM 6a produced the maximal 80% suppression of the invasion by MDA-MB-231 relative to the DMSO control, which is comparable with the phenotype of CARM1-KO (Figure 6e). Critically, no further inhibition by 6a on the invasiveness was observed upon 6a treatment (in comparison with the treatment with DMSO or 6b treatment) of MDA-MB-231 CARM1-KO cells (Figure 6e). Notably, treatment with 6a and 6b under the current condition has no apparent impact on the proliferation of parental or CARM1-KO MDA-MB-231 cells (Figure 6—figure supplement 1), consistent with the intact proliferation upon treatment with other CARM1 chemical probes (Drew et al., 2017; Greenblatt et al., 2018; Nakayama et al., 2018). These results suggest that SKI-73 (6a) and CARM1 knockout perturb the common, proliferation-independent biological process and then suppresses 80% of the invasiveness of MDA-MB-231 cells. We thus characterized SKI-73 (6a) as a chemical probe that can be used to interrogate the CARM1-dependent invasion of breast cancer cells.

A scRNA-seq and cell-cycle-aware algorithm reveals CARM1-dependent epigenetic plasticity

Because of the advancement of scRNA-seq technology, stunning subpopulation heterogeneity has been uncovered even for well-defined cellular types (Tanay and Regev, 2017). In the context of tumor metastasis, including its initial invasion step, epigenetic plasticity is required to allow a small subset of tumor cells to adapt distinct transcriptional cues for neo-properties (Chatterjee et al., 2018; Flavahan et al., 2017; Wu et al., 2019). To explore the feasibility of dissecting the CARM1-dependent, invasion-prone subset of MDA-MB-231 breast cancer cells, we formulated a cell-cycle-aware algorithm of scRNA-seq analysis and dissected those subpopulations that were sensitive to CARM1 perturbation (Figure 7a, see Materials and methods). Here we conducted 10 × Genomics droplet-based scRNA-seq of 3232, 3583 and 4099 individual cells (a total of 10,914 cells) exposed to 48 hr treatment with SKI-73 (6a), SKI-73N (6b) and DMSO, respectively. Guided by Silhouette analysis, cell-cycle-associated transcripts were identified as dominant signatures of subpopulations (Figure 7—figure supplements 118). These signatures naturally exist for proliferative cells and are not expected to be specific for the invasive phenotype. To dissect the subpopulation-associated transcriptomic signatures of invasive cells, we included one additional layer for hierarchical clustering by first classifying the individual cells into G0/G1, S, and G2/M stages (6885, 1520 and 2509 cells, respectively) (Figure 7—figure supplement 6, Supplementary file 1-Table E), and then conducted the unsupervised clustering within each cell-cycle-aware subset (Figure 7b, Figure 7—figure supplements 1930, Supplementary file 1-Table E). To resolve the subpopulations without redundant clustering, we developed an entropy analysis method and relied on the Fisher Exact test (see Materials and methods). The optimal scores of the combined methods were implemented to determine the numbers of cluster for each subset (Figure 7b, Figure 7—figure supplements 20, 24 and 28) (Butler et al., 2018). The cell-cycle-aware algorithm allowed the clustering of these cells according to the three cell cycle stages under the three treatment conditions and resulted in 21, 7 and 6 subpopulations in G0/G1, S, and G2/M phases, respectively (Figure 7b, Figure 7—figure supplements 21, 25 and 29, Supplementary file 1-Tables F–H). Notably, the 48 hr treatments with SKI-73 (6a) or SKI-73N (6b) had no effect on the cell cycle, as indicated by their comparable cell-cycle distribution patterns (Figure 7—figure supplement 6; Supplementary file 1-Table E), a finding that is consistent with intact proliferation after all three treatments (Figure 6—figure supplement 1).

Figure 7 with 43 supplements see all
scRNA-seq analysis of MDA-MB-231 cells upon CARM1 perturbation.

(a) Schematic description of scRNA-seq analysis algorithms. (b) tSNE plots of the 21 clustered subpopulations of the G0/G1-phase cells treated with SKI-73N (6b), DMSO and SKI-73 (6a). (c) Population analysis of the 21 clustered subpopulations of the G0/G1-phase cells upon treatment with SKI-73N (6b) or SKI-73 (6a). (d) Phylogenic tree of the 21 clustered subpopulations of the G0/G1-phase cells and invasive cells. (e) Violin plots of the representative transcripts of G0/G1-phase invasion cells that distinguish Subpopulation-8 from the closely related Subpopulations 6, 7, 9, and 14 of G0/G1-phase cells. (f) Working modeling of CARM1-dependent epigenetic plasticity perturbed by SKI-73 (6a).

CARM1-associated epigenetic plasticity of breast cancer cells at single-cell resolution

With the 21, 7 and 6 subpopulations clustered into the G0/G1, S, and G2/M stages, respectively, we then conducted population analysis, comparing SKI-73 (6a) and SKI-73N (6b) against DMSO (Figure 7c, Figure 7—figure supplements 31 and 32, Supplementary file 1-Table F–H). These subpopulations can be readily classified into five distinct categories according to their cell-cycle-aware responses to SKI-73 (6a) and SKI-73N (6b) treatment: commonly resistant/emerging/depleted versus differentially depleted/emerging (SKI-73/SKI-73N- or 6a/6b-specific) (Figure 7c, Figure 7—figure supplements 31 and 32, Supplementary file 1-Table F–H). Here, we are particularly interested in the SKI-73(6a)-specific depleted subpopulations (Subpopulation 0/2/8/11/13/14/17/19 of G0/G1-phase cells and 3 of S-phase cells) as the potential invasion-associated subpopulations, given their sensitivity to SKI-73 (6a) but not its control compound SKI-73N (6b). The subpopulations that remain unchanged after the treatment with SKI-73 (6a) or SKI-73N (6b) (Subpopulation 1/7/12/15 of G0/G1-phase cells; 2/4/5 of S-phase cells; 1 of G2/M-phase cells) were defined as the common resistant subset. SKI-73(6a)-specific emerging subpopulations (Subpopulation 3/4/5/6/16 of G0/G1-phase cells; 6 of S-phase cells; 4 of G2/M-phase cells) are expected to be suppressed by CARM1 but emerge upon its inhibition. The remaining subpopulations are either associated with the effects of the small-molecule scaffold of SKI-73 (6a)/SKI-73N (6b) (commonly emerging Subpopulation 9 of G0/G1-phase cells, 0/5 of G2/M-phase cells; commonly depleted Subpopulation 2/3 of G2/M-phase cells) or with SKI-73N (6b)-specific effects (differentially depleted Subpopulation 10/18 of G0/G1-phase cells, 0 of S-phase cells; differentially emerging Subpopulation 20 of G0/G1-phase cells). Interestingly, scRNA-seq analysis of CARM1-KO cells (in comparison with SKI-73 (6a)-treated cells)suggests that CARM1 knockout has more profound effects on the overall landscape of epigenetic plasticity (Figure 7d, Figure 7—figure supplement 33). Collectively, the chemical probe SKI-73 (6a) alters the epigenetic plasticity of MDA-MB-231 breast cancer cells via the combined effects of SKI-73(6a)’s molecular scaffold and specific inhibition of CARM1’s methyltransferase activity.

Identification of CARM1-dependent, invasion-prone subpopulations of breast cancer cells

Given that SKI-73 (6a) has no effect on the cell cycle and proliferation of MDA-MB-231 cells under the current treatment dose and duration, we envision that the invasion capability of MDA-MB-231 cells mainly arises from an invasion-prone subset, 80% of which is depleted by SKI-73 (6a) treatment (Figure 6c–e). We thus focused on Subpopulations 0/2/8/11/13/14/17/19 of G0/G1-phase cells and Subpopulation 3 of S-phase cells: in total nine depleted subpopulations specific for SKI-73 (6a) (Figure 7c, Figure 7—figure supplements 31 and 32, Supplementary file 1-Table F–H). To identify invasion-prone subpopulation(s) among these candidates, we compared the transcriptional signature(s) of these subpopulations with those of cells that freshly invaded through Matrigel within 16 hr. Strikingly, in comparison with the highly heterogenous scRNA-seq signature of the parental MDA-MB-231 cells, the freshly harvested invasive cells (3793 cells for scRNA-seq) are relatively homogeneous, with their subpopulations mainly determined by the cell-cycle-related transcriptomic signatures (Figure 7d, Figure 7—figure supplements 3337). We then classified the freshly harvested invasive cells into G0/G1, S and G2/M stages (Figure 7—figure supplements 3840, Supplementary file 1-Table E). Through correlation analysis comparing the invasive cells and the subpopulations within each cell-cycle stage (Figure 7d, Figure 7—figure supplements 3942), we revealed the subsets whose transcriptional signatures closely relate to those of the invasive cells, including Subpopulations 6/7/8/9/14 in G0/G1-phase cells, 0/3 in S-phase cells and 1/2 of G2/M-phase cells (Supplementary file 1-Table F–K). In the context of population analysis for the nine SKI-73 (6a)-specific depleted subpopulations, Subpopulations 8/14 of G0/G1-phase cells and Subpopulation 3 in S-phase are putative invasion-prone candidates. Subpopulation 8 of G0/G1-phase cells is the most sensitive and the only subpopulation that can be depleted by around 80% with SKI-73 (6a) treatment (Figure 7c). Given the ~80% suppression and ~20% residual invasion capability upon SKI-73 (6a) treatment, we argue that the invasive phenotype of MDA-MB-231 cells predominantly arises from Subpopulation 8 G0/G1-phase cells, which only accounts for ~8% of the parental cells in G0/G1 phase (~5% without cell-cycle awareness). Differential expression analysis further revealed the single-cell transcriptional signatures of metastasis-implicated genes (e.g. MORC4, S100A2, RPL39, IFI27, ARF6, CHD11, SDPR and KRT18) that are specific for the G0/G1-phase Subpopulation 8 and invasive cells but not for other G0/G1-phase invasion-prone candidates such as Subpopulation 6/7/9/14 (Figure 7e, Figure 7—figure supplement 43, Supplementary file 1-Table L). The remaining cells of G0/G1-phase Subpopulation 8 after SKI-73 (6a) treatment (Figure 7c,d) together with others (subpopulation-6/7/9/14 in G0/G1-phase cells, 0/3 in S-phase cells and 1/2 of G2/M-phase cells, Figure 7—figure supplements 31, 32, 41, 42) may account for the 20% residual invasion capacity.

In the context of SKI-73 (6a)-specific depletion of G0/G1-phase subpopulations, there are SKI-73 (6a)-specific emerging G0/G1-phase subpopulations: Subpopulation 3/4/5/6/16 (Figure 7c). Population analysis of G0/G1-phase cells further revealed that Subpopulations 4 and 16 account for 90% of the emerging subset upon SKI-73 (6a) treatment (Supplementary file 1-Table F). The transcriptional signatures and probably the associated invasion capability of Subpopulations 4 and 16 are dramatically different from those of the freshly harvested invasive cells and the bulk population of the parental cells, including the invasion-prone Subpopulation 8 (Figure 7b,d). Collectively, either CARM1 knockout or CARM1 inhibition with SKI-73 (6a) alters the epigenetic plasticity in a proliferation-independent manner by replacing the most invasion-prone subpopulation with the non-invasive subpopulation(s) to suppress the invasive phenotype (Figure 7f).

Discussion

Chemical probes of CARM1

On the basis of a novel small-molecule scaffold, 6′-homosinefungin (HSF), SKI-73 (6a) was developed as a pro-drug-like chemical probe for CARM1 by cloaking the 9′-amine moiety of 5a with the TML moiety. SKI-73N (6b) was developed as a control compound for SKI-73 (6a). The inhibitory activity of SKI-73 (6a) against CARM1 was demonstrated by the ability of SKI-73 (6a) but not SKI-73N (6b) to abolish the cellular methylation marks of CARM1: the Arg1064 methylation of BAF155 and the Arg455/Arg460 methylation of PABP1 (Lee and Bedford, 2002; Wang et al., 2014a). The ready intracellular cleavage of TML is expected for the conversion of SKI-73 and SKI-73N (6a and 6b) into 5a and 5b, respectively, but it is remarkable that SKI-73 and SKI-73N (6a and 6b) can also be efficiently processed into 2a and 2b inside cells. In comparison, 6a showed decent metabolic stability with no production of 2a in the presence of microsomes. Here, 2a and 5a are presented as potent and selective CARM1 inhibitors, whereas their control compounds 2b and 5b interact poorly with CARM1.

Competitive assays with the SAM cofactor and the peptide substrate showed that 2a and 5a act on CARM1 in a SAM-competitive and substrate-noncompetitive manner. The SAM-competitive mode is consistent with the ligand–complex structures of CARM1, in which the SAM binding site is occupied by 2a and 5a. Strikingly, as revealed by their ligand–CARM1 complex structures, 2a and 5a engage CARM1 through noncanonical modes, with their 6′-N-benzyl moieties in the binding pocket that is otherwise occupied by the α-amino carboxylate moiety of conventional SAM analogs such as SAH, SNF and 1. This observation is consistent with the 4.1‒6.5 °C increase in the in vitro and cellular Tm of CARM1 upon binding 2a and 5a, which contrasts with the smaller Tm changes with SAM as a ligand. The distinct modes of interaction of CARM1 with 2a and 5a (Figure 3c,g) also rationalize the CARM1 selectivity of the two SAM analogs over other methyltransferases, including closely related PRMT homologs. Through mathematic modeling using the inputs of the LC-MS/MS-quantified intracellular concentrations and CARM1-binding constants of relevant HSF derivatives and the SAM cofactor, we concluded that high intracellular concentrations of 5a and 2a, and thus efficient CARM1 occupancy, can be achieved rapidly and maintained for several days with a single low dose of SKI-73 (6a). By contrast, the occupancy by 5a and 2a of SMYD2, the next likely engaged target, is below 20% with the efficacious doses of 6a that affect cell invasion. The polar α-amino acid zwitterion moiety of 2a and the polar α-amino moiety of 5a probably account for their accumulation and retention inside cells.

To the best of our knowledge, EZM2302, TP-064, and SKI-73 (also 6a in this work, www.thesgc.org/chemical-probes/SKI-73) and their derivatives are the only selective and cell-active CARM1 inhibitors (Drew et al., 2017; Nakayama et al., 2018). Although the potency, selectivity, on-target engagement and potential off-target effects associated with these compounds have been examined in vitro and in cellular contexts as chemical probes, EZM2302, TP-064, and SKI-73 (6a) are differentiated by their molecular scaffolds and modes of interaction with CARM1 (www.thesgc.org/chemical-probes/SKI-73) (Drew et al., 2017; Nakayama et al., 2018). SKI-73 (6a) is a cofactor analog inhibitor embedding a N6'-homosinefungin moiety to engage the SAM binding site of CARM1 in a cofactor-competitive, substrate-noncompetitive manner; EZM2302 and TP-064 occupy the substrate-binding pocket of CARM1 in a SAH-uncompetitive or SAM-noncompetitive manner (Drew et al., 2017; Nakayama et al., 2018). In particular, the prodrug property of SKI-73 (6a) allows its ready cellular uptake, followed by rapid conversion into its active forms inside cells. The prolonged intracellular CARM1 inhibition further distinguishes SKI-73 (6a) from EZM2302 and TP-064.

Anti-cancer effects and conventional mechanisms associated with pharmacological inhibition of CARM1

With SKI-73 (6a) as a CARM1 chemical probe and SKI-73N (6b) as a control compound, we showed that pharmacological inhibition of CARM1 with SKI-73 (6a), but not SKI-73N (6b), suppressed 80% of the invasion capability of MDA-MB-231 cells. By contrast, the pharmacological inhibition of CARM1 with SKI-73 (6a) had no effect on the proliferation of MDA-MB-231 cells. This result is consistent with the lack of anti-proliferation activities of the other two CARM1 chemical probes, EZM2302 and TP-064, against breast cancer cell lines (Drew et al., 2017; Nakayama et al., 2018). The anti-invasion efficiency of SKI-73 (6a) is in good agreement with the intracellular occupancy and the resulting abolition of several methylation marks of CARM1 upon treatment with SKI-73 (6a). Our prior work showed that the methyltransferase activity of CARM1 is required for breast cancer metastasis (Wang et al., 2014a). Among the diverse cellular substrates of CARM1 (Blanc and Richard, 2017), BAF155—a key component of the SWI/SNF chromatin-remodeling complex–is essential for the invasion of MDA-MB-231 cells (Wang et al., 2014a). Mechanistically, the CARM1-mediated Arg1064 methylation of BAF155 facilitates the recruitment of the SWI/SNF chromatin-remodeling complex to a specific subset of gene loci (Wang et al., 2014a). Replacement of the native CARM1 with its catalytically dead mutant or with an Arg-to-Lys point mutation at the Arg1064 methylation site of BAF155 is sufficient to abolish the invasive capability of breast cancer cells (Wang et al., 2014a). CARM1 inhibition with SKI-73 (6a), but not with its control compound SKI-73N (6b), recapitulates the anti-invasion phenotype associated with the genetic perturbation of CARM1. More importantly, there is no additive effect upon combining CARM1-KO with SKI-73 (6a) treatment, underlying the fact that the two orthogonal approaches target the commonly shared pathway(s) that are essential for the invasion of breast cancer cells. In comparison to SKI-73 (6a), the CARM1 inhibitors EZM2302 and TP-064 demonstrated anti-proliferation effects on hematopoietic cancer cells, in particular multiple myeloma (Drew et al., 2017; Greenblatt et al., 2018; Nakayama et al., 2018). Mechanistically, genetic perturbation of CARM1 in the context of leukemia impairs cell-cycle progression, promotes myeloid differentiation, and ultimately induces apoptosis, probably by targeting pathways of proliferation and cell-cycle progression, that is, E2F-, MYC-, and mTOR-regulated processes (Greenblatt et al., 2018). In comparison, CARM1 inhibition with EZM2302 led to a slightly different phenotype, which includes reduction of RNA stability, E2F target downregulation, and induction of a p53 response signature for senescence. (Greenblatt et al., 2018). Collectively, the effects of CARM1 chemical probes are highly context-dependent, with SKI-73 (6a) having different uses in impairing the invasiveness of breast cancer cells, while TP-064 and EZM2302 have uses in preventing the proliferation of hematopoietic cancer cells.

CARM1-dependent epigenetic plasticity revealed by SKI-73 (6a) with single-cell resolution

Given the increased awareness of epigenetic plasticity (Flavahan et al., 2017), we employed the scRNA-seq approach to examine MDA-MB-231 cells and their responses to chemical and genetic perturbation with CARM1. Because of the lack of a prior reference to define subpopulations of MDA-MB-231 cells, we developed a cell-cycle-aware algorithm to cluster the subpopulations with a resolution that was able to dissect subtle changes upon treatment with SKI-73 (6a) versus its control compound SKI-73N (6b) in each cell-cycle stage. Guided by Silhouette analysis, the population entropy analysis and the Fisher Exact test, >10,000 MDA-MB-231 breast cancer cells were classified on the basis of their cell-cycle stages and then clustered into 34 subpopulations. With further annotation of these subpopulations according to their different responses to treatment with SKI-73 (6a) versus SKI-73N (6b), we readily dissected the subpopulations that were altered in a SKI-73(6a)-specific (CARM1-dependent) manner and then identified subsets with transcriptional signatures that are similar to that of the freshly isolated invasive cells. Quantitative analysis of SKI-73 (6a)-depleted subpopulations further revealed the most invasion-prone subpopulation, which accounts for only 5% of the total population but at least 80% of the invasive capability of the parental cells. Collectively, we propose a model in which MDA-MB-231 cells consist of subpopulations, with their epigenetic plasticity (Figure 7f) determined by multiple factors including the CARM1-involved BAF155 methylation (Wang et al., 2014a). SKI-73 (6a) inhibits the methyltransferase activity of CARM1, the Arg1064 methylation of BAF155, and thus the target genes associated with the methylated BAF155. These effects alter the cellular epigenetic landscape by affecting certain subpopulations of MDA-MB-231 cells without any apparent effect on cell cycle and proliferation. In the context of the invasion phenotype of MDA-MB-231 cells, the subset of invasion-prone cells is significantly suppressed upon the treatment with SKI-73 (6a). Essential components that are used to dissect the invasion-prone population in this CARM1-dependent epigenetic plasticity model are the scRNA-seq analysis of sufficient MDA-MB-231 cells (>10,000 cells here), the utility of the freshly isolated invasive cells as the reference, the timing and duration of treatment, and the use of SKI-73N (6b) and DMSO as controls. Interestingly, although the invasion-prone subpopulation is also abolished in the CARM1-KO strain, CARM1-KO reshapes the epigenetic plasticity in a much more profound manner, significantly reducing the subpopulation heterogeneity of MDA-MB-231 cells. The distinct outcomes for the pharmacological and genetic perturbation could be due to their different modes of action: short-term treatment with SKI-73 (6a) versus long-term clonal expansion of CARM1-KO cells. The pharmacological inhibition captures the immediate response, whereas the genetic perturbation reports long-term and potential resistant outcomes. This work thus presents a new paradigm to understand cancer metastasis in the context of epigenetic plasticity and provides guidance for similar analyses in broader contexts: other cell lines, patient-derived xenograft samples, and in vivo mouse models of breast cancer.

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional
information
Gene (Homo sapiens)Human CARM1 catalytic domainUniProtKB/Swiss-Prot:Q86 × 55.3 (positions140–480)With an N-terminal6 × His tag in
pFBOH-MHL, for crystallization
Cell line (Homo sapiens, female)MDA-MB-231 (female)ATCC
Cell line (Homo sapiens, female)MCF-7
(female)
ATCC
Cell line (Homo sapiens, female)MDA-MB-231; CARM1-KOWang et al., 2014a
Cell line (Homo sapiens, female)MCF-7; CARM1-KOWang et al., 2014a
Software, algorithmREFMACPMID: 15299926
Software, algorithmSEquence Quality Controlhttps://github.com/ambrosejcarr/seqc.git
Software, algorithmfisher.testhttp://mathworld.wolfram.com/FishersExactTest.html
Software, algorithmBuildClusterTreehttps://rdrr.io/cran/Seurat
Software, algorithmFindMarkershttps://rdrr.io/cran/Seurat
Software, algorithmDoHeatmaphttps://rdrr.io/cran/Seurat

Synthesis and characterization of 3, 1, 2a, 5a, 6a, 2b, 5b and 6b

Abbreviations in chemical structures

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Ac, acetyl; Bn, benzyl; Bz, benzoyl; Cbz, benzyloxycarbonyl; TFA, trifluoroacetyl.

Abbreviations of chemical reagents

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CbzCl, benzyl chloroformate; DCM, dichloromethane; DMF, N, N-dimethylformamide; HATU, 1-[bis(dimethylamino)methylene]−1 H-1,2,3-triazolo[4,5-b] pyridinium 3-oxid hexafluorophosphate; TFA, trifluoroacetic acid; THF, tetrahydrofuran; TML-NHS, N-hydroxysuccinimidyl ester 3-methyl-3-(2,4,5-trimethyl-3,6-dioxocyclo-hexa-1,4-dienyl)-butanoic acid; SAH, S-adenosyl homocysteine.

General experimental information

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Reagents for chemical reactions were purchased from Sigma-Aldrich without purification unless mentioned otherwise. Anhydrous solvents were prepared from a solvent purification system (PURE SOLV, Innovative Technology, Inc). Chemical reactions were carried out in an argon atmosphere at the temperatures displayed by the thermocouple or at ambient temperature (22°C) unless described otherwise. The phrase ″concentrated″ in the synthetic method session refers to the reaction workup to remove volatile solvents that uses a rotary evaporator attached to a diaphragm pump (15–20 Torr) and then a high vacuum pump (<1 Torr). Chromatography purification was carried out with silica gel from Dynamic Adsorbents, Inc (neutral, 32–63 µm). NMR spectra were recorded on Bruker AVIII 600MHz spectrometers and reported in terms of chemical shifts (ppm), multiplicities (s = singlet, d = doublet, t = triplet, q = quartet, p=pentet, m = multiplet, and br = broad), and integration and coupling constants (J in Hz). Chemical shifts were recorded with residual proton peaks of deuterated solvents as references (residual 1H of DMSO, 2.50 ppm; CD3OD, 3.31 ppm; D2O, 4.80 ppm; 13C of DMSO, 39.52 ppm; CD3OD, 49.00 ppm). 1H-NMR spectra were recorded at 24.0°C or at 70.0°C. 1H-NMR spectra at 70.0°C were recorded in DMSO-d6 to facilitate the equilibrium between rotamers. 13C-NMR spectra were recorded at 24°C. Mass spectra for compound characterization were collected by Waters Acuity SQD LC-MS in electron spray ionization (ESI) mode. The final concentrations of the stock solutions of 3, 1, 2a, 5a, 5b, SAH and nonradioactive SAM were determined on the basis of their UV absorption at 260 nm (ε260 = 15,400 L.mol−1.cm−1) using a Nanodrop 1000 Spectrophotometer (Thermo Scientific). The final concentrations of 6a and 6b were determined with 1H-NMR in CD3OD containing 1.0 mM SAH as an internal reference for the first time and then with a Nanodrop 1000 Spectrophotometer (Thermo Scientific) on the basis of their UV absorption at 267 nm (ε267 = 19,300 L.mol−1.cm−1) thereafter. CD3-SAM was prepared as described previously (Linscott et al., 2016).

Synthesis of 3

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Compound 3 was prepared as previously reported (Wu et al., 2016). Briefly, N6-benzoyladenine (44 mg, 0.18 mmol), hexamethyldisilazane (3 mL) and pyridine (1 mL) were added into an oven-dried flask. The suspension was heated at 115°C to produce a clear solution and stirred for another 3 hr. The mixture was concentrated under reduced pressure. The residual was dried using azeotrope with toluene (3 × 5 mL) and over high vacuum. A solution of triacetate derivative (0.037 mmol) in 15 mL 1,2-dichloroethane to the solid crude of bis-sily-N6-benzoyladenine. The resultant suspension was treated with TMSOTf (33 µL, 0.18 mmol) and heated at 50°C for 2 hr. The mixture was quenched with 10 mL saturated NaHCO3, followed by extraction with 3 × 20 mL CH2Cl2. The combined organic phases were washed with brine, dried with anhydrous Na2SO4, and evaporated to produce 56 mg crude of 3. Flash silica gel chromatography with MeOH/DCM = 30:1 produced 30 mg of 3 as a white solid (92% yield).

3: 1H-NMR (600 MHz, DMSO-d6, 84°C): δ 10.76(s, 1H), 9.39(d, 1H, J = 6.4), 8.71(s, 1H), 8.59(s, 1H), 8.04(d, 2H, J = 7.6), 7.63(t, 1H, J = 7.4), 7.54(t, 2H, J = 7.6), 7.22–7.31(m, 8H), 7.12–7.13(m, 2H), 6.23(d, 1H, J = 5.6), 6.04(t, 1H, J = 5.6), 5.41(t, 1H, J = 5.6), 5.10(d, 2H, J = 5.5), 4.45(d, 1H, J = 15.6), 4.39(d, 1H, J = 15.6), 4.24–4.28(m, 1H), 4.15–4.18(m, 1H), 3.66(s, 3H), 3.23–3.26(m, 1H), 3.13–3.16(m, 1H), 2.10(s, 3H), 2.04(s, 3H), 1.92–1.95(m, 1H), 1.81–1.88(m, 2H), 1.75–1.80(m, 1H), 1.64–1.67(m, 1H), 1.31–1.39(m, 2H); 13C-NMR (150 MHz, DMSO-d6 rotamers): δ 170.92, 169.60, 169.51, 169.40, 165.67, 156.59(q, J = 36.4), 155.87, 151.85, 151.80, 150.71, 143.88, 137.85, 136.77, 133.23, 132.57, 128.54, 128.52, 128.43, 128.32, 128.29, 127.79, 127.51, 127.39, 127.28, 127.12, 126.90, 126.01, 115.77(q, J = 286.1), 85.85, 85.75, 79.17, 73.17, 71.89, 66.52, 66.38, 54.94, 52.82, 34.25, 32.81, 32.41, 30.72, 27.48, 26.70, 20.42, 20.38, 20.25; MS(ESI) m/z: 940 ([M+Na]+; HRMS: calculated for C45H47N7O11F3 ([M+H] +)918.3286, found 918.3311.

Synthesis of 1 and 2a from 3

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To a solution of 3 (45 mg, 0.050 mmol) in 15 mL CF3CH2OH, we added Pd/C (22 mg, 10 wt. %, wet support, Degussa type). This mixture was stirred under H2 (one atm) at ambient temperature (22°C) for 36 hr (Bailey et al., 2008). The reaction mixture was filtered through a short pad of Celite, followed by washing with 150 mL of MeOH. The combined filtrate was concentrated to produce around 1:1 crude mixture of S1 and S2 (Figure 1—figure supplement 1). This crude material was dissolved in 2 mL of MeOH and then mixed with 1 mL of 0.2 M LiOH. The resultant mixture was stirred at ambient temperature (22°C) for 40 hr, neutralized with 0.2 M HCl to reach pH = 7, and concentrated under reduced pressure to produce the crude product mixture of 1 and 2a. The crude products were then subjected to a preparative reversed-phase HPLC (XBridge Prep C18 5 μm OBD 19 × 150 mm) with 5–95% gradient of CH3CN in aqueous trifluoroacetic acid (TFA, vol. 0.1%) in 24 min with a flow rate of 10 mL/min to afford 6.5 mg of 1 (27% yield over two steps) and 7.5 mg of 2a (31% yield over two steps) as white solids. (See Figure 1—figure supplement 1).

1: 1H-NMR (600 MHz, D2O): δ 8.36(s, 1H), 8.35(s, 1H), 6.01(d, 1H, J = 3.7), 4.65 (dd, 1H, J = 3.7, 5.4), 4.36(t, 1H, J = 5.9), 4.22–4.19 (m, 1H), 4.03–3.99 (m, 1H), 3.82(t, 1H, J = 6.0), 3.05 (dd, 1H, J = 5.4 12.8), 2.96 (dd, 1H, J = 6.9, 12.8), 1.97–1.84 (m, 5H), 1.49–1.55(m, 2H); 13C-NMR (150 MHz, D2O): δ 173.33, 150.08, 148.17, 144.67, 142.76, 119.02, 115.28, 88.81, 81.18, 73.35, 53.79, 41.94, 33.65, 33.12, 26.88, 25.84; HRMS: calculated for C16H26N7O5 ([M+H]+) 396.1995, found: 396.1982.

2a: 1H-NMR (600 MHz, CD3OD): δ 8.16(s, 1H), 8.09(s, 1H), 7.25(d, 1H, J = 7.4), 7.20(t, 2H, J = 7.4), 7.12(t, 2H, J = 7.4), 5.84(d, 1H, J = 4.0), 4.49(dd, 1H, J = 5.4, 4.0), 4.08 (t, 1H, J = 5.7), 4.04(d, 1H, J = 13.0), 3.98–4.00(m, 1H), 3.95(d, 1H, J = 13.0), 3.82(t, 1H, J = 7.0), 2.94–3.00(m, 1H), 1.92–2.03(m, 2H), 1.83–1.91(m, 2H), 1.76–1.80(m, 1H), 1.46–1.56(m, 2H); 13C-NMR (150 MHz, CD3OD): δ 171.94, 155.85, 151.31, 150.18, 142.68, 132.10, 130.90 (2×13C), 130.83, 130.37 (2×13C), 121.19, 91.65, 82.07, 75.36, 74.88, 54.07, 53.01, 52.07, 34.69, 34.32, 28.72, 27.90. HRMS: calculated for C23H32N7O5 ([M+H]+) 486.2465, found:486.2464.

Synthesis of 5a and 6a (SKI-73) from 3

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To a solution of 3 (91 mg, 0.10 mmol) in 20 mL MeOH, we added 10 mL of 0.2 M LiOH. The resultant mixture was stirred at ambient temperature (22°C) for 40 hr. The reaction mixture was then neutralized with 0.2 M HCl to reach pH = 7.0 and concentrated under reduced pressure to produce 4 without further purification. The crude product 4 was then dissolved in 10 mL of THF and then mixed with 3 mL of saturated aqueous NaHCO3 and CbzCl (12 μL, 0.10 mmol) at 0°C. This mixture was stirred at 0°C for 3 hr, quenched with 0.2 M HCl to reach pH = 7.0 and concentrated under reduced pressure. The resultant solid was washed with 100 mL THF, then filtered and concentrated to afford 65 mg of the crude product S3 without further purification. To a solution of S3 in 8 mL DMF, we sequentially added HATU (114 mg, 0.3 mmol), 2,3,5-collidine (39 μL, 0.3 mmol) and 4-methoxyphenethylamine (44 μL, 0.3 mmol) (Han and Kim, 2004). The resultant mixture was stirred at ambient temperature (22°C) under argon until the starting material S3 was fully consumed as monitored by LC-MS. The reaction was then quenched with 3 mL of saturated aqueous NH4Cl, followed by extraction with 3 × 30 mL DCM. The combined organic layers were washed with brine, dried with anhydrous Na2SO4, filtered, and evaporated to give the crude solid product S4. This crude product was purified by a flash silica gel chromatography (v/v 1:12, MeOH/DCM) to give 55 mg of S4 as a white solid (62% yield over three steps). (See Figure 1—figure supplement 2).

S4: 1H-NMR (600 MHz, DMSO-d6, 70°C): δ 8.28 (s, 1), 8.24 (s, 1), 7.53 (br, 1), 7.35–7.20 (m, 13), 7.11 (d, 2, J = 7.02), 7.07 (d, 2, J = 8.52), 6.81 (d, 2, J = 8.52), 5.86 (d, 1, J = 5.03), 5.10 (d, 1, J = 12.9), 5.08 (d, 1, J = 12.9), 5.03 (s, 2), 4.60 (t, 1, J = 5.03), 4.47 (d, 1, J = 16.2), 4.38 (d, 1, J = 16.2), 3.98 (t, 1, J = 5.03), 3.93–3.87 (m, 2), 3.69 (s, 3), 3.53–3.50 (m, 2), 3.16–3.10 (m, 2), 2.63 (t, 2, J = 7.14), 1.96–1.85 (m, 1), 1.71–1.58 (m, 3), 1.50–1.41 (m, 1), 1.39–1.32 (m, 1), 1.28–1.20 (m, 1). 13C NMR (150 MHz, DMSO-d6, rotamers): δ 172.05 (15), 157.93, 156.47, 156.41, 156.23, 153.04, 149.04, 141.88, 138.18, 137.26 (2 × C), 131.48, 129.53 (2 × C), 128.80 (2 × C), 128.72 (2 × C), 128.70 (2 × C), 128.67, 128.19, 128.13, 128.03, 127.98, 127.68 (2 × C), 127.55, 127.17, 119.29, 113.99 (2 × C), 88.48, 82.88 (d), 73.86, 73.17, 66.82 (d), 65.80, 55.38, 55.23, 50.40 (2 × C) 40.62 (2 × C), 35.16, 34.46, 33.72, 33.37, 29.25(d), 28.03 (d). HRMS: calculated for C48H55N8O9 ([M+H+]) 887.4092, found: 887.4082.

To a solution of S4 (20 mg, 0.022 mmol) in 20 mL CF3CH2OH, we added Pd/C (10 mg, 10 wt. %, wet support, Degussa type). The resultant mixture was stirred under H2 (1 atm) at ambient temperature (22°C) until the starting material S4 was fully consumed as monitored by LC-MS. The reaction mixture was filtered through a short pad of Celite, followed by washing with 150 mL of MeOH. The combined filtrate was concentrated under reduced pressure to give the crude product 5a. This crude material is further subject to a preparative reversed-phase HPLC (XBridge Prep C18 5 μm OBD 19 × 150 mm) with 5–95% gradient (volume ratio) of CH3CN in aqueous trifluoroacetic acid (TFA, vol. 0.1%) in 24 min with a flow rate of 10 mL/min to afford 5.4 mg of 5a as a white solid (40% yield).

5a: 1H-NMR (600 MHz, CD3OD): δ 8.31 (s, 1), 8.21 (s, 1), 7.36 (t, 1, J = 7.32), 7.30 (t, 2, J = 7.32), 7.25 (d, 2, J = 7.32), 7.09 (d, 2, J = 8.58), 6.82 (d, 2, J = 8.58), 5.96 (d, 1, J = 3.84), 4.56 (dd, 1, J = 3.84, 5.28), 4.17–4.14 (m, 2), 4.09–4.03 (m, 2), 3.76–3.72 (m, 1), 3.78 (s, 3), 3.53–3.48 (m, 1), 3.40–3.34 (m, 1), 3.03–2.97 (m, 2), 2.78–2.69 (m, 2), 2.09–2.05 (m, 1), 2.05–1.99 (m, 1), 1.91–1.82 (m, 2), 1.74–1.68 (m, 1), 1.48–1.43 (m, 1), 1.41–1.35 (m, 1). 13C NMR (150 MHz, CD3OD): δ 169.72, 159.80, 154.63, 149.98, 149.65, 142.95, 132.08, 132.05, 130.81 (2 × C), 130.78 (2 × C), 130.69, 130.22 (2 × C), 121.00, 115.03 (2 × C), 91.51, 82.20, 75.30, 74.90, 57.63, 55.74, 54.32, 52.88, 42.17, 36.65, 34.59, 34.37, 29.72, 27.64. HRMS: calculated for C32H43N8O5 ([M+H+]) 619.3356, found: 619.3326.

To a solution of 5a (6 mg, 0.01 mmol) in 1 mL anhydrous DMF was added Et3N (12 μL, 0.05 mmol) and N-hydroxysuccinimidyl ester 3-methyl-3-(2,4,5-trimethyl-3,6-dioxocyclo-hexa-1,4-dienyl) butanoic acid (TML-NHS ester) (3.5 mg, 0.01 mmol) at 0°C (Rohde et al., 2006). The resultant mixture was stirred under argon for 8 hr, quenched with 2 mL of saturated aqueous NH4Cl solution and then concentrated under reduced pressure. The resultant product 6a was purified a preparative reversed-phase HPLC (XBridge Prep C18 5 μm OBD 19 × 150 mm) with 5–95% gradient (volume ratio) of CH3CN in aqueous trifluoroacetic acid (TFA, vol. 0.1%) in 24 min with a flow rate of 10 mL/min to afford 2.8 mg of 6a as a yellow solid (33% yield).

6a: 1H-NMR (600 MHz, CD3OD): δ 8.26 (s, 1), 8.19 (s, 1), 7.38–7.31 (m, 3), 7.26–7.24 (m, 2), 7.08 (d, 2, J = 8.40), 6.82 (d, 2, J = 8.40), 5.93 (d, 1, J = 4.50), 4.62 (t, 1, J = 4.50), 4.15–4.05 (m, 5), 3.76 (s, 3), 3.25–3.20 (m, 2), 3.05 (dd, 1, J = 6.00, 6.90), 2.99 (dd, 1, J = 6.00, 6.90), 2.90 (d, 1, J = 14.8), 2.79 (d, 1, J = 14.8), 2.67 (t, 2, J = 7.20), 2.08 (s, 3), 2.07–2.00 (m, 1), 2.08–1.98 (m, 2), 1.94 (s, 3), 1.91 (s, 3), 1.92–1.82 (m, 1), 1.78–1.72 (m, 1), 1.41 (s, 3), 1.39 (s, 3), 1.38–1.29 (m, 2). 13C NMR (150 MHz, CD3OD): δ 192.12, 188.69, 174.46, 173.71, 159.76, 155.11, 154.3, 149.97, 149.24, 144.93, 143.15, 138.81, 138.71, 132.23, 132.09, 130.87 (2 × C), 130.83 (2 × C), 130.76, 130.27 (2 × C), 120.99, 114.97 (2 × C), 91.30, 82.60, 75.32, 74.89, 55.70, 54.22, 52.86, 51.80, 49.58, 42.09, 39.56, 35.52, 35.35, 33.99, 29.58, 29.27, 29.21, 28.70, 14.42, 12.93, 12.04. HRMS: calculated for C46H59N8O8 ([M+H+]) 851.4456, found: 851.4431.

Synthesis of 5b and 6b (SKI-73N) from S5

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Compound S5 was prepared as described previously (Zheng et al., 2012). Into a solution of S5 (94 mg, 0.10 mmol) in 20 mL MeOH solution, we added 10 mL of 0.2 M LiOH. The resultant mixture was stirred at ambient temperature (22°C) for 20 hr. The mixture was then neutralized with 0.2 M HCl to reach pH = 7 and concentrated under reduced pressure to afford the crude product S6 without further purification. To a solution of S6 in 8 mL anhydrous DMF, we sequentially added HATU (114 mg, 0.3 mmol), 2,3,5-collidine (39 μL, 0.3 mmol) and 4-methoxyphenethylamine (44 μL, 0.3 mmol). The resultant mixture was stirred at ambient temperature (22°C) under argon until the starting material S6 was fully consumed as monitored by LC-MS. The reaction was then quenched with 3 mL of saturated aqueous NH4Cl, followed by the extraction with 3 × 30 mL DCM. The combined organic layers were washed with brine, dried with anhydrous Na2SO4, filtered, and evaporated to give the crude product S7. This crude product was purified by a flash silica gel chromatography (v/v 1:12, MeOH/DCM) to afford 61 mg of S7 as a white solid (71% yield). (See Figure 1—figure supplement 3).

S7: 1H-NMR (600 MHz, DMSO-d6, 70°C): δ 8.29 (s, 1), 8.24 (s, 1), 7.50–7.47 (br, 1), 7.37–7.33 (m, 2), 7.28–7.20 (m, 12), 7.19–7.17 (m, 1), 7.07 (d, 2, J = 8.58), 6.81 (d, 2, J = 8.58), 5.84 (d, 1, J = 5.04), 5.07 (s, 2), 5.03 (d, 1, J = 12.7), 5.00 (d, 1, J = 12.7), 4.61 (t, 1, J = 5.04), 4.42 (d, 1, J = 15.8), 4.32 (d, 1, J = 15.8), 3.94 (t, 2, J = 5.04), 3.84–3.81 (m, 2), 3.70 (s), 3.25–3.20 (m, 2), 2.60 (t, 2, J = 7.14), 2.09–2.01 (m, 1), 1.85–1.82 (m, 1), 1.67–1.58 (m, 1), 1.56–1.46 (m, 2), 1.42–1.33 (m, 1). 13C NMR (150 MHz, DMSO- d6, rotamers): δ 171.45 (d), 157.57, 156.55, 155.81, 155.77, 152.22 (d), 148.55, 148.60, 141.95, 139.11, 137.01, 136.69 (d), 131.16, 129.53 (2 × C), 128.30 (2 × C), 128.22 (2 × C), 128.18 (2 × C), 128.08 (2 × C), 127.76, 127.64 (2 × C), 127.26, 127.05, 126.96, 126.71, 119.10, 113.62 (2 × C), 88.06, 81.56 (d), 73.52 (2 × C), 73.09 (2 × C), 66.47 (d), 65.35, 54.87, 54.84, 54.65, 40.38, 36.66 (d), 34.17, 29.47, 29.33. HRMS: calculated for C47H53N8O9 ([M+H+]) 873.3936, found: 873.3928.

To a solution of S7 (20 mg, 0.022 mmol) in 20 mL CF3CH2OH, we added Pd/C (10 mg, 10 wt. %, wet support, Degussa type). This mixture was stirred under H2 (1 atm) at ambient temperature (22°C) until the starting material S7 was fully consumed as monitored by LC-MS. The reaction mixture was then filtered through a short pad of Celite, followed by washing with 150 mL of MeOH. The combined filtrate was concentrated under reduced pressure and purified with a preparative reversed-phase HPLC (XBridge Prep C18 5 μm OBD 19 × 150 mm) with 5–95% gradient (volume ratio) of CH3CN in aqueous trifluoroacetic acid (TFA, vol. 0.1%) in 24 min with a flow rate of 10 mL/min to afford 5.4 mg of 5b as a white solid (40% yield).

5b: 1H-NMR (600 MHz, CD3OD): δ 8.28 (s), 8.14 (s), 7.30 (t, 1, J = 7.50), 7.21 (t, 2, J = 7.50), 7.13 (d, 2, J = 8.58), 7.08 (d, 2, J = 7.50), 6.83 (d, 2, J = 8.58), 5.96 (d, 1, J = 3.96, H1), 4.69 (dd, 1, J = 3.96, 5.88), 4.40 (t, 1, J = 5.88), 4.18–4.21 (m, 1), 4.10 (d, 1, J = 12.84), 4.05 (d, 1, J = 12.84), 3.85–3.83(m, 1), 3.72 (s, 3), 3.47–3.43 (m, 3), 2.79–2.74 (m, 2), 2.36 (ddd, 1, J = 4.32, 9.42, 15.1), 2.25 (ddd, 1, J = 3.00, 6.00, 15.1), 1.95–1.89 (m, 2), 1.85–1.80 (m, 2). 13C NMR (150 MHz, CD3OD): δ 169.29, 159.87, 156.45, 152.48, 150.06, 142.33, 132.00, 130.74 (2 × C), 130.58, 130.44 (2 × C), 130.10 (3 × C), 121.06, 115.05 (2 × C), 91.72, 80.78, 74.63, 74.12, 56.94, 55.68, 53.93, 49.50, 42.37, 35.47, 32.25, 28.71, 26.46. HRMS: calculated for C31H41N8O5 ([M+H+]) 605.3200, found: 605.3180.

To a solution of 5b (6 mg, 0.01 mmol) in 1 mL anhydrous DMF, we added Et3N (12 μL, 0.05 mmol) and N-hydroxysuccinimidyl ester 3-methyl-3-(2,4,5-trimethyl-3,6-dioxocyclo-hexa-1,4-dienyl) butanoic acid (TML-NHS ester) (3.5 mg, 0.01 mmol) at 0°C. The resultant mixture was stirred under argon for 8 hr, quenched with 2 mL of saturated aqueous NH4Cl solution and then concentrated under reduced pressure. The resultant crude product was purified a preparative reversed-phase HPLC (XBridge Prep C18 5 μm OBD 19 × 150 mm) with 5–95% gradient (volume ratio) of CH3CN in aqueous trifluoroacetic acid (TFA, vol. 0.1%) in 24 min with a flow rate of 10 mL/min to afford 3.0 mg of 6b (SKI-73N) as a yellow solid (35% yield).

6b (SKI-73N)
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1H-NMR (600 MHz, CD3OD): δ 8.31 (s, 1), 8.18 (s, 1), 7.32 (t, 1, J = 7.38), 7.24 (t, 2, J = 7.38), 7.11–7.07 (m, 4), 6.82 (d, 2, J = 8.40), 5.96 (d, 1, J = 3.78), 4.69 (dd, 1, J = 3.78, 5.94), 4.35 (t, 1, J = 5.94), 4.19–4.16 (m, 2), 4.13 (d, 1, J = 12.9), 4.04 (d, 1, J = 12.9), 3.74 (s, 3), 3.44–3.40 (m, 1), 3.37–3.30 (m, 2), 2.92 (d, 1, J = 14.9), 2.78 (d, 1, J = 14.9), 2.68 (t, 2, J = 7.08), 2.38–2.32 (m, 1), 2.25–2.20 (m, 1), 2.08 (s, 3), 1.94 (s, 3), 1.91 (s, 3), 1.82–1.78 (m, 2), 1.74–1.70 (m, 1), 1.57–1.54 (m, 1), 1.40 (s, 6). 13C NMR (150 MHz, CD3OD): δ 192.14, 188.66, 174.51, 173.17, 159.79, 155.66, 155.10, 151.29, 150.04, 144.95, 142.82, 138.79, 138.73, 132.22, 130.79 (3 × C), 130.61, 130.43 (2 × C), 130.13 (2 × C), 121.07, 114.98 (2 × C), 91.65, 81.14, 74.84, 74.24, 57.62, 55.68, 53.77, 49.50 (2 × C), 42.18, 39.57, 35.56, 32.85, 29.29, 29.17, 29.10, 27.76, 14.42, 12.94, 12.04. HRMS: calculated for C45H57N8O8 ([M+H+]) 837.4299, found: 837.4316.

Determination of the IC50 of SAH, SNF, 1, 2a, 5a, 5b and 6a

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The IC50 assays of SAH, SNF, 1, 2a, 5a and 5b against a collection of 34 methyltransferases were performed as previously described except for PRMT9 (Bromberg et al., 2017; Li et al., 2016a; Li et al., 2016b). The IC50 values were obtained by fitting %inhibition against the concentrations of the inhibitor to a sigmoid curve.

Briefly, the effects of these compounds on G9a, GLP1, SUV39H1, SUV39H2, SUV420H1, SUV420H2, SETD2, SETD8, SETDB1, SETD7/9, a trimeric complex of MLL1, a pentameric complex of MLL3, a trimeric complex of EZH2 (PRC2), PRMT1, PRMT3, CARM1, a PRMT5/MEP50 complex, PRMT6, PRMT7, PRMT8, PRMT9, PRDM9, SMYD2, SMYD3, DNMT1, BCDIN3D and METTL3-METTL14 were assessed by monitoring the incorporation of the tritium-labeled methyl group of [3H-Me]-SAM into substrates using a scintillation proximity assay (SPA). Here, we prepared a 10 µl reaction containing [3H-Me]-SAM and a substrate at their concentrations close to the apparent Km values for each enzyme. The reaction was quenched by adding 10 µl of 7.5 M guanidine hydrochloride. Into this mixture, we added 180 µL of 20 mM Tris buffer (pH 8.0). This mixture was then transferred to a 96-well FlashPlate followed by incubation for 1 hr. The counts per minute (CPM) was measured with a TopCount plate reader. The CPM readouts in the absence of these compounds or these enzymes were defined as 100% activity and background (0%), respectively. For BCDIN3D and METTL3-METTL14, biotinylated RNA strands were used as substrates. For DNMT1, the substrate was a double-stranded DNA with the forward strand biotinylated. For PRMT9, the assay buffer contains 10 nM PRMT9 (aa 1–845), 20 µM SAM, 80 nM biotinylated-SAP145 peptide substrate, 20 mM Tris-HCl (pH = 7.5), 5 mM DTT, and 0.01% Triton X-100. Purification of PRMT9 will be reported elsewhere.

A filter-based assay was applied for DOT1L, NSD1, NSD2, NSD3, ASH1L, DNMT3A/3L, and DNMT3B/3L. Here, we incubated a reaction mixture of 10 µL at 23°C for 1 hr, followed by adding 50 µL of 10% trichloroacetic acid (TCA) to quench the reaction. The resulting mixture was then transferred to filter plates (Millipore, Billerica, MA, USA), followed by centrifugation at 2000 rpm (Allegra X-15R; Beckman Coulter, Brea, CA, USA) for 2 min, washing twice with 10% TCA and once with 180 µL ethanol. After centrifugation and drying, 100 µL MicroScint-O (Perkin Elmer) was added into each well and the plates were centrifuged to remove the liquid. A 70 μL volume of MicroScint-O was added and the CPM was measured with a TopCount plate reader.

The radiometric filter paper assay described previously (Ibanez et al., 2012; Zheng et al., 2012) was also performed as the alternative assay to determine the IC50 values of 2a, 5a and 6a against CARM1. Briefly, 20 µL enzymatic reactions (triplicate of each data point) were carried out in the assay solution containing 50 mM HEPES-HCl (pH = 8.0), 0.005% Tween-20 (v/v), 0.0005% BSA, 1 mM TCEP, 25 nM CARM1, 1.5 μM H3 peptide (aa 1–40), 0.75 μM [3H-Me]-SAM (PerkinElmer, 5–15 Ci/mmol in 9:1 v/v of sulfuric acid and ethanol), and the varied concentrations of inhibitors. For the signal readout of no inhibition (high readout), the same reaction (in triplicate) was carried out except with inhibitors replaced with DMSO. For the signal readout of 100% inhibition (background readout), the same reaction (in triplicate) was carried out except with inhibitors replaced with DMSO and with the omission of 25 nM CARM1. Here, we incubated a 10 µL solution containing 50 nM CARM1 and an examined inhibitor at 2 × concentrations at 22°C for 30 min. To the 10 µL mixture of CARM1 and an inhibitor, we added 10 µL of the assay buffer containing 3.0 μM H3 peptide (aa 1–40) and 1.5 μM [3H-Me]-SAM. The resultant reaction was carried out at ambient temperature (22°C) for 6 hr, which is in the linear range of the increase of signal readouts. The reactions were then quenched by spotting the reaction mixture onto P81 ion-exchange cellulose chromatography paper (Fisher) to immobilize the peptide. The filter paper was washed 3 times with 50 mM NaHCO3/Na2CO3 buffer (pH = 9.2) to remove the unreacted [3H-Me]-SAM. The immobilized peptide containing [3H-Me] was quantified by a scintillation counter (Tri-Carb 2810TR, PerkinElmer). To determine the IC50 value of an inhibitor against CARM1, %inhibition at specific concentrations was calculated as [(CPM of high readout – CPM readout in the presence of the inhibitor)/(CPM of high readout – CPM of background readout)]×100%. The IC50 values were obtained by fitting %inhibition against the concentrations of the inhibitor to a sigmoid curve with GraphPad Prism.

Determination of IC50 and Kd values of CARM1 inhibitors and Km,SAM in the presence of the varied concentrations of SAM and peptide substrate

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SAM- and substrate-dependent IC50 values were determined with the radiometric filter paper assay as described above except in the presence of the varied concentrations of the SAM cofactor and the peptide substrate. The IC50 values of 2a and 5a were obtained in the presence of 0.09, 0.18, 0.37, 0.75, 1.50, 2.25, 3.00, 5.62, and 7.50 µM [3H-Me]-SAM or 0.3, 1.5, 7.5, 15, 30, and 50 µM H3 peptide (aa 1–40). The resultant IC50 values were plotted as a function of the concentrations of SAM and the peptide substrate using GraphPad Prism Software. Given the SAM-competitive character of 2a and 5a, their SAM-dependent IC50 values were then fitted according to Equation 1. Here [SAM] is the concentration of SAM, Km,SAM is the Michaelis-Menten constant, and Kd is the dissociation constant of the CARM1 inhibitor 2a or 5a. Kd, Kd/Km,SAM and Km,SAM can therefore be obtained through the intercept of the y-axis, the slope, and their ratio, respectively, upon fitting Equation 1. Given that 6a showed a much higher IC50 (1.1 ± 0.1 µM, Figure 5—figure supplement 3), the Kd value of 6a (Kd,6a = 0.32 µM) was directly obtained from the IC50 value, the Km,SAM derived above, and the assayed SAM concentration according to Equation 1.

(1) IC50=[SAM]×kd/km,SAM+kd

Surface plasmon resonance (SPR)

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Full-length CARM1 was used for SPR assay. DNA fragment encoding the full-length CARM1 was cloned into pFB-N-flag-LIC donor plasmid. The resulting plasmid was transformed into DH10Bac-competent E. coli cells (Invitrogen) and a recombinant Bacmid DNA was purified, followed by a recombinant baculovirus generation in Sf9 insect cells. Sf9 cells grown in HyQ SFX insect serum-free medium (ThermoScientific) were infected with 10 mL of P3 viral stocks per 1 L of suspension cell culture and incubated at 27°C using a platform shaker set at 150 revolutions per minute. The cells were collected when viability dropped to 70–80% (~72 hr after infection). Harvested cells were re-suspended in PBS, 1X protease inhibitor cocktail (100X protease inhibitor stock in 70% ethanol containing 0.25 mg/mL aprotinin, 0.25 mg/ml leupeptin, 0.25 mg/mL pepstatin A and 0.25 mg/mL E-64) and 2X Roche complete EDTA-free protease inhibitor cocktail tablet. The cells were lysed chemically by rotating 30 min with NP40 (final concentration of 0.6%) and 50 U/mL benzonase nuclease (Sigma), 2 mM 2-mercaptoethanol and 10% glycerol followed by sonication at frequency of 7 (10′‘on/10′‘off) for 2 min (Sonicator 3000, Misoni). The crude extract was clarified by high-speed centrifugation (60 min at 36,000 × g at 4°C) by Beckman Coulter centrifuge. The recombinant protein was purified by incubating the cleared lysate with anti-FLAG M2 affinity agarose gel (Sigma, Cat # A2220) and then rotating for 3 hr, followed by washing with 10 CV TBS (50 mM Tris-HCl, 150 mM NaCl, pH 7.4) containing 2 mM 2-mercaptoethanol, 1X protease inhibitor cocktail (100X protease inhibitor stock in 70% ethanol containing 0.25 mg/mL aprotinin, 0.25 mg/mL leupeptin, 0.25 mg/mL pepstatin A and 0.25 mg/mL E-64) and 1X Roche complete EDTA-free protease inhibitor cocktail tablet. The recombinant protein was eluted by competitive elution with a solution containing 100 µg/mL FLAG peptide (Sigma, Catalog # F4799) in 20 mM Tris pH:7.4, 150 mM NaCl, 5% glycerol, 3 mM 2-mercaptoethanol. Quality of CARM1 (>95%) was determined by SDS–PAGE. The protein was then concentrated, flash frozen with liquid nitrogen, and stored at −80°C for future use.

SPR analysis was performed using a Biacore T200 (GE Health Sciences Inc) at 25°C. Approximately 5500 response units of CARM1 (amino acids 1–608) were amino-coupled onto a CM5 chip in one flow cell according to the protocol of the manufacturer. Another flow cell was left empty for reference subtraction. SPR analysis was conducted in the HBS-EP buffer (20 mM HEPES pH 7.4, 150 mM NaCl, 3 mM EDTA, 0.05% Tween-20) containing 2% (v/v) DMSO. The stock solutions of five concentrations of compound 2a (24.7, 74.1, 222, 667 and 2000 nM) and four concentrations of 5a (6.2, 18.5, 55.6 and 167 nM) were prepared by serial dilution. Binding kinetic experiments were performed with single cycle kinetics with the contact time of 60 s and off time of 300 s in a flow rate of 30 µL/min. To facilitate complete dissociation of compound for the next cycle, a regeneration step (300 s, 40 µL/min of buffer), a period of stabilization (120 s) and two blank cycles were included between each cycle. Kinetic curve fittings were carried out with a 1:1 binding model or a heterogeneous ligand model using Biacore T200 Evaluation software (GE Health Sciences Inc).

In vitro thermal shift assay (TSA)

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TSA was performed as described previously (Blum et al., 2014; Niesen et al., 2007) to examine the melting temperature (Tm) of CARM1 in the presence or absence of ligands. For each measurement (triplicate of each data point), the assay solution containing 50 mM HEPES-HCl (pH = 8.0), Tween-20 0.005% (v/v), 1 mM TCEP, 0.5 μM CARM1, and 5 μM ligand (SAM, 1, 2a or 5a) was mixed with 5 × SYPRO Orange Protein Gel Stain stock (Sigma Aldrich) in a 96-well PCR plate. The mixture was equilibrated at 25°C in dark for 5 min and then loaded onto Bio-Rad CFX96 Real-Time PCR Detection System. The fluorescence readouts were recorded upon increasing the heating temperature from 25°C to 100°C at a rate of 0.2 °C/s. The raw data of the fluorescence readouts versus the temperatures were exported with CFX software and processed as the percentage of the fluorescent signal normalized between the lowest readout of 0% and the highest readout of 100% within the 25‒100°C region. The melting curves were plotted as the normalized fluorescence (%) versus the heating temperature and fit with a sigmoid curve with GraphPad Prism. The Tm corresponds to the temperature with the 50% relative fluorescent signal in the sigmoidal curve.

Crystallization of CARM1 in complex with 1 and 5a

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A DNA fragment encoding the methyltransferase domain of human CARM1 (residues 140–480) was cloned into a baculovirus expression vector pFBOH-MHL (http://www.thesgc.org/sites/default/files/toronto_vectors/pFBOH-MHL.pdf). The protein was expressed in Sf9 cells as an N-terminal 6 × His tag fusion protein and purified by metal chelating affinity chromatography (TALON resin, Clontech, Mountain View, CA, USA) followed by size-exclusion chromatography (Superdex 200, GE Healthcare). Pooled fractions containing CARM1 were subjected to the treatment of tobacco etch virus to remove the 6 × His tag. The protein was purified to homogeneity by ion-exchange chromatography. Purified CARM1 (6.5 mg/mL) was crystallized with the sitting drop vapor diffusion method at 20°C.

For the CARM1–1 complex, CARM1 was mixed with 1 at a 1:5 molar ratio (protein:ligand) and crystallized with the sitting drop vapor diffusion method at 20°C by mixing 1 µL of the protein solution with 1 µL of the reservoir solution containing 20% PEG3350 and 0.2 M diammonium tartrate. X-ray diffraction data for the CARM1–1 complex were collected at 100 K at beam line 23ID-B of Advanced Photon Source (APS), Argonne National Laboratory. Data sets were processed using the HKL-2000 suite (Otwinowski and Minor, 1997). The structure of the CARM1–1 complex was solved by molecular replacement using MOLREP (Otwinowski and Minor, 1997) with the PDB entry 2V74 as the search template. REFMAC (Emsley and Cowtan, 2004; Murshudov et al., 1997) was used for the structure refinement. Graphics program COOTS4 was used for model building and visualization. Crystal diffraction data and refinement statistics for the structure are displayed in Table 2. To further confirm the electronic densities of the ligand, the total omission electron density map was calculated using SFCHECK from CCP4suite and contoured at 1.0 s (Figure 4—figure supplement 1) (Emsley and Cowtan, 2004; Murshudov et al., 1997).

For the CARM1-5a complex, CARM1 was crystallized with the sitting drop vapor diffusion method at 20°C by mixing 1 µL of protein solution with 1 µL of the reservoir solution containing 25% PEGG3350, 0.1M ammonium sulfate and 0.1 M Hepes (pH = 7.5). The compound 5a (0.2 μL of 10 μM in DMSO) was added to the drops with apo crystals and incubated overnight. X-ray diffraction data for the CARM1-5a complex were collected at 100 K on a Rigaku FR-E superbright X-ray generator. Data were processed using the HKL-3000 suite (Minor et al., 2006). The structure was isomorphous to PDB entry 4IKP, which was used as a starting model. REFMAC (Murshudov et al., 2011) was used for structure refinement. Geometric restraints for compound refinement were prepared with GRADE v.1.102 developed at Global Phasing Ltd. (Cambridge, UK). The COOT graphics program (Emsley et al., 2010) was used for model building and visualization, and MOLPROBITY (Williams et al., 2018) was used for structure validation. To further confirm the electronic densities of the ligand, the total omission electron density map was calculated using SFCHECK from the CCP4suite and contoured at 1.0 s (Figure 3b) (Emsley and Cowtan, 2004; Murshudov et al., 1997).

Molecular docking and molecular dynamics simulations of CARM1–2a and CARM1–SNF complexes

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The ligands were docked into the binding site of CARM1 using the induced-fit docking (IFD) protocol (Sherman et al., 2006) implemented in the Schrodinger suite (release 2016–4). The poses for SNF and 2a were selected according to the IFD scores. Specifically, the results identified two distinct poses for 2a with similar scores but only one pose for SNF. The poses were then further relaxed by all-atom, explicit solvent molecular dynamics (MD) simulations. Herein, CARM1 models in complex with the two ligands were placed into explicit water boxes. A simple point charge (SPC) water model (Berendsen et al., 1981) was used to solvate the system, charges were neutralized, and 0.15 M NaCl was added. The total system size was ∼50,000 atoms. Desmond MD systems (D. E. Shaw Research, New York, NY) with OPLS3 force field (Harder et al., 2016) were used. The system was initially minimized and equilibrated with restraints on the ligand heavy atoms and protein backbone atoms, followed by production runs with all atoms unrestrained. The isothermal-isobaric ensemble was used with constant temperature (310 K) maintained with Langevin dynamics and 1 atm constant pressure achieved using the hybrid Nose-Hoover Langevin piston method (Feller et al., 1995) on a flexible periodic cell. For each CARM1–ligand complex, a 600 ns trajectory was collected.

Cell lines

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MCF-7 and MDA-MB-231 (parental and CARM1-KO) cell lines (Wang et al., 2014a) were used after their quality was confirmed with STR profile and standard mycoplasma contamination testing.

LC-MS/MS quantification of intracellular concentrations of 6a, 5a, 2a and SAM

Sample preparation for LC-MS/MS analysis

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To measure the intracellular concentrations of 6a, 5a, 2a, and SAM, a LC-MS/MS quantification method was developed with modification of what was reported previously (Wang et al., 2014b). Briefly, 0.2 × 106 MDA-MB-231 cells were incubated with varied concentrations of 6a (0.5, 2.5, 5.0, and 10.0 µM) for several different periods of time (0.1, 3, 6, 12, 24, and 48 hr). The treated cells (in triplicate for each data point) were harvested and centrifuged at 4°C at 94 x g for 5 min. The cell pellets were resuspended and then washed with 4 × 1 mL of 4°C PBS to remove extracellular 6a. The 4 × 1 mL washing is sufficient to remove extracellular 6a, as evidenced by the LC-MS/MS analysis in which the residual 6a in each washing buffer gradually decreased until the full loss of its MS signal after the 4 times of washing. The washed cell pellets were then treated with 40 µL MeOH (with 0.1% TFA, v/v) containing 0.125 µM 6b, 0.125 µM 5b, 16 µM 2b, and 2 µM CD3-SAM (Linscott et al., 2016) as the LC-MS/MS internal standards. The mixture was lysed by 150 W sonication at 0°C for 20 min. The resultant cell lysis was centrifuged with 21,130 g at 4°C for 20 min. The 30 µL aliquot of the MeOH extraction of each sample was collected into a 96-well plate (5042–1386, Agilent) and stored at −20°C until used for LC-MS/MS analysis.

LC-MS/MS conditions

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Liquid chromatography-tandem mass spectrometry analysis was performed with a 6410 triple-quad LC-MS/MS system (Agilent Technologies) in electrospray ionization (ESI) mode, equipped with an Agilent Zorbax Eclipse XDB-C18 column (2.1 × 50 mm, 3.5 µM). The samples were eluted with a 5–95% gradient (v/v) of CH3CN in aqueous formic acid (HCOOH, 0.1%, v/v) in 7 min with a flow rate of 0.4 mL/min. The 96-well sample plate obtained above was maintained in the 4°C chamber of the LC-MS/MS prior to analysis. The 7 µL MeOH extraction of each sample was injected into the LC-MS/MS, and the MS signals were collected using the multiple-reaction monitoring (MRM) mode.

Working curves of 6a, 5a, 2a, and SAM with 6b, 5b, 2b, and CD3-SAM as internal standards

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(See Figure 5c,e, Figure 5—figure supplements 1 and 2.) Standard working curves to quantify 6a, 5a, 2a, and SAM were generated by plotting a linear function (Equation 2) with the ratio of the mass peak areas (PA/PIS) on the x-axis and the ratio of the concentration (CA/CIS) between each analyte (A) and the structurally related internal standard (IS) on the y-axis. To obtain the values of ‘p’ and ‘q’ in Equation 2, the 0.1% TFA MeOH (v/v) samples containing the varied concentrations of an analyte (6a, 5a, 2a or SAM) and the fixed concentrations of the mixture of the internal standards (0.125 µM 6b, 0.125 µM 5b, 16 µM 2b, and 2 µM CD3-SAM) were subject to LC-MS/MS analysis. For 6a, 5a, and 2a, three working curves for each analyte (a total of 9 for the three analytes) were generated to cover the concentration range of 3.9 nM‒18.0 µM of the analyte (CA,6a, CA,5a and CA,2a) with the structurally related 6b, 5b and 2b (CIS,6b, CIS,5b and CIS,2b) as internal standards. For SAM, one working curve was generated to cover the concentration range of 0.28‒18 µM of SAM (CA,SAM) with CD3-SAM as the internal standard (CIS,CD3-SAM).

(2) Y=p×X+q

Quantification of intracellular concentrations of 6a, 5a, 2a, and SAM

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Using the standard working curves generated above, the concentration (CA) of each analyte (6a, 5a, 2a, and SAM) in the MeOH extraction of cell lysates was obtained through the ratio (PA/PIS) of the mass peak areas of each analyte (PA) versus each internal standard (PIS), and the concentration of the internal standard (CIS) according to Equation 2. For 6a, 5a, and 2a under each assay condition, three similar CA values (CA-6b, CA-5b, and CA-2b) were obtained with 6b, 5b and 2b as the internal standards, respectively. An average concentration of the analyte (C¯A) was obtained on the basis of the three concentrations weighted by the mass peak areas of the three internal standards (PIS,6b, PIS,5b and PIS,2b) according to Equation 3:

(3) C¯A=CA6b×PIS6bPIS6b+PIS5b+PIS2b+CA5b×PIS5bPIS6b+PIS5b+PIS2b+CA2b×PIS2bPIS6b+PIS5b+PIS2b

On the basis of the C¯A values of 6a, 5a, and 2a (C¯A,6a, C¯A,5a and C¯A,2a) in the MeOH extraction of cell lysates, the intracellular concentrations of the analyte 6a, 5a, and 2a (Cintra,6a Cintra,5a and Cintra,2a) were calculated according to Equation 4. Here C¯A,analyte is the weighted average of the three concentrations in the MeOH extraction, N is the cell number, and V is the mean volume of cells (µL). The mean volume of MDA-MB-231 cells is 1.3 × 10−6 µL/cell as reported previously (Coulter et al., 2012). The cell number (N) was determined with a hemocytometer. The concentration of SAM (CA,SAM) in the MeOH extraction was obtained according to Equation 2, in which ‘X’ is the ratio (PA,SAM/PIS,CD3-SAM) of the mass peak areas of SAM (PA,SAM) versus the internal standard CD3-SAM (PIS,CD3-SAM) and ‘Y’ is the ratio (CA,SAM/CIS,CD3-SAM) of the concentrations of SAM versus CD3-SAM in the MeOH extraction. Given the identical LC-MS properties of SAM and CD3-SAM, CA,SAM was obtained solely on the basis of the working curve with CD3-SAM as the internal standard. The intracellular concentration of SAM (Cintra,SAM) was then calculated using Equation 4, in which N is the cell number and V is the mean volume of MDA-MB-231 cells (1.3 × 10−6 µL/cell).

(4) Cinter,analyte=C¯A,analyte×40μLN×V

Quantification of intracellular concentrations of 6b, 5b, 2b and SAM

Similar experimental procedures were carried out to quantify the intracellular concentrations of 6b, 5b, 2b and SAM except that 6b, 5b, and 2b were the analytes and their structurally related 6a, 5a and 2a were used as the internal standards with their CIS values of 0.125 µM, 0.125 µM, and 0.0125 µM, respectively. For each analyte (6b, 5b, and 2b), three working curves (Figure 5c,e, Figure 5—figure supplement 2) were generated to cover the concentration range of 3.9 nM‒18.0 µM of the analyte (CA,6b, CA,5b and CA,2b) with 6a, 5a and 2a (CIS-6a, CIS-5a, and CIS-2a) as the LC-MS/MS internal standards, respectively.

Calculation of the percentages of intracellular apo-CARM1, CARM1 occupied by 6a, 5a, and 2a, and SAM-bonded CARM1 versus the total amount of CARM1

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On the basis of the intracellular concentrations (Cintra, analyte) of 6a, 5a, 2a, and SAM quantified by the LC-MS/MS experiment described above, the percentages of intracellular apo-CARM1, CARM1 occupied by 6a, 5a, and 2a, SAM-bound CARM1 versus the total amount of intracellular CARM1 were calculated using Equations 5-7, respectively.

(5) Occupancy(Apo)%=[E][E]+[ESAM]+i=1n[EIi]×100%=11+Cinter,SAMKm,SAM+inCinter,IiKdIi×100%
(6) Occupancy(I)%=i=1n[EIi][E]+[ESAM]+i=1n[EIi]×100%=inCinter,Iikd,Ii1+Cinter,SAMKm,SAM+inCinter,IiKdIi×100%
(7) Occupancy(SAM)%=[ESAM][E]+[ESAM]+i=1n[EIi]×100%=Cinter,SAMkm,SAM1+Cinter,SAMKm,SAM+inCinter,IiKdIi×100%

Here [E], [E-SAM] and [E-Ii] are the intracellular concentrations of apo-CAMR1, the CARM1–SAM complex and the CARM1 occupied by the inhibitors; Cintra,SAM and Cintra,Ii are the intracellular concentrations of SAM and individual CARM1 inhibitors, respectively; Km,SAM is the Michaelis-Menton constant of SAM in forming the CARM1–SAM complex, which is approximated to Kd,SAM (Km,SAMKd,SAM = 245 nM); and Kd,Ii is the dissociation constant of these CARM1 inhibitors. In the case of the cellular treatment with 6a, ‘n’ is equal to 3; I1, I2 and I3 stand for 2a, 5a and 6a, respectively; Kd,I1, Kd,I2 and Kd,I3 stand for Kd,2a = 17 nM, Kd,5a = 9 nM and Kd,6a = 275 nM.

Calculation of SMYD2 occupancy by 2a within cells

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In a manner similar to that described above for the ligand occupancy of CARM1, the SMYD2 occupancy by 2a was modeled with Kd,SAM = 60 nM, Kd,2a = 150 nM and Equation 6. Here, the Kd,SAM value was reported previously upon developing the activity assay of SMYD2 (Sweis et al., 2015). The Kd,2a value was obtained according to IC50 = [SAM]×Kd/Kd,SAM+Kd, in which [SAM]=Kd,SAM = 60 nM for the IC50 assay and IC50 = 0.3 µM was obtained (Supplementary file 1-Table A). The terms of SMYD2 occupancy by exogenous ligands were ignored given their low concentrations and low affinity relative to that of 2a (Supplementary file 1-Table A).

Microsomal stability of 6a

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Potential in vivo clearance of 6a was evaluated in the presence of liver microsomes as previously reported (Hansen et al., 2012). Briefly, 6a was dissolved into 50 mM potassium phosphate buffer (pH 7.4) to a final concentration of 100 μM. To a 0.5 mL Eppendorf vial containing ice-cold potassium phosphate buffer (pH 7.4), we added 5 μL of the 100 μM stock solution of 6a, 3 μL of a freshly prepared NADPH solution (5 mM, Sigma-Aldrich, N7785), and 1.33 μL of freshly thawed rat liver microsomes (Sigma-Aldrich, M9066) to yield assay samples with a final volume of 50 μL. The resulting mixture was immediately vortexed and incubated in a 37°C shaker. At the time interval of 0, 20, 40, and 60 min in triplicates, respective assay samples were quenched by adding 50 μL of ice-cold methanol, vigorously vortexed, and centrifuged with 5,000 g at 4°C for 20 min. From the methanol-quenched sample, 50 μL of supernatant was collected and mixed with 50 μL of 0.1% (v/v) TFA/MeOH solution containing 0.125 µM 6b, 0.125 µM 5b, and 16 µM 2b as the internal standards. The resulting mixture was transferred into a 96-well plate (Agilent, 5042–1386) and stored at −20°C for LC-MS/MS analysis. The microsomal stability of 6a was evaluated via LC-MS/MS quantification of the residual 6a and of two potential microsome-processed products, 5a and 2a, upon quantification of intracellular concentrations of 6a, 5a and 2a (as described above). The amount of 6a was plotted as the percentage of the residual concentration at each time point against its initial concentration at 0 min with GraphPad Prism Software. Potential production of 5a and 2a from 6a was examined in parallel. Interestingly, despite robust accumulation of 5a, no 2a was identified with the detection threshold of 0.02 µM.

For negative controls, 3 μL of 50 mM potassium phosphate buffer (pH 7.4) replaced the freshly prepared NADPH solution; two assay samples at the time interval of 0 and 60 min were collected. Under the current microsomal condition, the concentration of 6a remained the same (10 ± 0.7 μM) and there was no production of 5a or 2a.

Evaluation of methylation marks of CARM1 in breast cancer cells

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MDA-MB-231 cells and MCF-7 parental and CARM1-KO (Wang et al., 2014a) cells were maintained in DMEM (Gibco, Gaithersburg, MD) medium containing 10% FBS (Gibco). These cells were then treated with compounds or with DMSO for 48 hr. The resultant cells were washed twice in phosphate-buffered saline (PBS) and the samples were sonicated in ice-cold RIPA buffer (Thermo, Waltham, MA). The lysates were centrifuged (15,000 g) at 4°C for 15 min. The supernatants were kept with the total protein amount determined with Bradford protein assay (BioRad, Hercules, CA). After quantification, 50 μg of protein from each sample was loaded onto 6% SDS-PAGE and transferred onto a nitrocellulose membrane (PALL, Port Washington, NY). For the Arg1064 methylation mark of BAF155, the blots were blocked in 5% non-fat milk for 1 hr and incubated with anti-me-BAF155 (Cancer Cell, 2014), anti-BAF155 (1:1000; Santa Cruz Biotechnology, Dallas, TX), anti-CARM1 (1:1000; Genemed Synthesis, San Antonio, TX), and anti-β-actin (1:20000; Sigma-Aldrich, St. Louis, MO) overnight at 4°C. After washing three times in Tris-buffered saline with Tween 20 (TBST), the blots were incubated with HRP-conjugated secondary antibody (1:3000; Jackson ImmunoResearch, West Grove, PA). After washing blots with TBST, the membranes were developed using SuperSignal West Pico ECL solution (Thermo). For the Arg455/Arg460 methylation mark of PABP1, anti-me-PABP1 (1:1000) and anti-PABP1 (1:1000) antibodies (Genemed Synthesis, San Antonio, TX) were used instead. Here the antibodies against CARM1, PABP1, and me-PABP1 were custom generated by Genemed Synthesis (San Antonio, TX) (Zeng et al., 2013). The density of the protein bands was quantified using ImageJ software (NIH, Bethesda, MD). The EC50 values were obtained by fitting the methylation percentage (%) of BAF155 or PABP1 against the concentrations of the inhibitor using a sigmoidal equation with GraphPad Prism Software.

Cellular thermal shift assay (CETSA)

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CETSA was performed as described previously (Jafari et al., 2014) to examine the intracellular engagement of 6a or 6b with CARM1. Briefly, 2.0 × 106 MDA-MB-231 cells were incubated with 15 μM 6a or 6b and DMSO for 48 hr. The harvested cells were re-suspended in PBS buffer and divided into eight aliquots (30 µL/aliquot), and then heat-shocked at various temperatures (49.1, 54.6, 57.0, 59.5, 61.8, 63.9, 65.6, and 67.0°C) for 3 min with a Bio-Rad CFX96 Real-Time PCR Detection Instrument (using a temperature gradient of 49‒67°C). The heat-shocked cells were then lysed with the freeze-thaw method with a liquid nitrogen bath followed by a 25°C water bath for five cycles. The cell lysate was centrifuged at 4°C at 18,000 g for 20 min. The resultant supernatant containing the soluble protein fraction was collected and loaded onto an SDS-PAGE gel (20 µL). Western blotting of CARM1 was performed with anti-CARM1 antibody (Cell Signaling Technology, C31G9). For each sample, the band intensity of CARM1 was quantified with ImageJ. The band intensity of CARM1 was normalized to the band intensity at 49.1°C (the lowest heat-shock temperature). Melting curves were obtained by plotting the normalized band intensity against the heat-shock temperatures and fit with a Boltzmann sigmoidal equation in GraphPad Prism. The melting temperatures (Tm) of 6a, 6b, or DMSO were obtained as the heat-shock temperatures that correspond to the 50% normalized band intensity in the fitted sigmoidal curves.

Cell invasion and proliferation assay

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MDA-MB-231 parental and CARM1-KO cells (Wang et al., 2014a) were maintained in DMEM (Gibco, Gaithersburg, MD) medium containing 10% FBS (Gibco). Cell invasion assays were performed using 8.0 µm pore size Transwell inserts (Greiner Bio-One, Kremsmünster, Austria). MDA-MB-231 parental and CARM1-KO (Cancer Cell, 2014) cells were harvested with trypsin/EDTA and washed twice with serum-free DMEM (Gibco). 2 × 105 cells in 0.2 mL serum-free DMEM (Gibco) were seeded onto the upper chamber, which was pre-coated with a thin layer of 40 uL of 2 mg/mL Matrigel (Corning, NY, USA) for 2 hr incubation at 37°C. To the lower chamber, we added 0.6 mL DMEM containing 10% FBS (GIBCO) and compounds or DMSO. After 16 hr in the 37°C incubator, the cells on the inner side of the upper chamber together with the Matrigel layer were removed using cotton tips. The residual invasive cells in the outer side of the upper chamber were fixed in 3.7% formaldehyde (a weight percentage) at an ambient temperature (22°C) for 2 min and 100% methanol for 20 min, and then stained for 15 min with a solution containing 1% crystal violet and 2% ethanol in 100 mM borate buffer (pH 9.0). The number of invasive cells was counted under a microscope by taking five independent fields. Relative cell invasion was determined by the number of the invasive cells normalized to the total number of cells adhering to 0.8 µm transwell filters. The EC50 was obtained with GraphPad Prism Software upon fitting Equation 8, in which ‘%Inhibition’ is the percentage of the inhibition of invasiveness, ‘Maximal Inhibition%’ is the percentage of the maximal inhibition of invasiveness, and [Inhibitor] is the concentration of the inhibitor.

(8) Inhibition%=MaximalInhibition%×[Inhibitor][Inhibitor]+EC50

To examine proliferation of MDA-MB-231 cells, 5000 cells of parental and CARM1-KO cells were seeded in a 96-well plate and incubated in 37°C overnight. These cells were treated with various doses (0.0001‒10 μM) of SKI-73 or SKI-73N (6a or 6b) in DMSO and incubated for 72 hr. An MTT assay was then performed to examine viability with DMSO-treated cells as the control. The relative viability of compound-treated cells versus DMSO-treated parent cells were plotted against the concentrations of SKI-73 and SKI-73N (6a and 6b).

Sample preparation for single-cell RNA-seq analysis (scRNA-seq)

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Cell preparation for scRNA-seq. 10 × Genomics droplet-based scRNA-seq was implemented to characterize five types of MDA-MB-231 cells: MDA-MB-231 cells treated with DMSO, SKI-73 (6a) and SKI-73N (6b), MDA-MB-231 cells that freshly invaded through Matrigel, and CARM1-KO MDA-MB-231 cells. For the first three samples, MDA-MB-231 cells were treated with DMSO, 10 μM SKI-73 (6a), and 10 μM SKI-73N (6b) for 48 hr. The resulting cells were trypsinized at 37°C for 3 min. The harvested cells were washed twice with 1 × PBS (phosphate-buffered saline) containing 0.04% (volume%) bovine serum albumin (BSA), gently dispersed to dissociate cells, and then filtered through a cell strainer (100 μm Nylon mesh, Fisherbrand) to obtain single-cell suspensions for scRNA-seq. For the CARM1-KO sample, the cells were trypsinized at 37°C for 3 min, washed twice with 1 × PBS containing 0.04% BSA, and filtered through a 100 μm Nylon-mesh cell strainer (Fisherbrand) to obtain single-cell suspensions for scRNA-seq.

To collect the cells that freshly invaded through Matrigel, the conditions described above for cell invasion assays were applied to allow approximately 5% of the 1 × 107 seeded MDA-MB-231 cells to invade through Matrigel. Briefly, ten Transwell inserts with a diameter of 24 mm and a pore size of 8.0 μm were pre-coated with a thin layer of 54 μL of 2 mg/mL Matrigel (Corning). 1 × 106 MDA-MB-231 cells in 1.0 mL serum-free DMEM (Gibco) were seeded into the upper chamber of each Matrigel-coated Transwell insert. 2.0 mL DMEM containing 10% FBS (Gibco) was added into the lower chambers. After 16 hr incubation, the cells on the inner side of the upper chamber together with the Matrigel layer were removed using cotton tips. The cells that freshly invaded–those attached on the outer side of the upper chamber–were subjected to 3 min trypsin digestion at 37°C for detachment. The resulting cells were washed twice with 1 × PBS containing 0.04% BSA, gently dispersed to dissociate cells, and filtered through a 100 μm Nylon-mesh cell strainer (Fisherbrand) to obtain single-cell suspensions for scRNA-seq.

Cell barcoding, library preparation and sequencing

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The scRNA-seq libraries were prepared following the user guide manual (CG00052 Rev E) provided by the 10 × Genomics and Chromium Single Cell 3' Reagent Kit (v2). Briefly, samples containing approximately 8700 cells (93–97% viability) were encapsulated in microfluidic droplets at a dilution of 66–70 cells/µL, which resulted in 4369–5457 recovered single-cells per sample with a multiplet rate ~3.9%. The resultant emulsion droplets were then broken and barcoded cDNA was purified with DynaBeads, followed by 12-cycles of PCR-amplification: −98 °C for 180 s, 12×(98°C for 15 s, 67°C for 20 s, 72°C for 60 s), and 72°C for 60 s. The 50 ng of PCR-amplified barcoded-cDNA was fragmented with the reagents provided in the kit and purified by SPRI beads with an averaged fragment size of 600 bp. The DNA library was then ligated to the sequencing adapter followed by indexing PCR: −98 °C for 45 s; 12× (98°C for 20 s, 54°C for 30 s, 72°C for 20 s), and 72°C for 60 s. The resulting DNA library was double-size purified (0.6–0.8×) with SPRI beads and sequenced on Illumina NovaSeq platform (R1 – 26 cycles, i7 – eight cycles, R2 – 96 cycles) resulting in 70–79 million reads per sample with average reads per single-cell being 8075–10,342 and average reads per transcript 1.11–1.15.

Processing, transformation, filtering and dimensionality reduction of scRNA-seq data

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The fastq files containing the transcriptome and barcoding metadata were demultiplexed using the SEquence Quality Control (SEQC) pipeline (http:github.com/ambrosejcarr/seqc.git) resulting in around 8000 UMIs per one cell. The table of UMI counts was used as the input and Seurat package v.2.3.4 (Butler et al., 2018) was applied for scRNA-seq analysis. Here, the raw UMI counts were normalized per cell by dividing the total number of UMIs in each individual cell, multiplying by a scale factor of 10,000 and transforming into natural logarithm values. Cells with 1000~5000 genes and <20% mitochondrial RNA transcripts were kept for further analysis. Dimensionality reduction was carried out by selecting a set of highly variable genes on the basis of the average expression and dispersion per gene. The set of genes was used for principle component analysis (PCA). Top principle components were then chosen for cell clustering analysis and t-SNE projection. Regression was performed to remove cell–cell variation in gene expression driven by the UMI number, mitochondrial gene content and ribosomal gene content using ‘ScaleData’ function in Seurat package (Nestorowa et al., 2016). Clusters of cells were identified by clustering algorithm based on a shared nearest neighbor (SNN) modularity optimization that was included in Seurat package v.2.3.4 (Butler et al., 2018).

Subpopulation clustering guided by scRNA-seq

Cell-cycle awareness

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To assign cells to cell-cycle stages (G0/G1, S and G2/M), individual cells were scored on the basis of their expression of G2/M-phase and S-phase markers (Nestorowa et al., 2016) by comparing the average expression of these markers with that of a random set of background genes (Tirosh et al., 2016). Cells with positive higher S-phase or G2/M-phase scores were assigned as S-phase or G2/M-phase cells, respectively. Cells with negative S-phase and G2/M-phase scores were assigned as non-S/G2/M-phase cells and annotated as G0/G1-phase cells. The whole cell population, as well as its subpopulations, can thus be classified into the three groups according to their cell-cycle scores.

Determination of the number of clusters

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Three algorithms–Silhouette analysis, entropy scoring, and Fisher’s Exact Test–were applied collectively to determine the number of clusters.

Silhouette analysis

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Silhouette analysis was conducted with no awareness of cell origins (DMSO-, SKI-73(6a)- or SKI-73N(6b)-treated cells) and was calculated on the basis of distances defined as Euclidean distance between any pair of cells on the two-dimensional t-SNE projection (de Amorim and Hennig, 2015; Rousseeuw, 1987).

Entropy scoring

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We developed an entropy-based scoring method to evaluate the efficiency of clustering subpopulations with the three cell origins (DMSO-, SKI-73(6a)-, and SKI-73N(6b)-treated). Entropy score is defined within a range of 0‒1 by Equation 9, which was derived on the basis of the double-weighted sum of cell-origin-based Shannon entropy across clustered subpopulations. Herein, ‘i’ is the series number of a clustered subpopulation starting from zero; ‘n’ is the largest series number of clustered subpopulations; the total number of subpopulation is ‘n+1’ (i = 0‒n); ‘fi’ corresponds to the fraction of the subpopulation ‘i’ in the total cell population (0<fi1;Σi=1nfi=1) ; ‘j’ represents one of the three cell origins (j = 1, 2 or 3); ‘dj,i’ (‘dDMSO,i’, ‘dSKI-73N,i’ and ‘dSKI-73,i’) is the fractional distribution of the cells with the ‘j’ origin (DMSO, SKI-73/6a, and SKI-73N/6b) within the ‘i’ subpopulation (0dj,i1;Σj=13dj,i=1) ; ‘fj’ is the fraction of the cells with the ‘j’ origin within the total population (0<fi1;Σj=13fi=1) ; 'Σj=13(fi×loge(fj))' is the theoretical maximum of (Σi=10(fi×(Σj=13(dj,i×loge(dj,i))))(all the cells in a single cluster). A smaller entropy score indicates that the corresponding method allows cell subpopulations to be clustered with higher resolution for the DMSO-, SKI-73(6a)-, and SKI-73N(6b)-treated cells. The minimal entropy score of zero indicates that subpopulations can be fully resolved for the three treatment conditions.

(9) EntropyScore=(Σi=0n(fi×(Σj=13(dj,i×loge(dj,i))))/(Σj=13(fj×loge(fi))

Fisher’s Exact Test

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Fisher’s Exact Test was implemented to evaluate the agreement of the clusters with the three cell origins (DMSO-, SKI-73(6a)- or SKI-73N(6b)-treated cells) using R package ‘fisher.test’: http://mathworld.wolfram.com/FishersExactTest.html (Mehta and Patel, 1983). Because of the significant computation cost of Fisher’s Exact Test, the cell population of each Fisher’s Exact Test was down-sampled to 150 cells and this process was repeated for 100 times to cover a majority of the cell population. The p-value of Fisher’s Exact Test was then computed by Monte Carlo simulation. Means and standard errors of p-values were calculated and reported as the outputs of Fisher’s Exact Test.

Three algorithmic scoring systems were used over a range of the resolution parameter that sets the corresponding ‘granularity’ of clustering, with higher values indicating a greater number of clusters. Here, silhouette analysis was applied to determine the number of clusters in an unsupervised manner, without awareness of the three cell origins (DMSO-, SKI-73(6a)- or SKI-73N(6b)-treated cells). The entropy scoring and Fisher’s Exact Test were implemented to evaluate the biological meaning of the clustering, using the minimal cluster number to resolve the cells between the three treatment conditions maximally. Given the awareness of the three cell origins, the minimal number of clusters with the maximal resolution of cell origin guided by the entropy scoring and Fisher’s Exact Test is expected within the 1~3-fold range of the optimized number of clusters guided by Silhouette analysis. Fisher’s Exact Test was used as the primary scoring method to determine the efficiency of clustering, given its higher resolution.

Population analysis of the three treatment conditions

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dj,i’ (‘dDMSO,i’, ‘dSKI-73N,i’ and ‘dSKI-73,i’ in Equation 9 are defined as the fraction of the cells with the ‘j’ origin (j = 1, 2 or three for the treatment with DMSO, SKI-73N/6b and SKI-73/6a, respectively) within the ‘i’ subpopulation (i = 0‒n for the clustered subpopulation). ‘di,total’ is defined as the fraction of the cells of the ‘i’ subpopulation within the total cell population in each cell-cycle stage. ‘d(j,i),total =djj,i ×di,total’ represents the fraction of the cells with the ‘j’ origin (DMSO, SKI-73/6a, and SKI-73N/6b) and the ‘i’ subpopulation within the total cell population in each cell-cycle stage. For a specific subpopulation ‘i’, there are three ‘d(j,i),total’ values (d(DMSO,i),total, d(SKI-73N,i),total and d(SKI-73,i),total) for the treatments with DMSO, SKI-73N (6b) and SKI-73 (6a), respectively. The alteration of subpopulations is defined as the ratios of d(SKI-73N,i),total/d(DMSO,i),total or d(SKI-73,i),total/d(DMSO,i),total fall out of the range of 1.0 ± 0.2. Population analysis was conducted by classifying the SKI-73N/SKI-73(6a/6b)-treated subpopulations into the following five categories: commonly resistant (0.8 < ‘d(SKI-73N,i),total/d(DMSO,i),total’ and ‘d(SKI-73,i),total/d(DMSO,i),total’<1.2), commonly emerging (‘d(SKI-73N,i),total/d(DMSO,i),total’ and ‘d(SKI-73,i),total/d(DMSO,i),total’≥1.2), commonly depleted (‘d(SKI-73N,i),total/d(DMSO,i),total’ and ‘d(SKI-73,i),total/d(DMSO,i),total’≤0.8; ∣‘d(SKI-73N,i),total/d(DMSO,i),total’−‘d(SKI-73,i),total/d(DMSO,i),total’∣<0.15), differentially emerging (either ‘d(SKI-73N,i),total/d(DMSO,i),total’ or ‘d(SKI-73,i),total/d(DMSO,i),total’>1.2), and differentially depleted (‘d(SKI-73N,i),total/d(DMSO,i),total’ or ‘d(SKI-73,i),total/d(DMSO,i),total’<0.8; ∣‘d(SKI-73N,i),total/d(DMSO,i),total’−‘d(SKI-73,i),total/d(DMSO,i),total’∣>0.15). Although additional combinations of differentially altered subpopulations could be possible, only those defined above were found under our treatment conditions.

Correlation analysis of subpopulations

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In each cell cycle (G0/G1, S and G2/M) of the cells treated with DMSO, SKI-73 (6a) or SKI-73N (6b) and ‘invasion cells’, correlation analysis of subpopulations was conducted with ‘BuildClusterTree’ function in the Seurat package (https://rdrr.io/cran/Seurat/man/BuildClusterTree.html) (Nestorowa et al., 2016). The phylogenetic trees were constructed by averaging gene expressions across all cells in each subpopulation and then calculating distance on the basis of expressions averaged between different subpopulations.

Differential expression across remotely related subpopulations and the selection of representative transcripts

Differentially expressed genes were identified by comparing two groups of cells using the Wilcox rank sumtest with the ‘FindMarkers’ function in the Seurat package (Nestorowa et al., 2016). In particular, the ‘invasion cells’ and their most correlated clusters (which were revealed in the correlation analysis) were selected as the ‘high’ group; the remaining remotely related clusters were selected as the ‘low’ group. The differential expression analysis was then performed by comparing cells in the ‘high’ group with the cells in the ‘low’ group. For the G0/G1-phase cells, the ‘invasion cells’ and Subpopulation 6, 7, 8, 9, 14 were selected as the ‘high’ group; Subpopulation 0, 1, 2, 3, 4, 5, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20 were selected as the ‘low’ group. For the G2/M-phase cells, the ‘invasion cells’ and Subpopulations 1, 2 were selected as the ‘high’ group; and Subpopulations 0, 3, 4, 5 were selected as the ‘low’ group. For the S-phase cells, the ‘invasion cells’ and Subpopulations 0, 3 were selected as the ‘high’ group; and Subpopulations 1, 2, 4, 5, 6 were selected as the ‘low’ group. Differentially expressed genes were ranked according to the average log2 fold change ‘avg_logFC’ and the adjusted p-values ‘p_val_adj’ with the Seurat package. Top upregulated and downregulated genes were chosen by setting a cutoff on their ‘avg_logFC’ values (>0.25 or <−0.25). Then, curated genes with potential functional relevance to cancer malignancy (30 upregulated and 10 downregulated genes) were selected as representative genes for generating heat map plots using the ‘DoHeatmap’ function in the Seurat package.

Analysis of differential expression across invasion-prone subpopulation candidates and the selection of representative transcripts for Violin plots

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To select candidate genes of the G0/G1-phase cells for violin plots, the ‘invasion cells’ and the cells of Subpopulation 8 were selected as the ‘high’ group and the Subpopulations 6, 7, 9, 14 were selected as the ‘low’ group. Differentially expressed genes were ranked according to the average log2 fold change ‘avg_logFC’ and adjusted p-values ‘p_val_adj’. Top up- and downregulated genes were chosen by setting their ‘avg_logFC’ values > 0.25 or < −0.25 and by curating genes that are functionally implicated in cancer malignancy. Heat map plots were generated for the selected gene with the ‘DoHeatmap’ function in the Seurat package (Nestorowa et al., 2016). Furthermore, a panel of the top eight genes highlighting similarity between the invasion-prone Subpopulation 8 and ‘invasion cells’ (the top five upregulated and top three downregulated genes) were selected for generating violin plots.

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Decision letter

  1. Philip A Cole
    Senior Editor; Harvard Medical School, United States
  2. Wilfred A van der Donk
    Reviewing Editor; University of Illinois, United States
  3. Paul R Thompson
    Reviewer; University of Massachusetts Medical School, United States
  4. Mark Bedford
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This manuscript describes the development of the small molecular weight compound SKI-73 as a prodrug that releases two potent and selective inhibitors of protein arginine methyltransferase 4 (CARM1) in cells. Using a previously reported inhibitor from natural sources as a scaffold, the authors developed the two inhibitors that display sub-nanomolar potency against the target enzyme and >10-fold selectivity over 7 other human protein arginine methyltransferase and 26 methyltransferases of other classes. Two other chemical probes for the target enzyme have been identified previously. Importantly, all three of these chemical probes have distinct molecular scaffolds and mechanisms. The current compounds compete for the cofactor binding site, unlike the previous two inhibitors that target the substrate arginine binding pocket. The authors next added chemical groups to a primary amine of the initial non-cell permeable inhibitor to develop the cell-permeable pro-drug SKI-73. This strategy potentially delivers a new class of compounds for inhibiting methyltransferases. Detailed single-cell transcription analysis revealed that SKI-73 alters epigenetic plasticity and that the subpopulation of cells that is reduced upon SKI-73 treatment is similar to that of freshly isolated invasive cells. The experimental approach used to arrive at this conclusion will be applicable to studies involving other epigenetic tool compounds. Overall this is an excellent study on the development of CARM1-selective chemical probe with potential for the treatment of metastatic breast cancer.

Decision letter after peer review:

Thank you for submitting your article "A chemical probe of CARM1 alters epigenetic plasticity against breast cancer cell invasion" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Wilfred van der Donk as Reviewing Editor and Philip Cole as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Paul R Thompson; Mark Bedford.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

This manuscript describes the development of SKI-73 as a prodrug that releases two potent and selective inhibitors of protein arginine methyltransferase 4 (PRMT4 or CARM1) in cells. Using 6'-homosinefungin (HSF) as a core scaffold, the authors developed two inhibitors (2a and 5a) with sub-nanomolar potency for CARM1 and >10-fold selectivity over 7 human PRMTs and 26 methyltransferases of other classes. A related compound that lacks a 6'-methylene group (5b) exhibited less activity. Two other CARM1 chemical probes have been identified previously – EZM2302 and TP-064. Importantly, all three of these chemical probes have distinct molecular scaffolds. The current study shows that compounds 2a and 5a compete for the SAM binding site, unlike the other two inhibitors that target the substrate arginine binding pocket. The authors masked the primary amine of 5a to develop SKI-73, a cell permeable pro-drug. Surprisingly, SKI-73 generated the parent inhibitor 5a in MDA-MB-231 cells along with 2a and these compounds accumulated to relatively high concentrations. They potently inhibited methylation of BAF155 in MCF-7 and MDA-MB-231 cells and PABP1 in MDA-MB-231 cells, two well-known CARM1 substrates. Furthermore, SKI-73 efficiently blocked invasion of MDA-MB-231 without affecting proliferation. This strategy potentially delivers a new class of compounds for inhibiting methyltransferases. Detailed single-cell RNA-seq analysis revealed that SKI-73 alters epigenetic plasticity and that the subpopulation of cells that is lost/reduced upon SKI-73 treatment is similar to that of freshly isolated invasive cells. This approach will be applicable to studies involving other epigenetic tool compounds. Overall this is an excellent study on the development of CARM1-selective chemical probe with potential for the treatment of metastatic breast cancer. The work is potentially suitable for publication in eLife after the authors address a number of points raised during review.

Essential revisions:

1) The authors should determine the stability of 6a in microsomes to better account for the formation of 2a from 6a in cells.

2) In subsection “Characterization of 6a (SKI-73) as a chemical probe of CARM1” the authors mention that 6b, the negative control, also is processed to 5b and 2b. Therefore, the authors should provide information on the selectivity of 2b in Figure 1. Only data is shown for 5b, which appears quite selective and has a relatively low IC50. The assumption in the discussion is that 2b and 5b will not bind (the authors state that they interact poorly with CARM1), but this is not actually shown.

3) Compound 2a seems to be a fairly good inhibitor of SMYD2 (Figure 1C). When SKI-73 is taken up by cells and processed, it primarily results in the accumulation of 2a (Figure 5—figure supplement 6). Thus, part of the phenotype seen in the epigenetic reprogramming studies may be due to SMYD2 inhibition. This issue needs to be addressed in the following way:

a) Are SMYD2 methylation sites impacted by the treatment of SKI-73? For this experiment, methyl-specific antibodies to known non-histone substrates (like p53, Rb or HSP90) should be tested;

b) Tagged SMYD2 (or endogenous) should be tested in the CETSA assay as performed in Figure 4F;

c) The issue of potential SKI-73 off-target effects should be addressed in the discussion.

4) Figure 6C shows a major shift in the subpopulation of cells that display SKI-73 specific depletion. This finding is nicely linked to the subpopulation of freshly isolated invasive cells. The other dramatic change is in the "SKI-73 specific emerging" cell population (3,4,5,6,16). Is there anything unique about these subpopulations of cells?

5) In general, the medicinal chemistry and compound design are not clearly described and this part of the paper is not easy to follow. The authors should be able to be explain the reasoning and design in a clearer manner. Similarly, the details of the biochemical and cellular assays should be better described and discussed.

https://doi.org/10.7554/eLife.47110.sa1

Author response

Essential revisions:

1) The authors should determine the stability of 6a in microsomes to better account for the formation of 2a from 6a in cells.

Thanks, reviewers, for this excellent suggestion. We have conducted this experiment (Figure 5—figure supplement 5, Materials and methods). While we can observe the consumption of 6a and the production of 5a in the presence of microsomes, the production of 2a could not be observed. This result suggests that the activity to produce 2a is absent in microsomes but present in breast cancer cells. We have included and discussed these results in the third paragraph of the session “Characterization of 6a (SKI-73) as a chemical probe of CARM1”.

2) In subsection “Characterization of 6a (SKI-73) as a chemical probe of CARM1” the authors mention that 6b, the negative control, also is processed to 5b and 2b. Therefore, the authors should provide information on the selectivity of 2b in Figure 1. Only data is shown for 5b, which appears quite selective and has a relatively low IC50. The assumption in the discussion is that 2b and 5b will not bind (the authors state that they interact poorly with CARM1), but this is not actually shown.

We apologized for this ignorance. Some of the IC50 values including the IC50 against CARM1 were reported by us (Zheng et al., 2012). We thus measured the rest IC50 and combined these data in Figure 1C and Supplementary file—table S1.

3) Compound 2a seems to be a fairly good inhibitor of SMYD2 (Figure 1C). When SKI-73 is taken up by cells and processed, it primarily results in the accumulation of 2a (Figure 5—figure supplement 6). Thus, part of the phenotype seen in the epigenetic reprogramming studies may be due to SMYD2 inhibition. This issue needs to be addressed in the following way:

a) Are SMYD2 methylation sites impacted by the treatment of SKI-73? For this experiment, methyl-specific antibodies to known non-histone substrates (like p53, Rb or HSP90) should be tested;

b) Tagged SMYD2 (or endogenous) should be tested in the CETSA assay as performed in Figure 4F;

c) The issue of potential SKI-73 off-target effects should be addressed in the discussion.

Thanks to the reviewer for this excellent point. We noted that, while 2a’s IC50 against SMYD2 is only 10-fold lower that against CARM1, Kd value of SMYD2 to bind SAM is 4-fold lower than that of CARM1. The combined effect leads to 37-fold larger Kd,2a/Kd,SAM ratio of SMYD2 relative to that of CARM1. We argue that the Kd,2a/Kd,SAM ratios are more relevant to evaluate the selectivity and 2a inhibits SMYD2 at much higher concentrations than CARM1. When we planed the experiments to directly examine SMYD2-mediated methylation as suggested by reviewers, we encountered the challenge because the reported experiments were conducted by overexpressing SMYD2 targets in the presence of high-level or overexpressed SMYD2 (Sweis et al., 2015). Despite this difficulty, for the methylation depletion assay as well as the CETSA assay, we agree that 2a might show the off-target effect at extremely high concentrations. To evaluate whether the concentrations of 2a used in our cellular assays are sufficient to inhibit SMYD2, we modeled the occupancy of 2a on SMYD2 in a similar manner as described for 2a’s occupancy on CARM1 (Figure 5E versus Figure 5—figure supplement 4). At the EC50 (1.3 µM) against cancer cell invasion (Figure 6A), 2a’s occupancy on SMDY2 is only around 10%. We have included and discussed these points in the second paragraph of the session “Characterization of 6a (SKI-73) as a chemical probe of CARM1”.

4) Figure 6C shows a major shift in the subpopulation of cells that display SKI-73 specific depletion. This finding is nicely linked to the subpopulation of freshly isolated invasive cells. The other dramatic change is in the "SKI-73 specific emerging" cell population (3,4,5,6,16). Is there anything unique about these subpopulations of cells?

Thanks to the reviewer for this suggestion. We further conducted population analysis and found that Subpopulations 4 and 16 account for 90% of the emerging subset. The transcriptional signatures (likely the associated invasion capability) of Subpopulations 4 and 16 are dramatically different from those of the freshly harvested invasive cells and the bulk population of the parental cells including the invasion-prone Subpopulation-8 (Figure 7B,D). SKI-73-specific emerging subpopulations are expected to be suppressed by CARM1 but emerge upon its inhibition, which relate to general biology of CARM1. We have included this discussion in the first paragraph of the session “CARM1-associated epigenetic plasticity of breast cancer cells with single-cell resolution”; the second paragraph of the session “Identification of CARM1-dependent, invasion-prone subpopulations of breast cancer cells”.

5) In general, the medicinal chemistry and compound design are not clearly described and this part of the paper is not easy to follow. The authors should be able to be explain the reasoning and design in a clearer manner. Similarly, the details of the biochemical and cellular assays should be better described and discussed.

Thanks to the reviewer for this concern. We have included more details/rationales of medicinal chemistry and assays in the sessions of “Development of 6′-homosinefungin derivatives as potent and selective CARM1 inhibitors” and “Modes of interaction of 6′-homosinefungin derivatives as CARM1 inhibitors”.

https://doi.org/10.7554/eLife.47110.sa2

Article and author information

Author details

  1. Xiao-Chuan Cai

    Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration
    Competing interests
    No competing interests declared
  2. Tuo Zhang

    Genomics Resources Core Facility, Weill Cornell Medical College, Cornell University, New York, United States
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared
  3. Eui-jun Kim

    McArdle Laboratory for Cancer Research, University of Wisconsin-Madison, Madison, United States
    Contribution
    Formal analysis, Funding acquisition, Validation, Investigation, Methodology
    Competing interests
    No competing interests declared
  4. Ming Jiang

    1. Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
    2. Program of Pharmacology, Weill Cornell Medical College of Cornell University, New York, United States
    Contribution
    Validation, Investigation
    Competing interests
    No competing interests declared
  5. Ke Wang

    Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Contribution
    Resources, Validation, Investigation, Methodology
    Competing interests
    No competing interests declared
  6. Junyi Wang

    Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Contribution
    Validation, Investigation
    Competing interests
    No competing interests declared
  7. Shi Chen

    1. Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
    2. Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, United States
    Contribution
    Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5860-2616
  8. Nawei Zhang

    1. Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
    2. Department of Obstetrics and Gynecology, Chaoyang Hospital, Affiliation Hospital of Capital Medical University, Beijing, China
    Contribution
    Validation, Investigation
    Competing interests
    No competing interests declared
  9. Hong Wu

    Structural Genomics Consortium, University of Toronto, Toronto, Canada
    Contribution
    Validation, Investigation
    Competing interests
    No competing interests declared
  10. Fengling Li

    Structural Genomics Consortium, University of Toronto, Toronto, Canada
    Contribution
    Validation, Investigation
    Competing interests
    No competing interests declared
  11. Carlo C dela Seña

    Structural Genomics Consortium, University of Toronto, Toronto, Canada
    Contribution
    Validation, Investigation
    Competing interests
    No competing interests declared
  12. Hong Zeng

    Structural Genomics Consortium, University of Toronto, Toronto, Canada
    Contribution
    Validation, Investigation
    Competing interests
    No competing interests declared
  13. Victor Vivcharuk

    Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, United States
    Contribution
    Validation, Investigation, Methodology
    Competing interests
    No competing interests declared
  14. Xiang Niu

    1. Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
    2. Tri-Institutional PhD Program in Computational Biology and Medicine, Memorial Sloan Kettering Cancer Center, New York, United States
    Contribution
    Conceptualization
    Competing interests
    No competing interests declared
  15. Weihong Zheng

    Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  16. Jonghan P Lee

    1. Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
    2. Tri-Institutional PhD Program in Chemical Biology, Memorial Sloan Kettering Cancer Center, New York, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  17. Yuling Chen

    Center for Synthetic and Systematic Biology, School of Life Sciences, Tsinghua University, Beijing, China
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  18. Dalia Barsyte

    Structural Genomics Consortium, University of Toronto, Toronto, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  19. Magda Szewczyk

    Structural Genomics Consortium, University of Toronto, Toronto, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  20. Taraneh Hajian

    Structural Genomics Consortium, University of Toronto, Toronto, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  21. Glorymar Ibáñez

    Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  22. Aiping Dong

    Structural Genomics Consortium, University of Toronto, Toronto, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  23. Ludmila Dombrovski

    Structural Genomics Consortium, University of Toronto, Toronto, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  24. Zhenyu Zhang (deceased)

    Department of Obstetrics and Gynecology, Chaoyang Hospital, Affiliation Hospital of Capital Medical University, Beijing, China
    Contribution
    Conceptualization, Funding acquisition
    Competing interests
    No competing interests declared
  25. Haiteng Deng

    1. Structural Genomics Consortium, University of Toronto, Toronto, Canada
    2. Center for Synthetic and Systematic Biology, School of Life Sciences, Tsinghua University, Beijing, China
    Contribution
    Conceptualization, Resources, Funding acquisition
    Competing interests
    No competing interests declared
  26. Jinrong Min

    1. Structural Genomics Consortium, University of Toronto, Toronto, Canada
    2. Department of Physiology, University of Toronto, Toronto, Canada
    Contribution
    Conceptualization, Resources, Funding acquisition
    Competing interests
    No competing interests declared
  27. Cheryl H Arrowsmith

    1. Structural Genomics Consortium, University of Toronto, Toronto, Canada
    2. Princess Margaret Cancer Centre, Department of Medical Biophysics, University of Toronto, Toronto, Canada
    Contribution
    Supervision, Funding acquisition
    Competing interests
    No competing interests declared
  28. Linas Mazutis

    Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  29. Lei Shi

    Department of Physiology and Biophysics, Weill Cornell Medical College of Cornell University, New York, United States
    Contribution
    Conceptualization, Resources, Supervision, Methodology
    Competing interests
    No competing interests declared
  30. Masoud Vedadi

    1. Structural Genomics Consortium, University of Toronto, Toronto, Canada
    2. Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada
    Contribution
    Supervision, Validation, Investigation
    Competing interests
    No competing interests declared
  31. Peter J Brown

    Structural Genomics Consortium, University of Toronto, Toronto, Canada
    Contribution
    Resources, Supervision, Funding acquisition
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8454-0367
  32. Jenny Xiang

    Genomics Resources Core Facility, Weill Cornell Medical College, Cornell University, New York, United States
    Contribution
    Resources, Supervision, Funding acquisition
    Competing interests
    No competing interests declared
  33. Li-Xuan Qin

    Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology
    Competing interests
    No competing interests declared
  34. Wei Xu

    McArdle Laboratory for Cancer Research, University of Wisconsin-Madison, Madison, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition
    Competing interests
    No competing interests declared
  35. Minkui Luo

    1. Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, United States
    2. Program of Pharmacology, Weill Cornell Medical College of Cornell University, New York, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration
    For correspondence
    luom@mskcc.org
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7409-7034

Funding

National Institutes of Health (R01GM096056)

  • Minkui Luo

National Institutes of Health (R01GM120570)

  • Minkui Luo

National Cancer Institute (5P30 CA008748)

  • Minkui Luo

National Cancer Institute (R01CA236356)

  • Wei Xu

National Cancer Institute (R01CA213293)

  • Wei Xu

Starr Cancer Consortium (I8-A8-058)

  • Minkui Luo

Memorial Sloan Kettering Cancer Center (Functional Genomics Initiative)

  • Minkui Luo

Mr William H Goodwin and Mrs Alice Goodwin Commonwealth Foundation for Cancer Research

  • Minkui Luo

Memorial Sloan Kettering Cancer Center (Metastasis and Tumor Ecosystems Center)

  • Minkui Luo

Susan G. Komen (PDF17481306)

  • Eui-jun Kim

Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support (YangFan Project - ZYLX201713)

  • Zhenyu Zhang

The Structural Genomics Consortium

  • Peter J Brown

National Institutes of Health (R35GM131858)

  • Minkui Luo

Memorial Sloan Kettering Cancer Center (Experimental Therapeutics Center)

  • Minkui Luo

National Cancer Institute (CA214845)

  • Li-Xuan Qin

National Cancer Institute (CA008748)

  • Li-Xuan Qin

Sloan Kettering Institute

  • Minkui Luo

Tri-institutional PhD Program in Chemical Biology

  • Shi Chen

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors thank Christina Leslie for suggesting scRNA-seq analysis. Funding has been provided by the US National Institutes of Health (ML: R01GM096056, R01GM120570, R35GM131858), the US National Cancer Institute (ML: 5P30 CA008748; WX: R01CA236356, R01CA213293; LXQ: CA214845, CA008748), the Starr Cancer Consortium (ML), the MSKCC Functional Genomics Initiative (ML), the Sloan Kettering Institute (ML), the Mr. William H Goodwin and Mrs. Alice Goodwin Commonwealth Foundation for Cancer Research, the Experimental Therapeutics Center of Memorial Sloan Kettering Cancer Center (ML), the MSKCC Metastasis and Tumor Ecosystems Center (ML), the Tri-Institutional PhD Program in Chemical Biology (SC), the Susan G Komen Foundation (EJK: PDF17481306), and Special Funding of Beijing Municipal Administration of Hospitals Clinical Medicine Development (YangFan Project) (ZZ: ZYLX201713). The Structural Genomics Consortium is a registered charity (no. 1097737) that receives funds from AbbVie, Bayer Pharma AG, Boehringer Ingelheim, the Canada Foundation for Innovation, the Eshelman Institute for Innovation, Genome Canada, the Innovative Medicines Initiative (EU/EFPIA) (ULTRA-DD grant no. 115766); Janssen, Merck KGaA (Darmstadt, Germany), MSD, Novartis Pharma AG, the Ontario Ministry of Economic Development and Innovation, Pfizer, the São Paulo Research Foundation-FAPESP, Takeda, and the Wellcome Trust. The X-ray structure results for CARM1 are derived from work performed at Argonne National Laboratory, Structural Biology Center (SBC) at the Advanced Photon Source. SBC-CAT is operated by U Chicago Argonne, LLC, for the US Department of Energy, Office of Biological and Environmental Research under contract DE-AC02-06CH11357. These experiments were performed using beamline 08ID-1 at the Canadian Light Source, which is supported by the Canada Foundation for Innovation, Natural Sciences and Engineering Research Council of Canada, the University of Saskatchewan, the Government of Saskatchewan, Western Economic Diversification Canada, the National Research Council Canada, and the Canadian Institutes of Health Research.

Dr. Zhang unfortunately passed away during the revision of this manuscript. 

Senior Editor

  1. Philip A Cole, Harvard Medical School, United States

Reviewing Editor

  1. Wilfred A van der Donk, University of Illinois, United States

Reviewers

  1. Paul R Thompson, University of Massachusetts Medical School, United States
  2. Mark Bedford

Publication history

  1. Received: March 24, 2019
  2. Accepted: October 27, 2019
  3. Accepted Manuscript published: October 28, 2019 (version 1)
  4. Version of Record published: December 17, 2019 (version 2)

Copyright

© 2019, Cai et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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