Mapping Protein Targets of Bioactive Small Molecules Using Lipid

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Mapping Protein Targets of Bioactive Small Molecules Using LipidBased Chemical Proteomics Kenneth M. Lum,†,∥ Yoshiaki Sato,†,∥ Brittney A. Beyer,§ Warren C. Plaisted,‡ Justin L. Anglin,‡ Luke L. Lairson,*,§ and Benjamin F. Cravatt*,† †

Department of Molecular Medicine, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, California 92037, United States ‡ California Institute for Biomedical Research, La Jolla, California 92037, United States § Department of Chemistry, The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, California 92037, United States S Supporting Information *

ABSTRACT: Lipids play critical roles in cell biology, often through direct interactions with proteins. We recently described the use of photoreactive lipid probes combined with quantitative mass spectrometry to globally map lipid−protein interactions, and the effects of drugs on these interactions, in cells. Here, we investigate the broader potential of lipid-based chemical proteomic probes for determining the cellular targets of biologically active small molecules, including natural product derivatives and repurposed drugs of ill-defined mechanisms. We identify the prostaglandin-regulatory enzyme PTGR2 as a target of the antidiabetic hops derivative KDT501 and show that miconazole an antifungal drug that attenuates disease severity in preclinical models of multiple sclerosisinhibits SGPL1, an enzyme that degrades the signaling lipid sphingosine-1-phosphate, drug analogues of which are used to treat multiple sclerosis in humans. Our findings highlight the versatility of lipid-based chemical proteomics probes for mapping small molecule−protein interactions in human cells to gain mechanistic understanding of bioactive compounds.

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pids,11,13 and fatty acids.6,8,10 Using fatty acid-based probes (arachidonic acid: A-DA, 1 and AEA-DA, 2; oleic acid: OEADA, 3; Figure 1), we generated large-scale lipid−protein interaction maps in human cells, along with initial ligandability profiles of these maps, as reflected by the displacement of lipid probe binding to proteins by small-molecule drug competitors (Figure 1).10 LiPIP identified both the established targets of drugs and also off-target proteins, many of which were previously not known to interact with small molecules. Encouraged by the potential for LiPIP to coordinately assess lipid and drug interactions for many hundreds of proteins in parallel in human cells, we asked herein whether this method could more generally facilitate mapping of protein targets of bioactive small molecules, including compounds of unknown mechanisms of action (MOA). Specifically, we examine (1) emerging chemical probes (SR-4559 and SR-4995) for ABHD5 (α/β hydrolase domain-containing protein 5),15 a nonenzymatic regulator of lipolysis;16 (2) KDT501, a natural product derivative of hops with antidiabetic properties, but lacking molecular targets;17 and (3) miconazole, an antifungal drug that stimulates oligodendrocyte differentiation and

ipids serve many important cellular functions, including, as broadly defined, energy storage, structural organization, and signaling. These functions are often achieved through interactions with proteins, for example, as substrates of enzymes, ligands for receptors, and/or components of integral membrane protein complexes. Lipid−protein interactions are also relevant to drug development, as many therapeutic targets are components of lipid metabolic and/or signaling pathways,1,2 as well as often being physically associated with membrane compartments of the cell.3 The large-scale profiling of lipids, or lipidomics, has greatly enriched our understanding of the composition, regulation, and function of diverse lipid networks in human biology and disease.4,5 We know much less, however, about how lipids interact with proteins in human cells and the extent to which these points of intersection can serve as targets for chemical probe and drug development. Inspired by these challenges, we and others have introduced chemical proteomic methods to inventory lipid−protein interactions on a global scale in living cells.6−13 In these approaches, which we refer to hereafter as LiPIP (Lipid−Protein Interaction Profiling), “clickable” photoreactive lipid probes are synthesized and coupled with quantitative mass spectrometry to globally assess lipid−protein interactions,6−14 as well as their sensitivity to drug binding in human cells.10 Examples of LiPIP probes developed to date include those based on sterols,9 sphingolipids,7,8 phospholi© XXXX American Chemical Society

Received: July 10, 2017 Accepted: August 30, 2017

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DOI: 10.1021/acschembio.7b00581 ACS Chem. Biol. XXXX, XXX, XXX−XXX

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Figure 1. Lipid−Protein Interaction Profiling (LiPIP). (a) Workflow for mapping small molecule−protein interactions by LiPIP. Isotopically light and heavy (SILAC) cells are treated with vehicle or small molecule, respectively, together with a photoreactive lipid probe. The cells are then irradiated with ultraviolet (UV) light to induce covalent cross-linking and lysed, and equal amounts of light and heavy lysate proteins are combined. Probe-modified proteins are conjugated to an azide-biotin tag51 by copper-catalyzed azide−alkyne cycloaddition (CuAAC, or click) chemistry,52 enriched with streptavidin, and digested with trypsin for 2D-LC-MS/MS analysis of the corresponding tryptic peptides. Targets of the small molecules are designated as proteins that show SILAC ratios (light/heavy) of ≥3.0. (b) Chemical structures of the lipid probes used in this study, based on arachidonoyl (AEA-DA and A-DA) or oleoyl (OEA-DA) acyl chains, and containing photoreactive diazirine and clickable alkyne groups.

arachidonoyl LiPIP probe 1, termed A-DA (10 μM, 30 min), which contains both a diazirine photoreactive group and an alkyne clickable handle10 (Figure 1b). Cells were then exposed to UV light-induced photo-cross-linking and lysed and isotopically differentiated lysates combined in equal proportions for chemical proteomic processing and analysis, as summarized in Figure 1a and described previously.10 A similar set of studies was also conducted for the ABHD5 ligands (at 25 μM) with complementary arachidonoyl probe 2termed AEADAwhere both the diazirine and alkyne group are placed on the acyl chain of the probe10 (Figure 1b). The combined chemical proteomic experiments identified ABHD5 as a top cellular target of both SR-4559 and SR-4995 (Figure 2c and Figure S1). Each ligand produced substantial engagement of ABHD5 at both 5 and 25 μM, blocking the A-DA or AEA-DA probe interaction by >80%, with SR-4995 exhibiting a slightly higher engagement than SR-4559 (Figure 2c), consistent with the respective potencies reported previously for these ligands.15 Both ABHD5 ligands showed good selectivity across the more than 600 lipid probe-binding proteins quantified in the chemical proteomic experiments, with few if any off-target proteins observed at 5 μM test concentration (Figure 2c and Table S1). At 25 μM, the ligands showed more complete engagement of ABHD5 (Figure 2c) but also blocked LiPIP probe interactions of a handful of other proteins (SILAC ratios ≥ 3.0; Figure 2c,d). Most of these proteins were targeted by only one of the two ABHD5 ligands (Figure 2d) and, in all cases, with the exceptions of ABHD10 and EPHX2 (off-targets of SR-4559 and SR-4995, respectively), showed substantially weaker LiPIP probe displacement values compared to ABHD5 (Figure 2d). Notably, many of the off-targets were identified by one, but not both, of the LiPIP probes (Figure 2d), despite being enriched by both probes.10 These results, which are consistent with previous studies using LiPIP probes for mapping drug engagement,10 indicate that individual LiPIP

reverses disease severity in rodent models of multiple sclerosis.18 In each study, we identified and verified protein targets of the tested small molecules, revealing, for instance, that KDT501 and miconazole are inhibitors of the lipidmetabolizing enzymes PTGR2 and SGPL1, respectively.

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RESULTS AND DISCUSSION Profiling Emergent Chemical Probes for ABHD5. In our previous studies, we identified ABHD5 as a target of fatty acid-based LiPIP probes.10 ABHD5 is an unusual protein in that it possesses an α/β hydrolase fold but lacks the catalytic serine nucleophile (mutated to asparagine) that imparts hydrolytic function to enzymes from this class. Deleterious mutations in ABHD5 lead to the neutral lipid storage disease Chanarin Dorfman syndrome, typified by ectopic accumulation of lipids in several tissues,19 and biochemical and cell biological studies have shown that this protein acts as a positive regulator of the triglyceride lipase PNPLA2 (patatin-like phospholipase domain containing 2; also known as adipose triglyceride lipase ATGL; Figure 2a).20 The binding and activation of PNPLA2 by ABHD5 is negatively regulated by perilipin (PLIN) proteins (Figure 2a),15,16,19,21 and inhibitors of ABHD5-PLIN interactions may thus serve as chemical probes and pharmacotherapies to promote fat lipolysis.15 Recently, Sanders and colleagues15 reported the discovery of two structurally distinct ligands for ABHD5SR-4559, 4, and SR-4995, 5 (Figure 2b)that block PLIN interactions and stimulate lipolysis in adipocytes and muscle cells. Here, we asked whether LiPIP could serve as a platform to determine the engagement of endogenous ABHD5 by SR-4559 and SR-4995, as well as the broader proteome-wide selectivity of these compounds, in human cells. HEK293T cells isotopically labeled with heavy or light amino acids were treated with DMSO or ABHD5 ligand (SR-4559 or SR-4995 tested at 5 and 25 μM), respectively, followed by B


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Figure 2. LiPIP of ABHD5 ligands SR-4559 and SR-4995. (a) Scheme depicting the role of ABHD5 in regulating intracellular lipolysis. SR-4559 and SR-4995 stimulate lipolysis by inhibiting the ABHD5-PLIN interaction. (b) Chemical structures of the ABHD5 ligands SR-4559 and SR-4995. (c) SILAC ratio plots of SR-4559 and SR-4995 (5 and 25 μM) determined by LiPIP with the A-DA probe (10 μM) in HEK293T cells. See Figure S1 for similar results obtained in LiPIP experiments with the AEA-DA probe. (d) Heatmap summarizing the targets (SILAC ratio ≥3.0) of SR-4559 and SR-4995 (25 μM) determined by LiPIP. (e) Partial primary sequence of ABHD5 (human numbering) that includes (1) the AEA-DA-labeled tryptic peptide (underlined) and (2) the NBD-HE-HP reactive Tyr330 (highlighted in yellow with an asterisk). The mouse and human orthologs of ABHD5 have 100% sequence identity in this region. (f) Top image, representative in-gel fluorescence image showing competitive blockade of A-DA (5 μM) labeling of recombinant ABHD5 in HEK293T cells by SR-4559 and SR-4995 (5 and 25 μM). Fluorescence image corresponds to signals of A-DA-labeled proteins following CuAAC conjugation to a rhodamine-azide tag.51 Bottom image, Western blot confirming the expression of ABHD5 in transfected HEK293T cells. M, mock-transfected HEK293T cells.

probes may bind to the same protein at different (or, in some cases, multiple) sites, only one of which may be sensitive to drug action. Our lipid−protein interaction maps also confirmed that the ABHD5-binding protein PNPLA2 was not directly engaged by SR-4559 and SR-4995,15 as this protein showed an unaltered lipid probe enrichment ratio in the presence of these compounds (Figure 2d). Previous studies have shown that SR-4559 and SR-4995 block the reactivity of ABHD5 with an electrophilic pnitrophenyl-phosphonate probe NBD-HE-HP.15 This probe was found to react with Tyr330 in ABHD5 (asterisked residue, Figure 2e), a residue that is predicted by recent homology modeling to be part of the ABHD5 “active site,” or binding pocket, shared with other α/β hydrolase proteins.21 Interestingly, in our previous work,10 we localized the site of AEA-DA probe labeling on mouse ABHD5 to a tryptic peptide in this same regionamino acids 321−344 (mouse numbering; underlined sequence, Figure 2e). That the ABHD5 ligands SR-4559 and SR-4995 compete both electrophilic (NBD-HE-

HP) and photoreactive (LiPIP) probe interactions provides good evidence that these ligands bind to the “active site” of ABHD5, even though this region lacks the catalytic residues found in other α/β hydrolases. Finally, we confirmed that LiPIP probe engagement of recombinant ABHD5, as well as the competitive blockade of this interaction by SR-4559 and SR4995, can also be visualized in ABHD5-transfected cells by gelbased profiling (Figure 2f), which may offer a convenient assay format for discovering and optimizing additional ABHD5 ligands. Identifying Targets of the Antidiabetic Agent KDT501. Having demonstrated herein and in a past study10 that LiPIP can serve as a cellular engagement assay to identify the established primary targets of small-molecule ligands, as well as to map off-targets, for several drugs and chemical probes, we next sought to apply this platform to discover protein targets of bioactive compounds with poorly understood mechanisms of action. As a first case study, we selected KDT501, 6 (Figure 3a), a natural product derivative extracted C


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Figure 3. Discovery of PTGR2 as a target of KDT501. (a) Chemical structure of antidiabetic hops derivative KDT501. (b) SILAC ratio plots of LiPIP experiments of HEK293T cells treated with KDT501 (25 or 50 μM) and A-DA or AEA-DA probes (10 μM). (c) Top image, representative in-gel fluorescence images showing concentration-dependent blockade of lipid probe labeling of recombinant PTGR2 (A-DA probe, 5 μM) and BAIAP2 (AEA-DA, 5 μM) in HEK293T cells by KDT501. Bottom image, Western blots confirming the expression of PTGR2 and BAIAP2 in transfected HEK293T cells. M, METAP2-transfected (control) HEK293T cells. (d) Concentration-dependent blockade of A-DA probe labeling (5 μM) of recombinant PTGR2 in transfected HEK293T lysates by KDT501. Data shown are mean fluorescence intensity values (normalized to DMSO control) ± SEM (n = 2/group). (e) Scheme showing PTGR2-catalyzed reduction of 15-keto PGE2 to 13,14-dihydro-15-keto PGE2 (dhkPGE2). (f) Representation of three-dimensional structure of human PTGR2 (PDB: 2ZB4) in complex with 15-keto PGE2 (yellow) and NAPDH rendered as balls-and-sticks.25 The AEA-DA-labeled tryptic peptides in PTGR2 mapped previously10 are shown in blue. (g) Concentrationdependent blockade of the catalytic activity of recombinant PTGR2 by KDT501 as measured using a 15-keto PGE2 substrate assay. Data shown as mean dhk-PGE2 formation (% DMSO control) ± SEM (n = 3/group). (h) Left image, relative 15-keto PGE2 reductase activity of lysates from HEK293T cells expressing PTGR2 (or control protein METAP2). Right image, concentration-dependent blockade of the 15-keto PGE2 reductase activity of PTGR2-transfected cell lysates by KDT501. Data shown as mean dhk-PGE2 formation (% DMSO control) ± SEM (n = 3/group). D


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Figure 4. Discovery of SGPL1 as a target of miconazole. (a) Chemical structures of miconazole and voriconazole. (b) SILAC ratio plots of miconazole and voriconazole (30 μM) determined by LiPIP with the AEA-DA (10 μM) probe in Neuro2a cells. (c) Representative MS1 chromatograms and SILAC ratios of protein targets of miconazole (SILAC ratio ≥3.0) determined by LiPIP. None of these proteins were competed by voriconazole in LiPIP experiments (SILAC ratio ∼ 1.0). (d) Scheme showing intracellular synthetic and degradation pathways for S1P. (e) Top image, representative in-gel fluorescence image showing the concentration-dependent blockade of AEA-DA (2 μM) labeling of recombinant E


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mSGPL1 in HEK293T cells by miconazole, but not voriconazole. Bottom image, Western blot confirming the expression of mSGPL1 in transfected HEK293T cells. M, mock-transfected HEK293T cells. (f) Representation of homodimeric hSGPL1 (PDB: 4Q6R) showing the pyridoxal 5′phosphate cofactor covalently bound to K311 (“balls-and-sticks”) and the active-site inhibitor (cyan).35 The residues corresponding to the AEA-DAlabeled tryptic peptide (inferred from site-of-labeling experiments in Neuro2a)10 are highlighted in navy blue. (g) Left image, relative SGPL1 activity of lysates from mSGPL1- or mock-transfected HEK293T cells. Right image, concentration-dependent blockade of the catalytic activity of mSGPL1transfected cell lysates by miconazole. Data shown as mean fluorescence intensity (% DMSO control) ± SEM (n = 3/group). (h) Plot of relative cellular amounts of S1P in Neuro2a cells following treatment of the indicated concentrations of miconazole or voriconazole (6 h). Data were obtained by targeted LC-MS/MS and shown as mean integration ratios of the S1P peak to internal standard peak ± SEM (n = 5/group). *p < 0.05 and ***p < 0.001 compared to DMSO-treated cells. (i) Structure of SGPL1 inhibitor, 11.35 (j) Plot of myelin basic protein positive (MBP+) cells (%) treated with compound 11, miconazole, or both; MBP is a marker of terminally differentiated oligodendrocytes. Data shown as mean ± SEM (n = 4/group); n.s = not significant, p > 0.05.

recombinant human PTGR2 protein, monitoring the amount of 13,14-dihydro-15-keto PGE2, 8 (dhk-PGE2), produced after incubating the enzyme with 15-keto PGE2 and NADPH. In this assay system, KDT501 showed concentration-dependent inhibition of dhk-PGE2 production with an IC50 value of 8.4 μM (95% CI: 4.4−16 μM; Figure 3g). We next tested lysates from HEK293T cells transfected with PTGR2 and observed a similar inhibitory effect of KDT501 (IC50, 1.8 μM; 95% CI, 1.4−2.4 μM; Figure 3h), which was in good agreement with the concentration-dependent blockade of lipid probe labeling of PTGR2 by KDT501 (see Figures 3c,d and S2). These results, taken together, indicate that KDT501 acts as an inhibitor of PTGR2. Considering that the PTGR2 substrate 15-keto-PGE2 serves as an endogenous ligand for PPAR-γ,26 blockade of PTGR2 activity could contribute in part to the adipogenic and metabolic effects of KDT501. Identifying Targets of the Oligodendrocyte-Differentiating and Remyelinating Agent Miconazole. Miconazole, 9 (Figure 4a), is an FDA-approved antifungal drug that blocks the synthesis of ergosterol, a critical component of fungal cell membranes.27 Miconazole has been found by phenotypic screening to promote oligodendrocyte precursor cell (OPC) differentiation and further demonstrated to enhance remyelination and reverse disease severity in rodent models of multiple sclerosis.18 The structurally related azole antifungal drug voriconazole, 10 (Figure 4a), was inactive in these assays.18 Miconazole, but not voriconazole, also induced changes in ERK1/2 phosphorylation in differentiating OPCs, pointing to the involvement of a mitogen-activated protein kinase (MAPK) pathway in the drug’s action;18 however, the direct protein target(s) of miconazole that mediates the differentiation of OPCs and remyelination processes in vivo remains unknown. Considering the lipophilicity of miconazole, as well as the importance of lipid pathways in oligodendrocyte function and myelination,28 we hypothesized that LiPIP may facilitate the identification of biologically relevant protein targets of miconazole in mammalian cells. We performed LiPIP experiments in mouse Neuro2a cells treated with DMSO (light) or drug (miconazole or voriconazole, 30 μM; heavy) and the lipid probes (10 μM) and designated drug-competed proteins as those showing light/ heavy SILAC ratios of ≥3.0 (dashed line, Figure 4B). From more than 500 lipid-binding proteins, we identified four that were targets of miconazole, but not voriconazoleCLPP (ATP-dependent Clp protease proteolytic subunit; SILAC ratio = 7.4), SGPL1 (sphingosine-1-phosphate lyase 1; SILAC ratio = 3.6), EPHX1 (epoxide hydrolase 1; SILAC ratio = 3.2), and TMEM97 (transmembrane protein 97; SILAC ratio = 3.1; Figure 4b,c and Table S1).

from hops that exhibits lipogenic and anti-inflammatory effects in human cells and antidiabetic activity in rodents17 and has progressed to phase 2 clinical trials.22 Another feature of KDT501 that attracted our attention was its heteroatom-rich, complex stereochemical structure, which presented synthetic challenges for direct chemical derivatization with affinity groups for target identification experiments. Whereas some of the pharmacological effects of KDT501, including lipogenesis and improvements in metabolic parameters, resemble the properties of PPAR-γ agonists,17 the compound is only a weak agonist of this nuclear receptor and induces a gene program that is distinct from the PPAR-γ agonist rosiglitazone.23 We therefore pursued the discovery of additional protein targets of KDT501 by LiPIP, where HEK293T cells were treated with KDT501 (25 and 50 μM) or DMSO and either the A-DA or AEA-DA probe (10 μM), followed by MS-based proteomic analysis. Among the several hundred lipid-interacting proteins mapped in these experiments (Table S1), only two showed a substantive blockade of enrichment in the presence of KDT501the metabolic enzyme PTGR2 (prostaglandin reductase 2) and the adaptor protein BAIAP2 (brain-specific angiogenesis inhibitor 1-associated protein 2; Figure 3b and Table S1). We recombinantly expressed both proteins in HEK293T cells and confirmed lipid probe labeling and the blockade of these interactions in a concentration-dependent manner by KDT501 (Figure 3c). PTGR2 is an annotated enzyme with established substrates, which facilitated follow-up studies on this target of KDT501. We found that KDT501 blocked A-DA probe labeling of recombinant PTGR2 in transfected cell lysates with an IC50 value of 4.0 μM (95% confidence interval (CI): 3.0−5.3 μM; Figures 3d and S2). Using this same assay, we also confirmed that the recently described PTGR2 inhibitor KNJ057,24 but not the structurally related inactive analog KNJ051, also blocked PTGR2 interactions with the A-DA probe (Figure S2). PTGR2 is an NADPH-dependent 15-oxo-prostaglandin 13,14-reductase that accepts various 15-keto prostaglandin substrates, including 15-keto PGE2, 7 (Figure 3e), and a cocrystal structure of 15-keto PGE2 bound to PTGR2 has been solved (Figure 3f).25 The lipid probe-binding site in PTGR2 (highlighted in blue, Figure 3f), inferred from mapping two prominent AEA-DA-labeled tryptic peptides (GDFVTSFYWPWQTK and AILDGNGLEK that sequentially span amino acids 93−107 of mouse PTGR2),10 is in close proximity to the enzyme’s tunnel-like substrate pocket, which contains residues (e.g., Phe99 and Tyr100) that are within 4 Å of 15-keto PGE2 (Figure 3f). On the basis of these results, we tested whether KDT501 inhibits PTGR2 activity. We first performed in vitro substrate assays using commercially available F


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We were intrigued by the elevations in cellular S1P content produced by miconazole, as there are several reports indicating that both S1P and S1P receptor agonists regulate OPC proliferation, differentiation, and migration in vitro and in vivo.32−34,37 We thus wondered whether miconazole might promote OPC differentiation, at least in part, by inhibiting SGPL1. To begin to test this concept, we evaluated a potent and structurally distinct SGPL1 inhibitor, 11, which was prepared generally as described previously35 and tested as a 7:3 mixture of two active regioisomers (7-CN and 6-CN) with the major isomer (7-CN, Figure 4i) corresponding to the more potent SGPL1 inhibitor.35 We confirmed that 11 blocked AEADA probe labeling of SGPL1 (Figure S4a) and was active in the fluorogenic substrate assay (Figure S4b). However, this compound did not promote OPC differentiation, either alone or in combination with miconazole, even when tested at concentrations far above the reported IC50 range (3−30 nM) for inhibiting SGPL1 activity (Figures 4j and S4c). We also found that our monoculture-based rat OPC differentiation assay was insensitive to S1P receptor modulators, such as (S)pFTY720, which alone, or in combination with miconazole, had no impact on OPC differentiation (Figure S4d). These data suggest that the lack of a functional effect of SGPL1 inhibitors could be due to an intrinsic insensitivity of the rat OPC system to S1P signaling, rather than the strength of the endogenous S1P tone regulated by SGPL1 in these cells. Regardless, we conclude that the miconazole-mediated promotion of rat OPC differentiation is not dependent on SGPL1 inhibition or S1P signaling, in general. It remains possible, however, that some of the in vivo effects of miconazole on OPC differentiation and/or disease progression in mouse models of multiple sclerosis may involve modulation of S1P signaling, which represents a clinically verified mechanism to suppress CNS autoimmunity.31 Chemical Proteomic Assessment of LiPIP Probes with Simplified Structures. Our LiPIP studies to date have mostly employed the arachidonate-based probes, A-DA and AEADA,10 which, despite their utility, pose a considerable synthetic challenge, especially for scaled-up production of the probes. We therefore wondered whether synthetically more tractable oleoyl probes, such as OEA-DA (Figure 1b)might also serve for mapping protein targets of bioactive small molecules by LiPIP. We first inventoried the protein targets of the OEA-DA probe in HEK293T and Neuro2a cells by quantitative MSbased proteomics, resulting in the identification of ∼450−850 proteins in each cell type that were substantially enriched (≥3.0) by the OEA-DA probe in a UV light-dependent manner (UV vs no-UV experiments; Figure 5a and Table S2). In contrast, virtually no proteins showed ratios of >3.0 in control experiments comparing UV versus UV conditions with the OEA-DA probe (Figure S5a). Many OEA-DA targets (> 50%) were also enriched by the AEA-DA probe in previous studies (Figure 5b and Figure S5b),10 indicating good general overlap in proteomic coverage for the different probes. Considering further the propensity for stochastic protein detection in datadependent MS-based proteomic experiments,38 the shared proteomic footprint for oleoyl and arachidonoyl LiPIP probes is likely much higher. Supporting this conclusion, only a handful of protein targets identified exclusively in UV vs no-UV experiments performed with the AEA-DA probe (yellow group, Figure 5b) were also selectively enriched by the AEA-DA probe in direct comparison experiments against the OEA-DA probe (Figure S5c; also see previous studies10).

Of the four lipid-binding proteins targeted by miconazole, SGPL1 stood out because of its involvement in sphingolipid metabolism and signaling.29 Specifically, SGPL1 terminates the signaling function of sphingosine 1-phosphate (S1P) by degrading this bioactive lipid to hexadecenal and phosphoethanolamine (Figure 4d).29 S1P is a key lipid transmitter that acts through a set of G-protein-coupled receptors to regulate diverse physiological processes,30 including lymphocyte egress from peripheral lymphoid organs. This latter immunosuppressive function is thought to underlie, at least in part, the diseasemodifying activity of S1P receptor modulators, such as FTY720, in multiple sclerosis.30,31 These drugs, however, may also have direct effects on the nervous system. FTY720, for instance, has been shown to promote OPC differentiation in culture and mice, which could further curtail disease progression in multiple sclerosis.32−34 We verified miconazole interactions with recombinant mouse (m) SGPL1 expressed in HEK293T cells, where this drug, but not voriconazole, produced a concentration-dependent blockade of AEA-DA probe binding to mSGPL1 (Figure 4e). Similar results were obtained with rat (r) and human (h) SGPL1, although the latter protein appeared less sensitive to miconazole than the rodent enzymes (Figure S3a,b). We estimated IC50 values for miconazole blockade of AEA-DASGPL1 interactions of 10−50 μM, depending on the SGPL1 ortholog (Figures 4e and S3a,b). Miconazole, but not voriconazole, also blocked AEA-DA probe interactions with mSGPL1 in vitro (Figure S3c). We further found that S1P inhibited AEA-DA binding to SGPL1 (Figure S3c), suggesting that S1P and miconazole bind to a similar region of the enzyme. In previous studies,10 we identified two AEA-DA-modified peptides in mSGPL1LKDFEPYLEILESYSTK (amino acids 9−26) and TTGMGAIYGMAQATIDR (519−536)that map to the predicted luminal and cytoplasmic regions of the protein, respectively. On the basis of the crystal structure of an Nterminally truncated hSGPL1 (Δ1−61),35 the cytoplasmic AEA-DA-binding peptide (highlighted in blue) resides proximal to the enzyme active site, as inferred by the presence of a competitive inhibitor (highlighted in cyan) that makes contact with the catalytic lysine (K353)-pyridoxal 5′-phosphate complex (Figure 4f). This information, combined with the competitive blockade of AEA-DA binding to SGPL1 by both S1P and miconazole, led us to speculate that the latter compound may act as an SGPL1 inhibitor. Lysates from HEK293T cells heterologously expressing mSGPL1 showed robust activity with a fluorogenic S1P substrate analog36 compared to control cell lysates (Figure 4g, left) and this activity was blocked in a concentrationdependent manner by miconazole (IC50 value = 3.2 μM; 95% CI, 2.4−4.1 μM; Figure 4g, right). Generally similar results were obtained for rSGPL1 (IC50 value = 4.3 μM; 95% CI, 2.9− 6.3 μM) or hSGPL1 (IC50 value = 6.5 μM; 95% CI, 3.4−12.4 μM; Figure S3d and e), although the activity of miconazole plateaued at 40% maximal inhibition for hSGPL1. Consistent with our chemical proteomic results, voriconazole did not inhibit any of the SGPL1 enzymes when tested at concentrations up to 200 μM (Figure 4g, right, and Figure S3d and e). We also found that miconazole, but not voriconazole, increased the endogenous S1P content of Neuro2a cells (Figure 4h). These results, taken together, indicate that miconazole acts as an SGPL1 inhibitor in vitro and in cells. G


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Inset: Enlarged plot of target proteins. (c) Venn diagrams showing the degree of overlap of OEA-DA and AEA-DA10 targets in HEK293T and Neuro2a (N2a) cells. (d) Representative MS1 chromatograms and SILAC ratios of targets of bioactive small molecules investigated herein using OEA-DA or AEA-DA probes.

Having established good overlap in protein targets for oleoyl and arachidonoyl LiPIP probes in human cells, we next asked whether the OEA-DA probe could discover protein targets for the bioactive small molecules tested herein. Strikingly, we found that, for each bioactive compound, the OEA-DA data closely matched results obtained with arachidonoyl probes, verifying ABHD5 as a target of SR-4559 and SR-4995, PTGR2 and BAIAP2 as targets of KDT501, and SGPL1 as a target of miconazole, but not voriconazole (Figure 5d). The OEA-DA probe also uncovered most of the additional targets of the bioactive compounds mapped in arachidonoyl probe experiments, (e.g., EPHX2 for SR-4995 and CLPP for miconazole; Figure S5b). Taken together, these data indicate that oleoyl probes serve as suitable surrogates for arachidonoyl probes in LiPIP studies and may offer a more straightforward means to facilitate the general implementation of lipid-based chemical proteomic platforms with minimal synthetic constraints of scale.

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DISCUSSION Chemical proteomics has emerged as a powerful approach to characterize the protein interaction landscapes of bioactive small molecules. The data acquired from chemical proteomic experiments can inform on the degree of engagement of a target of interest by a small molecule, as well as identify offtargets for the compound. Chemical probes for proteome-wide investigations of compound activity are often structurally based on the bioactive small molecule itself. The field of activity-based protein profiling (ABPP), however, has demonstrated the complementary value of broad-spectrum chemical probes as reporters of small molecule−protein interactions in native biological systems. In these experiments, the chemical proteomic probe is typically unrelated in structure to the test compound but rather exhibits reactivity with a large set of mechanistically and/or structurally related proteins. More recently, we and others have shown that the concepts of ABPP can be extended to create chemical probes that engage many hundreds to thousands of proteins and protein sites. Such probes include electrophilic agents that target nucleophilic amino acids (e.g., cysteine,39,40 lysine41−43) and photoreactive probes that contain general recognition elements such as druglike fragments24,44 or natural metabolites.10,45,46 Photoreactive, clickable lipids have emerged as a particularly versatile class of chemical proteomic probes, likely reflecting the diverse array of proteins that naturally bind to, or are modified by, lipids in cells.12,47,48 We previously showed that, beyond inventorying lipid-interacting proteins, LiPIP can also identify targets and off-targets of drugs that interact with lipid-binding proteins. Here, we have extended these initial findings in several important ways. First, we have shown that LiPIP can identify protein targets of bioactive small molecules of ill-defined mechanisms. We proceeded to demonstrate in these cases that drug binding affected the activity of the target proteins (PTGR2 for KDT501, SGPL1 for miconazole), providing further evidence that LiPIP probes, and the compounds that

Figure 5. Comparative evaluation of oleoyl and arachidonoyl probes for LiPIP. (a) Combined SILAC ratio plots from UV/no-UV and control UV/UV experiments used to determine the UV-dependent targets of OEA-DA in (a) HEK293T and (b) Neuro2a cells. UVdependent targets (marked by red dots) were defined as proteins that were quantified in UV/no-UV experiments with a ratio of ≥3 and in UV/UV control experiments with a SILAC ratio between 0.5 and 2.0. H


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possess multiple binding sites for lipid probes, only a subset of which are sensitive to drug action. This feature could explain why certain small molecule−protein interactions (e.g., the discovered protein targets of ABHD5 ligand) were detected with only a subset of the lipid probes. We accordingly recommend performing LiPIP with multiple probes to maximize the likelihood of identifying protein targets for bioactive compounds in cells. We also should emphasize that lipid probe binding to a protein, and even competitive blockade of this event by a small-molecule drug, does not necessarily mean the interactions affect the function of the protein. We were encouraged that, for the protein targets investigated herein (PTGR2 and SGPL1), the discovered interacting small molecules (KDT501 and miconazole, respectively) were found to act as inhibitors, but we also acknowledge that, for other targets (e.g., BAIAP2), performing such follow-up experiments may prove more complicated in the absence of straightforward functional assays. Ultimately, it would be valuable to create drug-resistant mutants of target proteins, which could then be introduced into biological systems to assess the contributions of individual targets to drug activity. Of course, such mutants would also need to maintain wild-type protein functions, which may be challenging for cases where drugs bind to the active sites of proteins. The stimulation of lipolysis in adipocytes and muscle cells by the ABHD5 ligands SR-4559 and SR-4995 has been demonstrated previously,15 but understanding the degree of ligand engagement of ABHD5 required to produce these cellular effects remains a challenge that LiPIP is well-suited to address. Indeed, one could envision future studies where these two parameterslipolysis and LiPIP measurementsare quantitatively related across a broad concentration range of ABHD5 ligands, which may facilitate the fine-tuning of ligand activity to avoid the induction of excessive lipolysis. Importantly, this information would also be acquired alongside a global assessment of protein interactions, which has so far provided evidence that SR-4559 and SR-4995 are remarkably selective in cells. We more generally foresee such a role for LiPIP in the characterization of many chemical probes and drug candidates that interface with lipid pathways in cells.

compete their protein interactions, tend to engage proteins at functional sites. Also of note is that KDT501 is a structurally complex natural product derivative that exhibits only moderately potent activity in cells (10−50 μM). Such features would likely present challenges for target identification using more conventional affinity enrichment protocols where the drug would need to be chemically derivatized and maintain reversible binding interactions with proteins in vitro. LiPIP, on the other hand, can assess protein interactions for bioactive compounds in cells at pharmacologically relevant concentrations. That KDT501 and miconazole interacted with only a handful of the hundreds of lipid-binding proteins assayed by LiPIP in cells indicates the compounds are not promiscuous protein-binding molecules. Finally, from a technical perspective, we have shown that the protein interaction landscape and drug profiling activity of arachidonoyl-based lipid probes are shared by oleoyl-based lipid probes, which represent structurally and synthetically simplified analogues that may be more suitable for routine performance of large-scale LiPIP experiments. There may, however, be select proteins that show strong preference for interacting with one class of lipid probes, which can be discovered by performing head-to-head comparisons of protein enrichments with different lipid probes.10 The PTGR2 substrate 15-keto PGE2 has been shown to act as an endogenous agonist of PPAR-γ to enhance adipogenesis,26 and genetic silencing of PTGR2 raises cellular levels of 15-keto PGE2, leading to PPAR-γ activation.49 These previous findings, combined with our discovery that KDT501 acts as a PTGR2 inhibitor, suggest that at least some of the adipogenic and antidiabetic effects of KDT501 could be due to inhibition of PTGR2. We cannot, however, exclude the involvement of additional proteins in KDT501 action, including targets identified herein, such as BAIAP2, or others that were not detected by LiPIP. In this regard, we note that BAIAP2 is a substrate of the insulin receptor (also referred to as insulin receptor substrate p53/p58)50 and may therefore play a role in insulin signaling. The implications of identifying SGPL1 as a target of miconazole are less clear, given that we did not find evidence that this enzyme, or S1P signaling in general, affected OPC differentiation. We acknowledge that previous research has implicated S1P signaling in OPC differentiation.32−34 However, in our hands, S1P1R ligands did not induce differentiation of rat OPCs, even though these cells were responsive to miconazole. Further studies in S1P-responsive OPCs would be required to more conclusively assess the impact of SGPL1 inhibition on OPC differentiation. Regardless, considering the clinically validated activity of S1P1R modulators in multiple sclerosis,30,31 it remains possible that a blockade of SGPL1 could contribute to the beneficial effects of miconazole in animal models of this disease. It is also important to emphasize some of the limitations of LiPIP as a platform for mapping small molecule−protein interactions in cells. The most obvious shortcoming is that LiPIP is restricted to identifying protein targets that interact with lipid probes in cells. Thus, the platform is arguably most relevant for compounds that interface with lipid signaling and/ or metabolic pathways and may fail to detect drug-binding proteins that function in other areas of cellular biochemistry. There may also be cases where the LiPIP probes bind to proteins but produce low cross-linking yields, which may hinder the enrichment and detection of low-abundance proteins. Even for proteins detected by LiPIP, it is also possible that some may

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METHODS

Materials. Lipid probes were obtained from our previously described synthesis.10 ABHD5 ligands SR-4559 and SR-4995 were kind gifts from the Roush lab (Scripps Research, Florida), supplied as 10 mM stocks in DMSO. KDT501 was provided by KinDex Pharmaceuticals. Voriconazole, 15-keto PGE2, 13,14-dihydro-15-keto PGE2, and 13,14-dihydro-15-keto PGE2-d4 were purchased from Cayman Chemical Company. Miconazole was purchased from VWR International. Phosphosphingolipid standards (d18:1-S1P and d17:1S1P) were purchased from Avanti Polar Lipids, Inc. (R)-1-benzyl-4-(4(5-cyanopyridin-2-yl)-3-methylpiperazin-1-yl)phthalazine-6-carbonitrile, 11, was obtained by chemical synthesis.35 See the Supporting Information for details. Cell Culture. See the Supporting Information for details. Molecular Cloning and Protein Overexpression in HEK293T Cells. See the Supporting Information for details. Lipid Probe Labeling and Processing of Proteomes for Gel or 2D-LC-MS/MS Analysis. Cells and lysates were labeled and processed according to previous methods,10 with minor modifications. See the Supporting Information for details. Mass Spectrometric Analysis of Tryptic Peptides, Identification, and Quantification. Mass spectrometry data were acquired and processed as previously described,10,24 with minor modifications, described in full in the Supporting Information. The mass I


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(2) Samuelsson, B. (2012) Role of basic science in the development of new medicines: examples from the eicosanoid field. J. Biol. Chem. 287, 10070−10080. (3) Overington, J. P., Al-Lazikani, B., and Hopkins, A. L. (2006) How many drug targets are there? Nat. Rev. Drug Discovery 5, 993−996. (4) Fahy, E., Subramaniam, S., Brown, H. A., Glass, C. K., Merrill, A. H., Murphy, R. C., Raetz, C. R. H., Russell, D. W., Seyama, Y., Shaw, W., Shimizu, T., Spener, F., van Meer, G., VanNieuwenhze, M. S., White, S. H., Witztum, J. L., and Dennis, E. A. (2005) A comprehensive classification system for lipids. J. Lipid Res. 46, 839− 862. (5) Gross, R. W. (2017) The evolution of lipidomics through space and time. Biochim. Biophys. Acta, Mol. Cell Biol. Lipids 1862, 731. (6) Haberkant, P., Raijmakers, R., Wildwater, M., Sachsenheimer, T., Brügger, B., Maeda, K., Houweling, M., Gavin, A.-C., Schultz, C., van Meer, G., Heck, A. J. R., and Holthuis, J. C. M. (2013) In vivo profiling and visualization of cellular protein−lipid interactions using bifunctional fatty acids. Angew. Chem., Int. Ed. 52, 4033−4038. (7) Haberkant, P., Stein, F., Hoglinger, D., Gerl, M. J., Brugger, B., Van Veldhoven, P. P., Krijgsveld, J., Gavin, A. C., and Schultz, C. (2016) Bifunctional sphingosine for cell-based analysis of proteinsphingolipid interactions. ACS Chem. Biol. 11, 222−230. (8) Hoglinger, D., Nadler, A., Haberkant, P., Kirkpatrick, J., Schifferer, M., Stein, F., Hauke, S., Porter, F. D., and Schultz, C. (2017) Trifunctional lipid probes for comprehensive studies of single lipid species in living cells. Proc. Natl. Acad. Sci. U. S. A. 114, 1566− 1571. (9) Hulce, J. J., Cognetta, A. B., Niphakis, M. J., Tully, S. E., and Cravatt, B. F. (2013) Proteome-wide mapping of cholesterolinteracting proteins in mammalian cells. Nat. Methods 10, 259−264. (10) Niphakis, M. J., Lum, K. M., Cognetta, A. B., Correia, B. E., Ichu, T.-A., Olucha, J., Brown, S. J., Kundu, S., Piscitelli, F., Rosen, H., and Cravatt, B. F. (2015) A global map of lipid-binding proteins and their ligandability in cells. Cell 161, 1668−1680. (11) Rowland, M. M., Bostic, H. E., Gong, D., Speers, A. E., Lucas, N., Cho, W., Cravatt, B. F., and Best, M. D. (2011) Phosphatidylinositol 3,4,5-trisphosphate activity probes for the labeling and proteomic characterization of protein binding partners. Biochemistry 50, 11143−11161. (12) Saliba, A.-E., Vonkova, I., and Gavin, A.-C. (2015) The systematic analysis of protein-lipid interactions comes of age. Nat. Rev. Mol. Cell Biol. 16, 753−761. (13) Wang, D., Du, S., Cazenave-Gassiot, A., Ge, J., Lee, J.-S., Wenk, M. R., and Yao, S. Q. (2017) Global mapping of protein−lipid interactions by using modified choline-containing phospholipids metabolically synthesized in live cells. Angew. Chem., Int. Ed. 56, 5829−5833. (14) Haberkant, P., and Holthuis, J. C. (2014) Fat & fabulous: bifunctional lipids in the spotlight. Biochim. Biophys. Acta, Mol. Cell Biol. Lipids 1841, 1022−1030. (15) Sanders, M. A., Madoux, F., Mladenovic, L., Zhang, H., Ye, X., Angrish, M., Mottillo, E. P., Caruso, J. A., Halvorsen, G., Roush, W. R., Chase, P., Hodder, P., and Granneman, J. G. (2015) Endogenous and synthetic ABHD5 ligands regulate ABHD5-perilipin interactions and lipolysis in fat and muscle. Cell Metab. 22, 851−860. (16) Granneman, J. G., Moore, H. P., Krishnamoorthy, R., and Rathod, M. (2009) Perilipin controls lipolysis by regulating the interactions of AB-hydrolase containing 5 (Abhd5) and adipose triglyceride lipase (Atgl). J. Biol. Chem. 284, 34538−34544. (17) Konda, V. R., Desai, A., Darland, G., Grayson, N., and Bland, J. S. (2014) KDT501, a derivative from hops, normalizes glucose metabolism and body weight in rodent models of diabetes. PLoS One 9, e87848. (18) Najm, F. J., Madhavan, M., Zaremba, A., Shick, E., Karl, R. T., Factor, D. C., Miller, T. E., Nevin, Z. S., Kantor, C., Sargent, A., Quick, K. L., Schlatzer, D. M., Tang, H., Papoian, R., Brimacombe, K. R., Shen, M., Boxer, M. B., Jadhav, A., Robinson, A. P., Podojil, J. R., Miller, S. D., Miller, R. H., and Tesar, P. J. (2015) Drug-based

spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE53 partner repository with the dataset identifier PXD007570. Activity Assays for PTGR2 and SGPL1. See the Supporting Information for details. High Content Imaging of OPCs Treated with Miconazole and/or SGPL1 Inhibitors. See the Supporting Information for details. Statistical Analysis. Statistical analysis was performed in GraphPad Prism. Metabolite data are shown as mean ± SEM (n = 3−5/ group). The statistical significance of differences between two groups was determined using Student’s t test (unpaired, two-tailed) unless otherwise specified. Where IC50 values were calculated for an assay, the data were fitted to a log (inhibitor) vs response (three parameter) model.

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ASSOCIATED CONTENT

* Supporting Information S

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acschembio.7b00581. Summary of protein SILAC ratio data from LiPIP experiments to determine the targets of the various bioactive compounds used in this study (XLSX) Summary of protein SILAC ratio data from LiPIP experiments to determine the UV-dependent targets of OEA-DA in HEK293T and Neuro2a cells (XLSX) Complete lists of peptide SILAC ratio data from all LiPIP experiments (XLSX) Supporting figures (Figures S1−S5), synthesis and characterization data, and extended methods (PDF)

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected]. *E-mail: [email protected]. ORCID

Benjamin F. Cravatt: 0000-0001-5330-3492 Author Contributions ∥

These authors contributed equally to this work. K.M.L., Y.S., L.L.L, and B.F.C. conceived of the project and designed experiments. K.M.L. and Y.S. performed ligand profiling MS experiments and followup studies, including protein overexpression and inhibition assays, and analyzed these data. B.A.B., W.C.P., and L.L.L. conducted the OPC differentiation experiments and analyzed these data. K.M.L., Y.S., L.L.L, and B.F.C. wrote the paper. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We are grateful to C. Parker as well as A. Galmozzi, B. Kok, and E. Saez (Saez Lab, TSRI) for helpful discussions and reagents. We also thank D. Beck and W. Roush for supplying SR-4559 and SR-4995. K.M.L. is supported by a National Science Scholarship (A*STAR, Singapore). This work was supported by the National Institutes of Health (CA132630, DK114785).

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Mapping Protein Targets of Bioactive Small Molecules Using Lipid

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