- Open Access
A novel small molecule inhibitor of p32 mitochondrial protein overexpressed in glioma
- Venkata Yenugonda1,
- Natsuko Nomura1,
- Valentina Kouznetsova2,
- Igor Tsigelny2,
- Valentina Fogal2,
- Elmar Nurmemmedov1,
- Santosh Kesari†1Email author and
- Ivan Babic†1Email authorView ORCID ID profile
© The Author(s) 2017
- Received: 20 July 2017
- Accepted: 6 October 2017
- Published: 18 October 2017
The mitochondrial protein p32 is a validated therapeutic target of cancer overexpressed in glioma. Therapeutic targeting of p32 with monoclonal antibody or p32-binding LyP-1 tumor-homing peptide can limit tumor growth. However, these agents do not specifically target mitochondrial-localized p32 and would not readily cross the blood–brain barrier to target p32-overexpressing gliomas. Identifying small molecule inhibitors of p32 overexpressed in cancer is a more rational therapeutic strategy. Thus, in this study we employed a pharmacophore modeling strategy to identify small molecules that could bind and inhibit mitochondrial p32.
A pharmacophore model of C1q and LyP-1 peptide association with p32 was used to screen a virtual compound library. A primary screening assay for inhibitors of p32 was developed to identify compounds that could rescue p32-dependent glutamine-addicted glioma cells from glutamine withdrawal. Inhibitors from this screen were analyzed for direct binding to p32 by fluorescence polarization assay and protein thermal shift. Affect of the p32 inhibitor on glioma cell proliferation was assessed by Alamar Blue assay, and affect on metabolism was examined by measuring lactate secretion.
Identification of a hit compound (M36) validates the pharmacophore model. M36 binds directly to p32 and inhibits LyP-1 tumor homing peptide association with p32 in vitro. M36 effectively inhibits the growth of p32 overexpressing glioma cells, and sensitizes the cells to glucose depletion.
This study demonstrates a novel screening strategy to identify potential inhibitors of mitochondrial p32 protein overexpressed in glioma. High throughput screening employing this strategy has potential to identify highly selective, potent, brain-penetrant small molecules amenable for further drug development.
- Mitochondrial p32
- Pharmacophore modeling
We previously reported that the gene encoding the protein p32 (C1qBP) is a direct transcriptional target of the cMyc oncogene . It is overexpressed in glioma and its expression correlates with malignancy grade . Higher levels of p32 expression have been reported for numerous types of cancers including breast, endometrial, ovarian, prostate, melanoma, and colon, suggesting a potential role in tumorigenesis [2–6]. P32 is predominantly localized to the mitochondria where it has a functional role in maintaining oxidative phosphorylation (OxPhos) [1, 7–10]. Loss of p32 switches metabolism from mitochondrial OxPhos to aerobic glycolysis. The functional role of p32 in the mitochondria is not completely understood. The reported crystal structure of p32 revealed it forms a homo trimer, and it was suggested that p32 might function as a regulator of Ca2+ ion concentration in the mitochondrial matrix, and mitochondrial Ca2+ levels are strongly coupled with OxPhos . Mitochondrial p32 regulates cancer cell metabolism and has a role in mediating Myc-induced glutamine addiction [1, 7, 8]. Loss of p32 in glutamine addicted glioma cells induced resistance to glutamine deprivation and sensitivity to glucose withdrawal . P32 appears to be critical for tumor progression. Genetic knockdown of p32 significantly reduced tumor formation in vivo [1, 8]. Thus p32 is a potential important therapeutic cancer target.
Although p32 is considered a mitochondrial matrix protein it is also localized to the cell surface and may have multiple functions [12–17]. In vivo targeting of cell surface expressed p32 with a monoclonal antibody inhibits tumor growth, thus demonstrating p32 provides a growth advantage to tumor cells [18, 19]. The exact mechanism of cell growth inhibition by anti-p32 antibodies is not completely understood, but may be due to the inhibition of cell surface p32, which prevents lamellipodia formation and cell migration . A tumor-homing peptide, LyP-1 (CGNKRTRGC), targets the cell surface localized p32 [12, 20–22]. LyP-1 has been used to deliver nanoparticles to breast tumors overexpressing p32, and has tumor growth inhibition effects in vivo . However, a limitation of both anti-p32 antibody and the LyP-1 peptide is that only the subset of p32 that is exposed on the cell surface can be targeted. P32 is predominantly localized to the mitochondria, and it would be advantageous for p32-directed therapeutics to inhibit the protein in all subcellular compartments. In addition, both antibody and peptide are too large to cross the blood–brain barrier, and may not be useful for targeting p32 overexpressed in brain cancers. Identifying a small molecule inhibitor of p32 represents a better therapeutic strategy. Since p32 is a validated cancer target, we expect a small molecule inhibitor of p32 to be an effective therapeutic for glioma and other types of cancers overexpressing p32—e.g., melanoma, breast, colon, and prostate cancers .
Here we describe and validate a pharmacophore modeling approach to identify potential small molecule inhibitors of p32. Molecular configurations of LyP-1 peptide binding to p32, and C1q binding p32, are shown to have similarities and can be superimposed. A consensus 3D pharmacophore model was developed based on common pharmacophore-centers features of these two complexes. This model was used to screen for compounds that would fit such pharmacophore. A primary screening assay employing a glutamine-addicted glioma cell line identifies a hit compound (M36). The potential p32 inhibitor binds directly to p32 and inhibits p32 association with LyP-1. We further demonstrate M36 can phenocopy p32 genetic knockdown, and can inhibit the growth of glioma cells and patient-derived glioma stem cells in culture.
Homology and pharmacophore modeling
Modeling protein loops of p32 protein
P32 crystal structure (PDB ID: 1p32) consists of the three similar chains. Each chain is encoded with 282 residues. NH2 terminus started at residue 74. Residues missing: In the chain A—141–161 (PTFDGEEEPSQGQKVEEQEPE), 191–196 (DEVGQE); in the chain B—139–163 (IPPTFDGEEEPSQGQKVEEQEPELT), 190–201 (PEDEVGQEDEAES); in the chain C—140–160 PPTFDGEEEPSQGQKVEEQEP), 190–201 (PEDEVGQEDEAES). The Loop Modeler module of MOE 2015 package (CCG, Montreal, Canada) was used to replace missing residues.
Docking of C1q with p32
Docking of C1q (PDB ID: 1pk6) with p32 (PDB ID: 1p32) was conducted using the Docking module of InsightII program (Accelrys, San Diego, Calif.). During docking, we used experimental data elucidating the binding region of C1q (Arg158, 162, 163; Ser156; and Val161 to p32 (Asp77, 79, 84, 88 and Glu89, 92, 93) [13, 24]. Then a binding fragment of C1q (residues 152–164: SIVSSSRGQVRRS) was separated.
Peptide modeling of LyP-1
Cyclic peptide LyP-1 (CGNKRTRGC) was constructed using Protein Builder module of InsightII package. Then sulfur bonds (CYS1–CYS9) were fixed and restrained molecular dynamics was performed taking in consideration the sulfur bond between Cys1 and Cys9.
Superposition of LyP-1 with binding fragment of C1q
Superposition of the cyclic peptide LyP-1 with binding fragment of C1q was performed using Align/Superpose module of Sequence Editor of MOE 2015.
Pharmacophore development and database screening
A six-feature pharmacophore was developed with the MOE 2015 Pharmacophore Query Editor. Pharmacophore centers were based on superimposed C1q–LyP-1 residues.
The Open NCI Database (http://cactvs.nci.nih.gov/download/nci/) release 4, containing 3D structures of over 260,000 compounds was virtually screened with Pharmacophore Search module of MOE 2015 for compounds that would fit the pharmacophore.
Similarity clustering of compounds
For similarity clustering, we used Fingerprint Clusters module of MOE. We used the GpiDAPH3 fingerprint and similarity-overlapping parameter SO = 40%. For more accurate clustering SO = 45% and SO = 53% were chosen.
Cell lines and reagents
SF188, U373, and UW426 cell lines were cultured in DMEM with 4.5 g/L glucose and l-Glutamine without sodium pyruvate (Corning Cellgro, cat# 10-017-CV) supplemented with 10% fetal bovine serum (FBS) (HyClone), 1 × penicillin streptomycin solution (Corning) at 37 °C/5% CO2. GBM8 is a patient derived glioma stem cell line and was obtained and cultured as neurospheres as previously described [25, 26]. Sphere cultures were passaged by dissociation, washing and resuspension in neural stem cell culture medium (NeuroCult™ NS-A Proliferation kit #05751, Stemcell Technologies), according to the manufacturer’s instructions. Institutional Review Boards of the UC San Diego Human Research Protections Program reviewed and approved this project (IRB # 100936), in accordance with the requirements of the Code of Federal Regulations on the Protection of Human Subjects. The IRB granted a waiver of informed consent for the recruitment component of this project.
For crystal violet staining cells were grown in 24 wells and incubated with indicated concentrations of inhibitor for 72 h. Cells were washed twice with PBS, incubated at room temperature with 0.2% crystal violet in 2% ethanol 10 min, washed gently with water several times then imaged.
Antibodies used for immunoblot analysis were: anti-C1QBP (D7H12) XP Rabbit mAb from Cell Signaling Technology (cat#6502), and anti-beta-Actin (13E5) Rabbit mAb from Cell Signaling Technology (cat#4970).
Cell viability assay
GBM8 cells were seeded at 2500 cells per well in 96-well plates. The next day increasing concentrations of inhibitor was added. After 4 days neurospheres were viewed and photographed under a Nikon microscope (4 × objective). Cell viability was determined by Alamar Blue assay after 8 days (Thermo Fisher Scientific).
For cell viability in high or low glucose, glioma cells were seeded in 96-well plates (2500 cells per well). The following day cells were washed twice with PBS, and glucose free DMEM supplemented (DMEM 1X, with l-Glutamine, without d-Glucose, without sodium pyruvate) (Gibco by Life Technologies, cat#11966-025) with 1% dialyzed FBS (Gibco by Life Technologies, cat#26400-036), penicillin–streptomycin (Corning), and d-(+)-Glucose solution (SIGMA), to either 2 or 25 mM final concentration, was added. Increasing inhibitor concentrations were added and incubated with cells for 72 h. Cell viability was determined by Alamar Blue assay (Thermo Fisher Scientific).
Protein thermal shift assay
Human recombinant p32 (OriGene) was tested in a reaction volume of 20 µL with 100 µM of p32 inhibitor. The thermal shift reaction was performed with a BioRad CFX96 real-time PCR machine, and analysis for binding induced shifts in thermal transition was performed with Precision Melt Analysis Software provided by the manufacturer (BioRad).
Glioma cells or neurospheres were plated in 96 well plates and treated with either DMSO control or p32 inhibitor at 50 µM for 4 days (SF188 cells) or 7 days (patient-derived neurospheres). Medium was collected and tested for lactate concentration using an l-Lactate assay kit (Eton Biosciences Inc.). Sytox Green nucleic acid stain (Thermo Fisher Scientific) was used to normalize for changes in cell number. Specifically, Sytox green was added to the cells at a final concentration of 5 µM and 100 µL of 0.4% Triton-X-100 solution was added (0.2% final concentration) and incubated for 30 min at room temperature in the dark. Fluorescence at 485ex/520em was measured using a fluorescence plate reader (POLARstar Omega). Relative lactate levels were obtained after normalizing by total cell number.
Primary screening assay in glutamine free media
SF188 glioma cells were seeded at 5 × 104 cells per well in a 12 well plate. The next day cells were washed twice with PBS and then glutamine free DMEM media added (DMEM 1 ×, with 4.5 g/L d-Glucose, without l-Glutamine and without sodium pyruvate) (Gibco by Life Technologies, cat#11960-044), and supplemented with 10% dialyzed FBS (Gibco by Life Technologies). After 3–4 days the media was removed and complete DMEM (with 2 mM glutamine and 10% FBS) was added to allow cells to recover for 24 h. Cell viability was determined by Alamar Blue assay (Thermo Fisher Scientific).
Fluorescence polarization (FP) assay
5FAM labeled LyP-1 peptide: [5FAM] G [C] GNKRTRG [C] [COOH] was synthesized by ThermoFisher Scientific. The peptide was made cyclic by disulfide bond between C2 and C10. Unlabeled linear LyP-1 peptide (H-CGNKRTRGC-OH) (synthesized by AnaSpec) was used for competition experiment. The labeled peptide was used at a final concentration of 0.5 nM. Fluorescence polarization assay was performed at final buffer concentrations of 10 mM HEPES (pH 7.5), 150 mM NaCl, 20 mM MgCl2, 5 mM EDTA, 2% Glycerol, 10% DMSO. Recombinant human p32 (OriGene) was diluted in PBS and incubated with the peptide in the dark for 2 h with shaking. Controls for FP: (1) Labeled peptide alone without any protein and (2) Labeled peptide + p32 but without any inhibitor (DMSO only control). Fluorescence Polarization was measured with POLARstar Omega plate reader Ex485 Em520 (number of flashes 50) (Gain adjustment was on the no protein wells set to 75 mFP).
Data are expressed as means ± standard deviations (SD) for all experiments. GraphPad Prism was used for statistical calculations and for graph generation.
We previously reported a pharmacophore modeling strategy for identifying compounds that could perturb the dimerization of the transcription factor Olig2 with its binding partner E47 . The 3D configuration of positively charged residues of C1q (Arg158, Arg161, and Arg162) is very close to such configuration of positively charged residues of LyP-1 (Arg5, Lys4, and Arg7) in the superimposed structures (Fig. 1c). The consensus 3D-pharmacophore hypothesis would have a significant possibility to define the pharmacophore features as shown in Fig. 1d, and we hypothesized that a small molecule able to fit such pharmacophore would bind to the p32 binding site as the C1q and LyP-1, thereby inhibiting its function. A six-feature pharmacophore was developed with the MOE2015 Pharmacophore Query Editor. Pharmacophore centers were based on superimposed C1q–LyP-1 residues: F1:Donor/Cationic Arg158–Arg7; F2:Donor/Cationic: Arg162–Lys4; F3:Donor/Cationic: Arg163–Arg5; F4:Acceptor/Anionic: Ser156–Thr6; F5:Hydrophobic: hydrophobic part of Arg162–Cys9; and F6:Hydrophobic: Val161–hydrophobic part of Arg5. This six-feature pharmacophore model was used as the benchmark for a virtual screen of 3D conformational databases derived from the NCI Open Database (release 4, accessed June 2012) of 265,242 compounds. The search with six-of-six-feature pharmacophore returned 30 compounds. Of these, five had logBB values in the range − 0.3 to − 1.5; as this is indicative of an ability to penetrate the blood–brain barrier, only these compounds were included in future analyses. The five-of-six-feature pharmacophore run returned 3423 compounds, from which 109 were selected using a set of criteria including RMSD to the pharmacophore model, energies of conformation, and a set of pharmacophore-based descriptors.
To reduce experimental work, we clustered 109 resulting compounds with MOE Compute/Fingerprint/Clusters module, using the GpiDAPH3 fingerprint and similarity—overlap parameter SO = 40%. The results yielded 10 clusters with at least two members [one cluster (1A) with 63 members, one (1B) with eight members, four with three members, and five with two members], and 16 clusters with one member. Cluster 1B was the best: six of eight compounds have a property to penetrate the blood–brain barrier (range of logBB = + 0.35 to + 0.65), and in the cluster 1C all four compounds (range logBB = − 0.31 to − 0.83). Since cluster 1A was too general, we performed a second clustering run using the GpiDAPH2 fingerprint and an SO of 45%. As a result, we obtained 15 clusters, 1 (2A) cluster with 33 members, 1 cluster (2B) with 11 members, 1 cluster (2C) with 5 members, 2 clusters with 2 members, and 10 clusters with 1 member. The compounds in cluster 2B belong to the same family and their logBB exceed − 1.6. The logBB range of compounds in cluster 2B is − 0.76 to − 1.41 and in cluster 2C is + 1.59 to − 1.41. The third clustering run included the cluster 2A and was provided with GpiDAPH3 fingerprint and an SO of 53%. This yielded one cluster (3A) with eleven compounds, one cluster (3B) with two compounds, and 10 clusters with one compound. A list of 19 compounds from clusters 1B, 2C, and 3A was selected.
Primary screening assay of potential p32 inhibitors
Direct binding of small molecule M36 to p32
Growth inhibition and altered metabolism induced by M36
The mitochondrial protein p32 is a validated cancer target overexpressed in glioblastoma and several other cancers . Genetic knockdown of p32 limits cell proliferation in vitro and tumor growth in vivo. Therapeutic targeting p32 with monoclonal antibodies or the p32 homing peptide LyP-1 can limit tumor growth in vivo [18–20, 22]. However, these strategies only target surface p32 and may not target mitochondrial localized p32. In addition, antibodies and peptides may not readily cross the blood–brain barrier and may not be effective against central nervous system cancers. Our study was specifically designed to identify a small molecule inhibitor of mitochondrial p32 function that could be developed as an anti-glioblastoma therapeutic. A pharmacophore model of C1q and of LyP-1 tumor homing peptide showed significant similarity and could be superimposed. The consensus 3D pharmacophore identified compounds able to engage all proposed pharmacophores features. This strategy identifies molecules expected to interfere with both C1q and LyP-1 association with p32.
We previously reported that mitochondrial p32 has a role in metabolism of Myc overexpressing cells and showed loss of p32 attenuates Myc-mediated glutamine addiction . Within the mitochondria it was proposed p32 functions to maintain mitochondrial integrity by regulating the expression of mitochondrial-DNA-encoded respiratory chain complexes [8, 10]. Mitochondria are essential for cancer cell survival acting as metabolic sensors that can trigger cell death through cytochrome c release . Recent reports have provided genetic and pharmacological evidence that mitochondrial metabolism is essential for tumor growth [8, 33, 34]. Mitochondrial inhibitors such as metformin, tigecycline, and VLX600 have demonstrated success in pre-clinical models of cancer [34–36]. Accumulating research thus demonstrates that targeting mitochondria metabolism of tumors can be an effective therapeutic strategy [35, 36]. In addition, recent studies have demonstrated glioma initiating stem-like cells (GSCs) rely heavily on mitochondrial OxPhos for bioenergetics and survival . GSCs can alter metabolism to support either a quiescent phenotype or rapidly proliferating phenotype depending on available nutrients and microenvironment. The design or identification of compounds that inhibit such metabolic plasticity offers a therapeutic window for sensitizing cancer cells to conventional therapy [36, 38, 39]. Directed targeting of GSC mitochondria in combination with conventional therapy (radiation and chemotherapy) may help eradicate GSCs and limit tumor recurrence. M36 represents a new class of compounds targeting an important mitochondrial regulatory protein. It is a potent inhibitor of patient-derived GSC line GBM8, and limits metabolic plasticity by targeting mitochondrial metabolism, forcing cells to use aerobic glycolysis for bioenergetics. This sensitizes the cells to depletion of glucose and other nutrients, and will likely also render them susceptible to glycolytic inhibitors. Future studies will examine whether M36 can synergize with chemotherapy or radiation to inhibit tumor growth, and whether the combination of a glycolysis inhibitor with p32 inhibitor can block tumor growth.
M36 effectively antagonized the interaction between p32 and LyP-1 peptide. In addition, direct binding of M36 to p32 was observed by thermal shift thus validating the pharmacophore model. LyP-1 can direct nanoparticles to p32-expressing tumors . Similarly, M36 has potential as a drug delivery agent based on its ability to bind p32, which becomes overexpressed on the surface of cancer cells  as has been reported for other p32-directed delivery compounds . Thus M36 has potential not just as an inhibitor of mitochondrial function in cancer cells overexpressing p32, but also as a nanoparticle homing agent directed to tumors that overexpress p32 at the cell surface. Future experiments will determine whether M36 has efficacy at limiting tumor growth in vivo and whether it binds to cell surface expressed p32.
Pharmacophore modeling of p32 association with the tumor homing peptide LyP-1 and C1q identifies potential p32 binding molecules. One of these molecules is shown to inhibit p32 function and inhibit proliferation of glioma lines overexpressing p32. In conclusion, the validation of this screening strategy offers potential to identify potent and selective inhibitors of mitochondrial p32 amenable for further drug development.
SK and IB designed experiments. IT and VK, performed pharmacophore modeling. NN, VF, JY, VY, EN and IB assisted with conducting experiments, and/or with data analysis and manuscript writing/review/editing. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Availability of data and materials
Materials used for the current study are available from the corresponding author on reasonable request.
Consent for publication
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We would like to thank Luis Serrano Family Foundation for partial funding for this project.
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