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What is immunopanning (vs. immunoprecipitation and FACS)?

What is immunopanning (vs. immunoprecipitation and FACS)?


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I had never heard the term before today. From what I can tell, it's using antibodies to purify a cell population of interest. I would appreciate more details, especially in how it differs from "immunoprecipitation" of cells via cell surface markers, and FACS.


Immunopanning is essentially an immunoprecipitation (IP) of cells using an antibody immobilized to a solid surface, like a cell culture plate. Conventionally, an IP is performed using small agarose or magnetic beads (~50 to 150μm in size) conjugated to an antibody or Protein A/G, and can pull down individual proteins, protein complexes, and/or nucleic acid complexes.

Cells are much larger than protein complexes, obviously, and for the most part much smaller than beads, but they can be damaged during a traditional IP, so immunopanning utilizes a single solid surface coated with the specific antibody or cell-surface protein ligand of choice. In this article (free PDF here), retrograde axonal transport was used to selectively label specific populations of neuronal cells with the extracellular portion of cholera toxin β (CTB) adsorbed to fluorescent beads, then the cells were purified using an immobilized anti-CTB antibody.

Fluorescence-Activated Cell Sorting or FACS is another way to purify cell populations. Cells are labeled with a fluorescent marker (either conjugated to an antibody specific to an extra-cellular epitope, via absorption as in the example above, or by expression of a fluorescent transgene like GFP), then scanned in a flow cytometer. Cells which match certain parameters or "gates" in the machine's software are then diverted into a separate collection chamber, allowing for the generation of a quite pure cell population.

However, as the authors point out in their paper above, FACS requires an expensive piece of equipment (can be over US$100,000), can be harsh on the cells, and is dependent on the availability of an antibody to a cell surface marker specific to the desired population of cells. Immunopanning is much less expensive, is more gentle on the cells, and in the authors' case was really the only choice for purifying their cell population of interest, as no specific biomarkers were available. It certainly won't replace FACS in the long run, but is another tool for investigators to use when studying specific cell types.


Flow Cytometry Protocol: Sample Preparation

Single-cell suspension is required for flow cytometry assays. Thus the adherent cell lines and tissue samples require processing into single-cell suspension before flow cytometry analyzed. A number of protocols are available and involved in mechanical dissociation or enzymatic digestion of the sample.

Note: When an enzymatic digestion is carried out, incubation has to be carefully monitored.

  • PBS: Dissolve 8 g NaCl, 0.2 g KCl, 1.15 g Na2HPO4 and 0.2 g KH2PO4 in 800 mL distilled water. Adjust the pH to 7.4 with HCl and final volume to 1 liter with additional distilled H2O.
  • Cell staining buffer: Add 0.5% BSA and 0.05% Sodium Azide (NaN3) to PBS, sterile-filtered.
  • RBC lysis buffer: 150 mM NH4Cl, 10 mM KHCO3 and 500 μM EDTA, sterile-filtered.
  • Ficoll - Paque or other density separation medium
  • Enzymes for tissue digestion
  • Cell culture medium (serum added)
  • EDTA
  • 15-mL centrifugal tube
  • Tissue culture dish
  • Scissors and scalpel blade
  • Springe
  • Centrifuge

Protocol A: Cultured Cells

1. Cell cultured in suspension just need to be harvested and centrifuged at 300-500 x g for 4-5 minutes and washed three times with cell staining buffer to remove residual growth factors that may be present in the culture medium. Then skip to Step 7.

2. For adherent cell lines, they may require pretreatment with EDTA or trypsin to facilitate removal from their substrates. Cells treated by trypsin should be further incubated in cell culture medium (serum added) on a rocker platform to neutralize the enzymatic activity without reattachment to the substrates.

3. Filtrate cell suspension through a 40 μm cell strainer to eliminate clumps and debris.

4. Centrifuge at 400-500 x g for 3-5 minutes and discard the supernatant.

5. Wash cells with cell staining buffer. Centrifuge at 300-400 x g for 5 minutes at 4°C and discard supernatant.

7. Resuspend the pellet in cell staining buffer.

8. Take 100 μL for cell counting and viability analysis.

9. Adjust the suspension to a concentration of 1 x 10 6 cells/mL in cell staining buffer.

Protocol B: Tissue Sample

1. Harvest tissue into a tissue culture dish containing 10 mL PBS.

2. Cells are dissociated by mechanical trituration and enzyme digestion.

  • Mechanical trituration: Tissue is cut into 2-4 mm pieces by scissors and scalpel blade and triturated with the plunger of a springe.
  • Enzyme digestion: After mechanical trituration, cells are detached using 0.25% trypsin diluted in PBS for 20 minutes at room temperature.

3. Neutralize the reaction with cell culture medium (serum added).

4. Filtrate cell suspension through a 40 μm cell strainer to eliminate clumps and debris.

5. Centrifuge at 300-500 x g for 4-5 minutes at 2-8 °C. Discard the supernatant.

6. (Optional) If necessary, resuspend cells in 3 mL 1 X RBC lysis buffer and incubate on ice for 5 minutes to lyse erythrocytes. Stop the reaction with 10 mL cell staining buffer. Centrifuge at 300-400 x g for 5 minutes at 4°C and discard the supernatant.

7. Wash cells with cell staining buffer. Centrifuge at 300-400 x g for 5 minutes at 4°C and discard supernatant.

9. Resuspend the pellet in cell staining buffer and perform a cell count and viability analysis.

10. Adjust the suspension to a concentration of 1 x 10 6 cells/mL in cell staining buffer.

Protocol C: Isolate PBMC from Whole Blood

1. Anticoagulated-blood is collected and diluted with PBS (1:1). Additionally, an equal volume of Ficoll underlay the diluted sample.

2. Centrifuge at 350 g for 10-20 minutes at room temperature.

3. Aspirate PMBC at the interface of the PBS and Ficoll layers.

4. Resuspend the cells in PBS.

5. Centrifuge at 300-500 g for 4-5 minutes at 2-8 °C. Discard the supernatant.

6. Resuspend cells in 3 mL1 X RBC lysis buffer and incubate on ice for 5 minutes to lyse erythrocytes. Stop cell lysis with 10 mL cell staining buffer. Centrifuge at 300-400 x g for 5 minutes at 4°C and discard the supernatant.

7. Wash cells with cell staining buffer. Centrifuge at 300-400 x g for 5 minutes at 4°C and discard supernatant.

9. Resuspend the pellet with staining buffer and perform a cell count and viability analysis.

10. Adjust the suspension to a concentration of 1 x 10 6 cells/mL in cell staining buffer.


Flow Cytometry Protocol: Intracellular Staining

Intracellular flow cytometry can be used to analyze a variety of intracellular molecules including cytokines, inflammatory mediators, transcription factors, and phosphoproteins. It can provide rich information concerning cellular function and signaling responses. Different from cell surface markers staining, intracellular antigens detection requires cell fixation and permeabilization before staining. Typically, cells are fixed with formaldehyde to preserve the cellular morphology, and then permeabilized with detergent or alcohol. Such fixation/permeabilization treatment allows the antibodies against intracellular antigens across the plasma membrane to stain intracellularly, while maintaining the morphological characteristics of cells.

  • For staining of secreted proteins, such as cytokines, a protein transport inhibitor such as Brefeldin A or Monensin, should be added prior to fixation/permeabilization in order to trap the cytokines inside the cells and enable intracellular staining.
  • When surface and intracellular staining is to be performed in the same sample. It is advised that surface staining should be carried out first to avoid any potential effects of the intracellular staining protocol.
  • PBS: Dissolve 8 g NaCl, 0.2 g KCl, 1.15 g Na2HPO4 and 0.2 g KH2PO4 in 800 mL distilled water. Adjust the pH to 7.4 with HCl and final volume to 1 liter with additional distilled H2O.
  • Cell staining buffer: Add 0.5% BSA and 0.05% Sodium Azide (NaN3) to PBS.
  • Fixation buffer: 1% paraformaldehyde in PBS.
  • Permeabilization buffer: 0.1% saponin in cell staining buffer.

Sample preparation

1. Harvest the tissues and cells, prepare a single-cell suspension and adjust the suspension to a concentration of 1 x 10 6 cells/mL in cell straining buffer.

Note: Cell surface staining may be done at this point.

2. Add 1 mL fixation solution per 1 x 10 6 cells and incubate for 15 minutes at room temperature.

3. Centrifuge at 400 x g for 5 minutes at room temperature and remove the fixation buffer.

4. Wash fixed cells with cell staining buffer. Centrifuge at 400 x g for 5 minutes and discard the supernatant.

Permeabilization

6. Resuspend fixed cells in 2 mL permeabilization buffer and centrifuge at 400 x g for 5 minutes at room temperature. Remove the supernatant.

7. Wash the fixed/permeabilized cells with permeabilization buffer. Centrifuge at 400 x g for 5 minutes and discard the supernatant.

9. Dilute the primary antibody with permeabilization buffer for an optimal working concentration and resuspend the fixed/permeabilized cells with primary antibody solution. Incubate for 15 to 20 minutes at 4°C.

Note: If using primary antibodies directly conjugated with fluorochrome, the incubation should be carried out in the dark, and then skip to Step 14.

10. Centrifuge at 400 x g for 5 minutes at 4°C and remove the supernatant.

11. Wash with permeabilization buffer and centrifuge at 350 x g for 5 minutes. Discard the supernatant.

13. Dilute the fluorescent-conjugated secondary antibody with permeabilization buffer for an optimal working concentration and resuspend the cell pellet with secondary antibody solution. Incubate on ice for 15-20 minutes in the dark.

14. Repeat Step 11 three times

15. Resuspend cells in 200-500 uL cell staining buffer for final flow cytometric analysis.


Fluorescence-activated cell sorting (FACS)


A fluorescence-activated cell sorter (FACS)

An antibody specific for a particular cell surface protein is associated to a fluorescent molecule and then added to a mixture of cells. For fluorescence when the specific cells pass through a laser beam they are monitored. Droplets containing single cells are given a positive or negative charge, based on whether the cell has limited the fluorescently-tagged antibody or not. Droplets containing a single cell are then detected by an electric field into collection tubes according to their charge.

Interests are first labeled with an antibody which is individual for a particular cell surface molecule. Antibody is coupled to a fluorescent dye, like when in a narrow stream the individual cells pass a laser beam in single file, the fluorescence of each cell is measured. A vibrating nozzle then forms small droplets which each containing a single cell which are given a negative or positive charge based on whether the cell they contain is fluorescing. A strong electric field defects the various charged droplets into separate containers so that each container has a homogeneous population of cells eventually with respect to the cell surface molecule tagged along fluorescent antibody. For biochemical analysis or grown in culture these homogeneous populations may then be used. By flow cytometry the RNA and DNA content of a cell can be measured also.

The cells need to remain viable and without contamination for subsequent culture. See the tips below.

• Include serum in buffers.
• Avoid sodium azide in the buffers during staining as this can be toxic to cells and compromise viability.
• The experiment should be undertaken in aseptic sterile conditions to ensure the cells do not become contaminated.
• It is not usually possible to do intracellular staining before sorting of live cells, as the permeabilization requires damage to the cell membrane which would compromise the cell viability.


Materials and Methods

VHH library and screening

One alpaca (Vicugna pacos) was immunized with purified autoPARylated hPARP1 protein according to the protocol described previously [21]. Alpacas belong to Livestock Center of the Faculty of Veterinary Medicine, Ludwig Maximilians University, Munich. Immunization was performed in strict accordance with the German Animal Welfare Law and has been approved by the government of Upper Bavaria (Permit number: 55.2-1-54-2531.6-9-06). 70 days after the first immunization,

100 ml blood was collected from the animal and lymphocytes were isolated by Ficoll gradient centrifugation using the Lymphocyte Separation Medium (PAA Laboratories GmbH). 1x 10 7 B-cells were used to prepare total RNA using the Nucleospin RNA Kit (Macherey-Nagel). Complementary DNA (cDNA) was amplified using the First-Strand cDNA Synthesis Kit (GE Healthcare) according to the manufacturer´s protocol. The VHH repertoire was amplified from the cDNA by 3 subsequent nested PCR reactions using 6 different VHH-specific primers [17]. The VHH library was subcloned into the SfiI/NotI sites of the pHEN4 phagemid vector and transformed into E. coli TG1 cells [22]. E. coli cells were further infected with M13K07 helper phages to produce phages carrying VHHs on their tips. The phage display/immunopanning procedures and ELISA were performed as detailed in [23]. For further studies, a VHH with the highest solubility and affinity to PARP1 was selected.

Expression plasmids

For bacterial expression of the VHH domain (nanobody), the sequence were cloned into the pHEN6 vector [22], thereby adding a C-terminal 6xHis-tag for IMAC purification. Bacterial expression vector of PARP1 VHH will be provided upon request to the authors by ChromoTek via MTA (material transfer agreement). For protein production, E. coli JM109 cells (NEB) were used. Expression and purification of the nanobody was carried out as described previously [24]. For mammalian expression of PARP1 chromobody, N-terminal fusions of the PARP1 nanobody to the fluorescent proteins TagGFP2 or TagRFP (Evrogen) were constructed using BglII/HindIII restriction sites in the target backbone vector. PARP1 Chromobody vector will be provided upon request to the authors by ChromoTek via MTA. All resulting constructs were sequenced and tested for expression in HEK293T cells followed by immunoblot analysis. Mammalian expression plasmids of GFP-hPARP1, GFP-hPARP2, GFP-hPARP3 and GFP-hPARP9 were kindly provided by Prof. Heinrich Leonhardt, LMU Munich. The plasmids coding for the GFP- or mCherry-tagged PARP1 domains (DBD-GFP, ZnF1-GFP, ZnF2-GFP, mCherry-ZnF3, WGR-PARP domain-mCherry) were kindly provided by Gyula Timinszky, LMU Munich. SF9 insect cells expressing Strep-tagged human PARP1 domains (DNA-binding domain, automodification domain, catalytic domain) where kindly provided by Annette Becker, TU Darmstadt. Point mutations were introduced into the human ZnF2 sequence with the Q5 ® site-directed mutagenesis kit (NEB) according to the manufacturer’s instructions.

Antibodies and chemical compounds

The following primary antibodies were used: rat anti-GFP clone 3H9 (ChromoTek), mouse anti-RFP clone 3F5 (ChromoTek), rabbit anti-TagRFP (Evrogen, AB233), mouse anti-PARP1 clone CII-10 (BD-Biosciences), mouse anti-pADPr clone 10H (Santa Cruz) and rabbit anti-GAPDH antibody (Santa Cruz, sc 25778). The following secondary antibodies were used for detecting the primaries: anti-rat/mouse/rabbit-Alexa Fluor 647/568/488 (Cell Signaling). The following small molecule compounds were administered: camptothecin (Tocris), actinomycin D (Sigma), 4-NQO (Sigma) and H2O2 (Sigma). The following affinity resins were used for immunoprecipitation: GFP-Trap ® , RFP-Trap ® and PARP1 nanotrap (ChromoTek).

PARP1-affinity resin generation and immunoprecipitation

Purified VHH was covalently coupled to Sepharose beads (GE Healthcare) via NHS according to the manufacturer´s protocol, creating so-called PARP1 nanotrap. For immunoprecipitation, 1 x 10 6 –1 x 10 7 HEK293T, HeLa, MEF or BHK cells expressing the target protein were washed and harvested in phosphate buffered saline (PBS). Cell pellets were homogenized in 200 μl RIPA buffer (10 mM Tris/Cl pH7.5, 150 mM NaCl, 0.5 mM EDTA, 0.1% SDS, 1% Triton X-100, 1% Deoxycholate), supplemented with 1 μg/μl DNaseI, 2 mM MgCl2, 2 mM PMSF, 1x mammalian protease inhibitor mix M (Serva) by repeated pipetting for 30 min on ice. After a centrifugation step (10 min at 17.000 x g), the soluble fraction was adjusted to 500 μl with a dilution buffer (10 mM Tris/Cl pH 7.5, 150 mM NaCl, 2 mM PMSF, 1x mammalian protease inhibitor mix M (Serva)) and incubated with 25 μl of the PARP1 nanotrap for 1 h in an end-over-end rotor at 4°C. As a negative control, a non-related nanobody coupled to 4% cross-linked agarose (GFP-Trap or RFP-Trap) were used. The bead pellet was washed two times in 500 μl dilution buffer. After the last washing step, the beads were transferred to a new cup, resuspended in 2x SDS-sample buffer (120 mM Tris/Cl pH 6.8 20% glycerol 4% SDS, 0.04% bromophenol blue 10% β-mercaptoethanol) and boiled for 10 min at 95°C. Samples (1–2% input, 1–2% flow-through, 25–50% bound) were analyzed by SDS-PAGE followed by western blotting.

SDS-PAGE and immunoblotting

Denaturing polyacrylamid gel electrophoresis (SDS-PAGE) was performed according to standard procedures. Proteins were transferred from SDS gels to nitrocellulose membranes (Bio-Rad) by semi-dry blotting and subsequently probed with different antibodies. Blots were scanned on the Typhoon-Trio laser scanner (GE Healthcare) and quantitatively analyzed with ImageJ software (http://imagej.nih.gov/ij/).

In vitro synthesis of pADPr polymers

pADPr polymer synthesis was performed according to the protocol from [25], with modifications detailed in [23, 26]. Here, pADPr polymers were synthesized using endogenous hPARP1 immobilized on PARP1 nanotrap and compared with the pADPr polymer synthesis catalyzed by “free” recombinant hPARP1 purified from E. coli.

Surface plasmon resonance

Affinity measurements with Biacore T200 (GE-Healthcare) were kindly conducted by PD Dr. Ralf Heermann at the LMU Munich. SPR sensorgrams were subsequently recorded using the Biacore T200 Control software 1.0 and the resulting data was analyzed with the Biacore T200 Evaluation software 1.0. The PARP1 nanobody was captured on a carboxymethyldextran chip (Xantec) via its C-terminal His6-tag by immobilizing an anti-His antibody (His Capture Kit 28-9950-56, GE Healthcare) through standard covalent amino-coupling to the chip surface. Recombinantly purified hPARP1 was passed over the chip in seven different concentrations from 10 nM to 1000 nM, with the lowest concentration injected twice as internal control. The hPARP1 injection time was 3 min, followed by a dissociation time of 10 min. The surface was regenerated with 10 mM glycine pH 1.5 for 30 sec followed by a stabilization period of 10 sec. The surface of the flow cell 1 was used to generate blank sensorgrams for substraction of bulk refractive index background. The reference sensorgrams were normalized to a base line of 0. Peaks in the sensorgrams at the beginning and the end of the injection emerged from the run time difference between the flow cells of the chip.

Cells culture and transfections

HEK293T, HeLa, HT1080, MCF7, U2OS, PC3 and BHK cells were cultivated according to standard protocols. Briefly, growth media consisted of DMEM (high glucose, pyruvate, L-Glutamine) supplemented with 10% fetal calf serum (FCS) and antibiotics. Cells were trypsinized for passaging and cultivated at 37°C in a humidified chamber with a 5% CO2 atmosphere. Plasmid-DNA was transfected with Lipofectamine ® 2000 (Life Technologies) according to the manufacturer´s protocol. HeLa cells stably expressing the PARP1 chromobody were generated by transfection of the PARP1 chromobody vector (PARP1 VHH fused to TagRFP) and selection of resistant clones with G418 (1 μg/μl) followed by single-clone cell sorting by FACS. For live-cell imaging, the cells were cultivated in DMEM without phenol red and supplemented with 5% FCS and 10 mM sterile HEPES (Sigma).

Resazurin assay

To test an overall impact of the chromobody on cell viability and metabolic status, untransfected and transfected HeLa cells 16 h post-transfection with the PARP1 chromobody plasmid were incubated for 24 h in cell culture medium containing resazurin (alamarBlue ® , AbD Serotec). The assay was performed and evaluated according to the manufacturer’s protocol. Absorbance was measured at 570 nm and 600 nm with a spectrophotometer (Multiskan ™ Go, Thermo Scientific).

Cell fixation and immunocytochemistry

For end-point analysis, cells were fixed with 3.7% formaldehyde in PBS for 10 min at RT. For detection of pADPr polymers with immunofluorescence, HeLa cells expressing/not expressing PARP1 chromobody grown on glass coverslips, were treated with 10 mM H2O2 (Sigma) for 10 minutes, fixed with ice-cold methanol/acetone (1:1) and incubated with anti-pADPr antibody (clone 10H, Santa Cruz) followed by secondary antibody. Subsequently, nuclei were stained with DAPI (Invitrogen).

Fluorescent Two-Hybrid assay (F2H ® )

Cell-based F2H ® protein-protein interaction assay was carried out with F2H Kit Basic (ChromoTek) according to manufacturer’s instructions.

Microscopy and image analysis

Epifluorescence imaging was performed using a Leica wide-field fluorescence microscope equipped with a 20x objective (Leica). F2H and HCA images were acquired with the InCell Analyzer 1000 (GE Healthcare) from 30 positions per well in an automated fashion. For evaluation of nucleoli, automated image analysis was carried out with an IN Cell Analyzer 1000 Workstation 3.5 (GE Healthcare). “Multi-target analysis” segmentation was performed to segment nuclei (based on their fluorescent intensity, size and shape in the DAPI channel), cells (“collar” in RFP channel) and organelles (nucleoli in the nuclei in RFP channel, based on the size). This allowed identification of morphologically appropriate nuclei, defining cytoplasmic area around the nuclei and nucleoli in the nuclei. Cell-by-cell analysis was performed for at least 100 cells stably expressing PARP1 chromobody per well. Percentage (%) of cells with nucleoli was calculated by normalizing the number of cells with more than one nucleolus to the total number of cells with PARP1 chromobody signal.

Laser microirradiation

Laser microirradiation live-cell experiments were carried out on an UltraView Vox spinning disc microscope with integrated FRAP PhotoKinesis accessory (Perkin Elmer) assembled to an Axio Observer D1 inverted stand (Zeiss) and using a 63x/1.4 NA Plan-Apochromat oil immersion objective. The microscope was equipped with a heated environmental chamber set to 37°C. Fluorophores were excited with 561 nm solid-state diode laser lines. Confocal image series were recorded with 14-bit image depth, a frame size of 256 × 256 pixels and a pixel size of 110 nm. Microirradiation was carried out with a 405 nm diode laser set to 100% emission. Preselected spots of

1 μm in diameter within the nucleus were irradiated for 1 s. Before and after microirradiation, confocal image series of one mid z-section were recorded at 1 s time interval (5 or 9 pre-irradiation and 100 or 130 post-irradiation frames). For evaluation of the recruitment kinetics, fluorescence intensities of the irradiated region were corrected for background and normalized to the pre-irradiation values. Data from 10–14 microirradiated cells of each cell type were averaged and plotted.

Carbon ion beam microirradiation

Carbon ion microirradiation was performed at the Munich ion microbeam SNAKE facility (Supraleitendes Nanoskop für Angewandte Kernphysikalische Experimente, Maier-Leibnitz-Laboratory, Garching, Germany) [27–29]. For irradiation and live-cell imaging, HeLa cells were transfected with the PARP1 chromobody and re-seeded into live-cell imaging cell containers, where cells grow on a BC418 plastic scintillator [28, 30]. Cells were cultivated in phenol red free medium supplemented with 2.5 mM HEPES and 0.25 mM Trolox. Carbon ions of 55 MeV total energy with a LET in water of 310 keV/μm were used in this work (count rate 1.5 Hz). Individual cell nuclei were irradiated with defined numbers of ions (30 or 300) per dot in five-dot irradiation patterns as described [27]. Distance between dots was 3 μm. About ten nuclei were targeted in a single irradiation, which takes about 1 s for 30 ions per dot or about 10 s for 300 ions per dot. Image acquisition was performed with an inverse epifluorescence microscope (Zeiss Axiovert 200M Z1) using a Zeiss Plan Apochromat 40x/0.95 objective (Korr Ph3 M27) and the software AxioVision 4.6 and an AxioCam Mr3 camera. Cell chambers were kept at 37°C during image acquisition.


Discussion

The identification of cell surface markers has provided critical information for characterizing SSCs regarding the types of cytokines and/or adhesion molecules that are potentially involved in SSC biology. For example, we previously identified ITGB1 on SSCs [35], and later determined that SSCs without Itgb1 fail to colonize seminiferous tubules after spermatogonial transplantation [47], which suggested that ITGB1 is an important molecule involved in SSC homing. Currently, there are 10 surface molecules that are expressed on mouse SSCs [11]. Some molecules, such as GFRA1 and melanoma cell adhesion molecule (MCAM), impact self-renewal [48, 49]. Although surface molecules are not always conserved among species, the transplantation of human SSCs results in colonization and division in mouse seminiferous tubules [50], which suggests that critical cell adhesion molecules may be conserved between mouse and human SSCs. Indeed, several cell surface molecules, such as ITGA6, CD9 antigen (CD9), epithelial cell adhesion molecule (EPCAM), and thymus cell antigen 1 (THY1), theta, are expressed on both mouse and human SSCs [21, 35, 51�]. Thus, the identification of SSC surface markers and analyses of their functions have contributed to the current understanding of SSCs.

The present study identified EPHA2 on SSCs and confirmed that it was expressed in CDH1-selected undifferentiated spermatogonia from mature adult testes and GS cells. These findings are consistent with initial reports showing that EPHA2 is enriched in KIT − spermatogonia [25]. Because EPHA2 was expressed in a portion of undifferentiated spermatogonia in the present study, it is possible that EPHA2 is involved in SSC biology. Additionally, analyses of GS cells revealed that EPHA2 expression was regulated by self-renewal factors (FGF2 and GDNF). However, EPHA2 is not the first molecule to be up-regulated by self-renewal factors because our group previously reported that self-renewal factors up-regulate MCAM, which is another SSC marker [49]. Therefore, some SSC markers appear to be influenced by environmental changes. Although the present results obtained via in vitro analysis with GS cells may not be directly extrapolated to an in vivo model, it is possible that GFRA1 + cells expressing MCAM or EPHA2 in the seminiferous tubules may be located more closely to niches. Because they are probably exposed to relatively high concentrations of self-renewal factors, these spermatogonia may undergo more active self-renewal division and show increased expression of MCAM or EPHA2.

To confirm the EPHA2 expression on SSCs, we fractionated testis cells by MACS and FACS and used the cells for spermatogonial transplantation experiments. Although we observed enrichment of SSCs by anti-EPHA2 antibody selection using MACS, real-time PCR analysis of FACS-collected cells showed that Bmi1, Id4, and Fbxw7 were more strongly expressed in EPHA2 negative population, which was contrary to our expectation. However, these molecules do not appear to be expressed specifically in Asingle spermatogonia in mature adult testes [30�]. Moreover, Bmi1 and Id4 expression was analyzed using transgenic mice, which may not necessarily reflect their protein levels [31, 34, 56]. Although these results of real-time PCR suggested a low SSC activity in EPHA2 high cells, an approximately 257.2-fold enrichment of SSCs was achieved by transplantation of this population. This finding indicates that EPHA2 might be a more useful surface molecule for collecting SSCs than GFRA1. The degree of enrichment appears modest compared to the results of previous studies however, it should be noted that only EPHA2 was used for FACS and wild-type adult testes were the source of the donor cells. In many previous experiments, SSCs were collected from cryptorchid testes, which are enriched for SSCs due to lack of differentiated germ cells, or several antibodies were used for selection [49, 57]. It is possible that combined treatment with other cell surface markers, such as MCAM, will greatly improve the purity of SSCs in multiparameter cell sorting.

One of the interesting results in the present study was the induction and phosphorylation of EPHA2 via self-renewal factor stimulation. It has been reported that EPHA2 is induced by Ras/mitogen-activated protein kinase (RAS-MAPK) pathway activity [58]. Because GDNF and FGF2 both activate RAS-MAPK pathway and the transfection of constitutive Hras induces the proliferation of GS cells without cytokines [59], it is likely that EPHA2 is induced via this pathway. However, in the present study, EPHA2 was both induced and phosphorylated by self-renewal factor stimulation, which was unexpected because known EPHA2 ligands were not added to the culture. It was initially thought that RET might bind EPHA2 and influence its phosphorylation because EPHA family members interact with other tyrosine kinases [17, 18], such as FGFR2, N-methyl-d-aspartate receptor, and C-X-C motif chemokine receptor type 4 [40�]. However, immunoprecipitation analyses did not reveal any association of EPHA2 with RET or FGFR2. Therefore, kinases that were activated downstream of the GDNF signal likely induced the phosphorylation of EPHA2. Additionally, EPHA2 may be involved in GDNF signal amplification because Epha2 KD reduced RET phosphorylation levels.

However, there was no apparent effect of EFNA1 in the GS cell cultures. It was assumed that EPHA2 activation might enhance GS cell proliferation because Epha2 KD reduced RET phosphorylation therefore, several attempts were made to induce EPHA2 phosphorylation using different methods, including chemical agonists, Epha2 CA transfection, and EFNA1 protein supplementation. However, we have not been able to find apparent improvement in GS cell proliferation (our unpublished observations). Because EPHA2 was already phosphorylated by self-renewal factor stimulation, it is possible that additional treatment may not have had a significant impact on the activation of EPHA2.

Another important observation from the present study was the association between FGFR2 and RET. RET associates with many molecules, including DOK1/5, GFRA1, GRB10 GRB2/7, SHC1, and STAT3 [60]. Both FGF2 and GDNF are established self-renewal factors for SSCs, and each can stimulate self-renewal division even in the absence of the other factor [61]. Interestingly, the proliferation of GS cells is dramatically improved when both cytokines are simultaneously present in the culture medium. Although the exact mechanisms underlying the synergy between these two factors remain unknown, our research group has previously reported that Bcl6b, which is a critical transcription factor for SSC self-renewal, is up-regulated by GDNF as well as FGF2 via Map2k1 activation [44]. Therefore, it is possible that GDNF and FGF2 might share the same target genes, and the intracellular interactions of their downstream signaling pathways might underlie the synergistic action of these cytokines. However, given the present results, it can be speculated that the direct association of FGFR2 and RET per se could explain the activation of common target genes. The manner in which the interaction of these receptors is initiated and the specific molecules that are involved in this process will be the next important focus of investigation and will help clarify the molecular mechanisms underlying self-renewal division.

Although the poor recovery of GS cells after Epha2 depletion could be explained by defective cell signaling, EPHA2 was also involved in adhesion. Eph receptors play important roles in the regulation of migration and adhesion. For example, neural crest cell migration depends on the repulsive interactions of Eph molecules and their ephrin ligands during path finding [62]. Because SSCs migrate to the germline niche after spermatogonial transplantation and associate strongly with laminin in vitro [47], it is possible that EPHA2 may also influence the laminin adhesion of GS cells. The decreased adhesion to laminin observed in the present study contradicts the findings of a previous report, which showed the ability of EPHA2 to induce the inactive conformation of integrins to inhibit cell spreading, migration, and integrin-mediated adhesion [63]. Although it remains unknown whether this influence on adhesive properties is affected by self-renewal factor stimulation, SSCs have a relatively strong affinity to basal membrane components [35]. The present results suggest that the involvement of EPHA2 in cell adhesion also contributed to the poor cell recovery after Epha2 KD.

In the present study, the reduced colonization of SSCs after Epha2 KD in fresh testes cells indicated that Epha2 was important for SSC activity. Given the present results with GS cells, it is possible that the poor colonization of freshly isolated SSCs was due to reduced survival and weaker binding affinity to the basement membrane. However, there is no reported phenotype in spermatogenesis in Epha2 KO mice, even though these animals exhibit abnormal angiogenesis and an increased frequency of developing chemically induced tumors [64, 65]. The lack of a reproductive phenotype may not be surprising given the expressions of other Eph family molecules. Indeed, it has been reported that Epha2 can be compensated for by Epha4 during neural tube development [66]. Thus, a lack of EPHA2 during development may have been compensated for by redundant signaling in Epha2 KO mice because there was relatively strong EPHA1 expression in GS cells in the present study. Given the impact of EPHA2 on RET phosphorylation, the mutants may also have experienced other abnormalities, such as during the initial formation of the SSC pool or the regeneration of spermatogenesis. Future studies using a combined KO/transgenic approach may be required to demonstrate the function of Epha2 in SSCs in vivo.

The identification of EPHA2 on SSCs will be useful for understanding SSC biology and improving SSC purification levels by combining them with other cell surface markers. The induction of EPHA2 expression via self-renewal factor stimulation suggests that this molecule plays a unique role in SSC self-renewal. Because it is a tyrosine kinase receptor and influences RET phosphorylation, a functional analysis of Epha2 and its family molecules will lead to a better understanding of the self-renewal machinery associated with SSCs. It is possible that the manipulation of EPHA2 may further enhance self-renewal signaling, which may be useful for improving the genetic manipulation of GS cells. Although there have been limited success in maintaining or expanding human SSCs in vitro, EPHA2 ligands would be good candidates for improving such cultures to expand SSCs in vitro. In vitro expansion of SSCs will be useful for genetic modification of the male germline to create disease models in animals. It is also expected to use these cells for human male infertility treatments [67]. Thus, EPHA2 will aid in SSC purification, further the current understanding self-renewal machinery, and have potential applications in SSC cultures.


3. Discussion

Blockades targeted on PD𠄁/PD‐L1 have been approved for treating human cancers with considerable clinical effects. However, the overall response rate to PD𠄁/PD‐L1 blockades is relatively low, and the underlying mechanism is still unclear. Recent studies revealed that tumor PD‐L1 level is related to the efficacy of PD𠄁/PD‐L1 blockades. Therefore, it is important to understand the molecular mechanism underlying the regulation of tumor PD‐L1. Sunitinib has been reported to have antitumor effect, but its role in cancer immunity is not well understood. Here we reported for the first time that Sunitinib improved OS in immune competent melanoma mouse model in vivo by induction of tumor CTL activity via alleviating tumor PD‐L1 expression level. Mechanistically, utilizing both in vitro and in vivo studies, we demonstrated that Sunitinib post‐translationally regulated PD‐L1 stability via p62�pendent selective autophagy and confirmed this regulation in RCC patient samples. Preclinically, we showed that Sunitinib had synergistic effect with CTLA𠄄 mAb in the treatment of melanoma and NSCLC immune competent mice. Clinically, we observed lower PD‐L1 levels and higher p62 levels in tumor region of nonresponders as compared to responders in anti‐PD𠄁‐treated NSCLC lung cancer patients. Taken together, our studies revealed a novel mechanism regarding the regulation of PD‐L1, identified a potential prognostic marker for anti‐PD𠄁 treatment efficacy, and provided a new combinatorial therapeutic strategy for the treatment of melanoma.

Sunitinib is FDA𠄊pproved tyrosine kinase inhibitor, which was utilized to treat cancer patients in clinic. [ 32 ] However, the role of Sunitinib in tumor immunity is still unclear. In this study, we found that Sunitinib suppressed protein level of tumor PD‐L1, therefore subsequently promoted CTL activity. To further test the clinical relevance of Sunitinib‐mediated inhibition of tumor PD‐L1 and subsequent immune surveillance, we utilized combination therapeutic strategy by cotreating Sunitinib and CTLA𠄄 mAb in both melanoma and NSCLC immune competent mice. Intriguingly, we observed that Sunitinib had synergistic effect with CTLA𠄄 mAb which significantly inhibited tumor growth and prolonged the OS rate by promoting immune surveillance. Sunitinib is a multitargeted RTK inhibitor targeting vascular endothelial growth factor receptor (VEGFR), platelet�rived growth factor receptor (PDGFR), C‐KIT (CD117), REarranged during Transfection (RET), CSF1R, and (FMS‐like tyrosine kinase 3, (FLT𠄃). [ 33 ] These targets are highly conserved between human and mouse genome. [ 34 ] Furthermore, recent study showed that Sunitinib treatment alone or in combination with anti‐VEGFR could enhance CD8+ T�ll numbers in mouse models or in metastatic renal cell carcinoma (mRCC) patients, [ 35 , 36 ] suggesting the similar specificities in mice compared with human, and these correspondence between humans and mice reinforce the validity of mouse models for human diseases. Therefore, these results suggest that combination treatment of Sunitinib and CTLA𠄄 mAb can be potential as a novel therapeutic strategy for the treatment of human melanoma and NSCLC.

Tumor PD‐L1 was reported to be regulated both at transcriptional and post‐transcriptional levels. A previous study reported that Sunitinib was characterized as an autophagy inducer, which was involved in specific substrate degradation. [ 37 ] Here, we revealed that Sunitinib controlled PD‐L1 degradation by regulation of p62�pended selective autophagy. By utilizing co‐immunoprecipitation and immunostaining, we demonstrated that PD‐L1 was interacted with p62 and colocalized in the lysosome. In summary, our data revealed that Sunitinib treatment regulated tumor PD‐L1 stability by induction of p62‐mediated selective autophagy. Mechanistically, this Sunitinib‐mediated downregulation of tumor PD‐L1 subsequently activates CTL activity, which promoted tumor surveillance. Preclinically, we demonstrated that combination treatment of Sunitinib and CTLA4 mAb significantly alleviated tumor burden and OS in melanoma and NSCLC immune competent mouse models.


Representative Results

We recently developed CEMs and demonstrated that this technology can be applied to regulate gene expression and the chromatin environment at a reporter locus in a dose-dependent and reversible manner. In Figure 1 , a model of the lead CEM, CEM23, is shown. HDAC machinery is recruited to the reporter locus by the HDAC inhibitor which, in this case, is the GFP reporter inserted at the Oct4 locus.

We sought to characterize the CEM system at the Oct4 locus in CiA mESCs. Because the cells express GFP, it was possible to quickly visualize the expression of the reporter with fluorescence microscopy. The cells were imaged after 48 hours of treatment with 100 nM of CEM23, along with untreated cells. The phase images show healthy mESCs, which is important because unhealthy or differentiated mESCs would indicate a non-specific GFP repression. The fluorescence images show a bright GFP expression in the control cells and a reduced GFP expression in mESCs treated with CEM23. Representative images are displayed in Figure 2 .

To quantify the changes in the GFP expression, flow cytometry was used. Again, the CiA mESCs were treated with 100 nM of CEM23 for 48 hours. Experimental cells treated with CEM23 and control cells were prepared for flow cytometry, using > 100,000 cells per sample. Consistently, we observed a > 30% decrease in GFP-expressing cells among those treated with CEMs. A representative histogram is shown in Figure 3 .

Once evidence of CEM-induced GFP gene expression decrease was shown, we tested for changes in the chromatin environment using ChIP. One important factor for preparing samples for ChIP is the extent of chromatin sonication. To have the samples as consistent and properly sheared as possible, 10 million cells were sonicated for 3.5 minutes to obtain chromatin between 200 and 500 bp in size ( Figure 4 ). Because we hypothesized that the repressive CEMs were binding to and recruiting HDAC proteins and repressive complexes to the reporter, we tested for changes in histone tail acetylation. Histone 3 Lysine 27 acetylation (H3K27ac) is commonly found along the transcriptional start site (TSS) of genes. We performed ChIP with an antibody for H3K27ac and then performed a qPCR with primer sets upstream and downstream of the TSS. The results show that a 48-hour treatment with 100 nM of CEM23 decreased the level of H3K27ac at the target locus when compared to control cells not treated with CEMs (p < 0.01, two-tailed Student's t-test with three biological replicates, Figure 5 ).

Figure 1 : CEMs bind to the CiA:Oct4 locus through recruitment to the FKBP and tether endogenous epigenetic machinery. The model of the CEM system shows Gal4-FKBP as the protein-to-DNA anchor. The FK506 portion of the CEM binds to FKBP and the HDAC inhibitor recruits endogenous HDAC proteins to repress GFP. Please click here to view a larger version of this figure.

Figure 2 : Fluorescence images of mouse embryonic stem cells (mESCs) with a CiA:Oct4-GFP reporter. Fluorescence microscopy images show a decrease in GFP expression upon CEM treatment. The top panel shows phase and fluorescent images of mESCs grown with untreated media. The bottom panel shows phase and fluorescent of the mESCs treated with 100 nM of CEM23 for 48 hours. The images adapted with permission from ACS Synthetic Biology12. Copyright (2018) American Chemical Society. Please click here to view a larger version of this figure.

Figure 3 : Flow cytometry analysis quantitates a decreased expression of GFP in mESCs upon CEM23 treatment. mESCs without CEM23 (blue) were compared with mESCs treated with 100 nM of CEM23 for 48 hours (red). Please click here to view a larger version of this figure.

Figure 4 : Sonication of chromatin is uniform, and a smear is visible between 200 and 500 bp. To perform ChIP-qPCR, the chromatin from mESCs was sonicated. Each sample consisted of approximately 10 million cells, which were sonicated for 3.5 minutes, to produce similarly-sheared chromatin samples between 200 and 500 bp in length. Please click here to view a larger version of this figure.

Figure 5 : CEM23 treatment causes a decrease in H3K27ac at the CiA locus. ChIP-qPCR was performed to test for changes in the chromatin environment. After a 48-hour treatment with 100 nM of CEM23, a decrease in H3K27ac was observed at CiA:Oct4 (**p < 0.01, two-tailed Student's t-test with three biological replicates. The error bars represent the standard deviation). Please click here to view a larger version of this figure.


Results

PARP inhibition offsets LMP1-mediated gene activation

To identify global targets of LMP1 regulated by PARP1, LMP1 was ectopically expressed in the EBV-negative Burkitt’s lymphoma cell line DG75 (S1A Fig). Cells were transduced with retroviral particles containing either pBABE (empty vector) or pBABE-HA-LMP1 vectors. Transduced cells were placed under long-term selection in medium containing 1 μg/ml puromycin and LMP1 expression was confirmed by western blotting, which showed physiological protein levels as observed in latency type III cell lines (S1B Fig). Previously we have demonstrated that expression of the type III latency-associated EBV protein LMP1 alone was sufficient to promote PARP1-mediated PARylation [22], and this was also observed following ectopic expression of LMP1 in DG75 (S1D Fig). LMP1 positive (+) and LMP1 negative (-) cells were incubated for 72 hrs with 1 μM of the PARP inhibitor olaparib or the DMSO vehicle as a control. RNA was then isolated and prepared for RNA sequencing. We observed that the expression of 2504 genes were significantly changed (FDRπ.01) when comparing LMP1- vs LMP1+ cells, with 1578 and 926 genes upregulated and downregulated by LMP1, respectively (S2A and S2B Fig). Ingenuity Pathway Analysis (IPA) predicted HIF-1α as one of the top upstream regulators activated by LMP1 (S2D Fig). Furthermore, gene function analysis identified pathways such as glycolysis I, gluconeogenesis I, Notch signaling and B cell development to be upregulated by LMP1 (S2C Fig). Inspection of regulated genes and IPA analysis showed well-known targets of LMP1 that have been reported in prior literature, confirming that ectopic expression in DG75 could recapitulate the changes in gene expression induced by LMP1.

We then compared untreated LMP1+ cells with LMP1+ cells treated with the PARP inhibitor olaparib. In total, we observed expression of 2435 genes to be significantly changed (FDRπ.01), with balanced up and downregulation following PARP inhibition (1163 and 1272 genes, respectively) (S3A and S3B Fig). In contrast to IPA predicted HIF-1α activation by LMP1, olaparib treatment is predicted to inhibit HIF-1α in LMP1+ cells (S3D Fig). Gene function analysis also identified regulation of pathways such as glycolysis I and gluconeogenesis I by PARP1 (S3C Fig).

We then overlaid the aforementioned two datasets and introduced log2 I1I Fold Change to identify our ‘LMP1/PARP1’ targets, of which there were 292 ( Fig 1A ). Of these 292 genes, the majority (225) were upregulated by LMP1 and offset by PARP1 inhibition ( Fig 1B ). We performed unsupervised hierarchical clustering and observed that the LMP1+ samples treated with olaparib and the LMP1- samples clustered together and separately from the LMP1+ untreated samples. We observed that two clusters emerged among the LMP1/PARP1 targets, which were analyzed by IPA gene function analysis. Cluster 1 genes were upregulated by LMP1 and downregulated following PARP1 inhibition, while cluster 2 genes were downregulated by LMP1 and upregulated following PARP1 inhibition ( Fig 1C ). IPA revealed PARP1/LMP1 targets were largely involved in metabolism and signaling, with two clusters emerging from gene function analysis ( Fig 1D ). In addition, disease or function analysis identified cancer, proliferation of lymphatic system, and proliferation of lymphocytes as LMP1/PARP1 targets that were decreased following olaparib treatment ( Fig 1E ).

A) Expression of 292 genes were significantly changed (FDRπ.01 log2 I1I Fold Change) when comparing LMP1- vs LMP1+ cells and overlaying this dataset with LMP1+ untreated cells vs LMP1+ cells treated with 1 μM olaparib for 72 hrs. B) Of these 292 genes, the majority (225) were upregulated by LMP1, which was offset by PARP inhibition. C) Heat map showing two gene clusters- cluster 1 genes are those upregulated by LMP1 and subsequently downregulated following PARP1 inhibition, and cluster 2 genes are those downregulated by LMP1 and subsequently upregulated following PARP1 inhibition. Gene expression is plotted as z-score normalized FPKM values. D) Ingenuity Pathway Analysis, IPA, reveals the gene functions of the PARP1/LMP1 targets are largely involved in metabolism and signaling. E) IPA Disease or function analysis identifies cancer, proliferation of lymphatic system and proliferation of lymphocytes as being LMP1/PARP1 targets that are decreased following olaparib treatment. F) IPA identified HIF-1α as a top upstream regulator activated by LMP1/PARP1 and inhibited following PARP inhibition.

LMP1 activates HIF-1α�pendent gene expression through PARP1

IPA identified HIF-1α, as well as its dimerization partner ARNT (HIF-1B), as top upstream regulators activated by LMP1/PARP1 and repressed following PARP inhibition ( Fig 1F ). This was based on increased transcription of HIF-1α-targets by LMP1 and their downregulation following PARP inhibition ( Fig 2A ). We validated several of these HIF-1α targets by qRT-PCR in both the DG75 cell line (fold change LMP1+/LMP1-) ( Fig 2B ) as well as EBV infected cells with latency III and I setting (fold change Mutu III/I) (S5E Fig). To establish that the inhibition of HIF-1α targets was due to PARP1 inhibition rather than off-target effects of olaparib, PARP1 was knocked down in LMP1+ and LMP1- DG75 cells ( Fig 2C and 2D ). Corresponding to PARP1 inhibition with olaparib, HIF-1α targets were upregulated in LMP1 + cells vs LMP1 �lls and this upregulation was diminished by PARP1 knockdown, as shown by qRT-PCR (fold change LMP1+/LMP1-) ( Fig 2E ), indicating that PARP1 is necessary for activation of these genes by LMP1.

A) Heatmap showing HIF-1α targets that are induced in LMP1+ cells vs LMP1- cells and decreased with PARP inhibition (FDRπ.01 log2 I1I Fold Change). Gene expression is plotted as z-score normalized FPKM values. B) Validation of targets identified through RNA seq of olaparib-treated samples- qRT-PCR showing relative expression of transcripts in untreated and olaparib-treated LMP1+ cells vs untreated LMP1- cells. C) Lentiviral sh-PARP1-GFP was used to validate olaparib-treated samples. Fluorescent microscopy showing GFP expression after transduction with shControl and shPARP1 following cell sorting by FACS. D) Western blot showing knockdown of PARP1 in LMP1+ cells following lentiviral transduction with shPARP1. E) Validation of targets identified through RNA seq of olaparib-treated samples using shPARP1 cells. qRT-PCR showing relative expression of transcripts in GFP control and shPARP1 LMP1+ cells vs GFP control LMP1- cells. All RT-qPCR Expression is relative to 18s. The graphs are representative of three independent experiments and shows mean ± standard deviation.

HIF-1α and PARP1 form a PARylated complex

It has been reported in the literature that PARP1 forms a complex with HIF-1α through direct protein interaction and increases HIF-1α�pendent gene expression [34]. To see if this was the case in our B cell lines, we performed an immunoprecipitation assay and found that HIF-1α immunoprecipitated with PARP1. We also observed that the HIF-1α/PARP1 interaction was increased in LMP1+ cells (around 40%) and PARP1 inhibition caused dissociation of the complex ( Fig 3A and 3B ). Whilst this LMP1-induced global increase in HIF-1α/PARP1 interaction was modest, we observed much greater increases in LMP1-induced PARP/HIF-1α binding at specific HIF-1α-responsive gene promoters (see below).

A) Following immunoprecipitation with IgG and PARP1 antibodies, western blot for HIF-1α confirms that PARP1 immunoprecipitates with HIF-1α to a greater extent in LMP1+ vs LMP1- cells and this is attenuated by 1 μM 72 hr olaparib treatment. B) Quantification of immunoprecipitation (normalized to input) representative of three biological replicates. C) Following incubation with Poly-ADP-ribose binding macrodomain resin and Poly-ADP-ribose neg control resin, western blot for HIF-1α and PARP1 confirms that the PARP1/HIF-1α complex is PARylated in LMP1+ cells and this is abolished by 1 μM 72 hr olaparib treatment. Input represents 10% of the protein lysate used for immunoprecipitation. The western blot is representative of at least three biological replicates. D) Quantification of PAR resin (normalized to input) representative of three biological replicates. P values for significant differences (Student’s t-test) are indicated on the graphs and are summarized by three asterisks (pπ.001).

As there is an increase in PARP1 activity and HIF-1 transcriptional activation in LMP1+ cells, and inhibition of PARP1 catalytic activity reduces HIF-1 transcriptional activation, we wanted to determine if the PARP1/HIF-1α complex was PARylated in LMP1+ cells. As shown in Fig 3C and 3D , following incubation with Poly-ADP-ribose binding macrodomain resin, western blot for HIF-1α and PARP1 confirms that the PARP1/HIF-1α complex is PARylated. Specifically, LMP1+ cells exhibited a two-fold increase in HIF-1α and PARP1 levels, respectively, compared to LMP1- cells following pull down with the Poly-ADP-ribose binding macrodomain resin ( Fig 3D ). Biological replicates of the IP and PAR resin assays are shown in S6 Fig. This suggests that PARylation of HIF-1α, or proteins bound to HIF-1α in a complex, may play a role in the stability of the complex as well as the increased transcriptional activation of HIF-1α in LMP1+ cells.

PARP1 co-activates HIF-1α�pendent gene expression by binding to the promoter regions of HIF-1α targets

To determine if increased PARP activation in LMP1+ cells was augmenting HIF-1 transcriptional activation by influencing HIF-1 binding to its downstream promoters, we performed ChIP-PCR experiments on promoter regions of validated HIF-1α targets. These targets have been validated by RT-qPCR and had demonstrated increased transcription in LMP1+ cells vs LMP1- cells and decreased transcription in LMP1+ cells, following both PARP1 inhibition and PARP1 knockdown. Promoter regions of three such HIF-1α targets were bound by PARP1 and HIF-1α considerably more in LMP1+ cells vs LMP1- cells. Furthermore, binding of HIF-1α and PARP1 was reduced at promoter regions of HIF-1α targets by PARP1 inhibition in LMP1+ cells ( Fig 4A and 4B ). One exception was at the BNIP3 promoter, where no loss of HIF-1α binding following PARP1 inhibition was observed. Therefore, in the case of BNIP3, it may be that despite HIF-1α binding, the HIF-1α/PARP complex is less active and less stable following PARP inhibition (as shown by IP data and loss of PARP1 binding to BNIP3 promoter), which results in the decreased gene expression observed. This leads to the speculation that the presence of PARP1 at the promoter may be the determining factor for activation of HIF-1-responsive gene expression in a subset of HIF-1-responsive genes. However, after ChIP-PCR experiments with EBV infected cells with latency III and I setting (Mutu III/I) (S5B Fig), we did observe loss of HIF-1α binding at the BNIP promoter following PARP1 inhibition. Thus, it may simply be a cell line specific response.

ChIP-qPCR assay for A) PARP1, B) HIF-1α, C) H3K27ac and D) H3K27me3 occupancy at the ALDOC (left), HILPDA (center) and BNIP3 (right) transcription start sites (TSS) in untreated LMP1- and LMP1+ cells and LMP1+ cells treated with 1 μM olaparib for 72 h. Results are expressed as fold change over IgG. Results are representative of three independent experiments and show mean ± standard deviation.

LMP1 leads to the accumulation of positive histone marks at HIF-1α–responsive genes

As shown by the previously discussed ChIP-qPCR experiments, PARP1 is present at the promoters of HIF-1 α�pendent genes. Due to the multiple roles PARP1 can play as a chromatin modifying enzyme [11�], we wanted to determine if the increased PARP1 binding at the promoter regions of the HIF-1α targets was due to a change in the chromatin landscape of the regions. As shown in Fig 4C and S5C Fig, these targets also had significant accumulation of the positive histone mark H3K27ac. Furthermore, this mark could be lost by PARP1 inhibition, which conversely led to the accumulation of the repressive histone mark H3K27me3 ( Fig 4D and S5D Fig). This suggests that the role of PARP1 as a coactivator of HIF-1 α�pendent gene expression could be attributed to its ability to modify histone tails, creating a more permissible environment for gene transcription.

LMP1 induces a global increase in chromatin bound HIF-1α

PARP1 and PARylation can affect the ability of proteins to interact with chromatin, therefore we determined whether the activation of PARP1 by LMP1 can influence the association of HIF-1α with chromatin and whether PARP inhibition could reverse this effect. We assessed HIF-1α levels in the cytoplasmic fraction, the nuclear soluble fraction and chromatin-bound fraction by western blot and following subcellular protein fractionation. Western blot for HIF-1α confirms its localization to chromatin in LMP1+ cells, which is reduced after olaparib treatment ( Fig 5A ). Specifically, we observed a 50% increase in chromatin-bound HIF-1α in LMP1+ cells vs LMP1- cells, which was reduced to 60% of LMP1- levels following PARP inhibition ( Fig 5B ). This global increase in chromatin bound HIF-1α in LMP1+ cells further suggests LMP1 enhancing HIF-1α transcriptional activation.

A) Following subcellular protein fractionation, western blot for HIF-1α confirms that HIF-1α is more localized to chromatin in LMP1+ cells and this localization is reduced with 1 μM 72 hr olaparib treatment. Lamin B1, Tubulin beta and Histone H3 serve as nuclear, cyctoplasmic and chromatin fraction loading controls, respectively. B) Quantification (normalized to Histone H3) representative of three biological replicates. P values for significant differences (Student’s t-test) are summarized by three asterisks (pπ.001), two asterisks (pπ.01), or one asterisk (pπ.05).

LMP1 confers a ‘Warburg’ effect

Many of the HIF-1α downstream transcriptional targets activated by LMP1 through PARP1 are involved in metabolism, therefore we aimed to determine if LMP1/PARP1interaction lead to any functional metabolic effect at the cellular level. To examine this, we performed mito stress test and glycolytic rate assays using a XF96 Extracellular Flux Analyzer (Seahorse Bioscience) to measure oxygen consumption rate (OCR) and extracellular acidification rate (ECAR). For the mito stress test, OCR and ECAR were detected under basal conditions followed by the sequential addition of oligomycin, fluoro-carbonyl cyanide phenylhydrazone (FCCP) and rotenone + antimycin A. As shown in Fig 6B , mitochondrial respiration is significantly decreased in LMP1+ cells. PARP1 inhibition in these cells subsequently leads to an increase in mitochondrial respiration ( Fig 6C ). This suggests that LMP-mediated activation of PARP1 leads to decreased reliance on mitochondrial respiration in the cell. PARP1 activation has been shown to damage mitochondrial activity characterized by secondary mitochondrial superoxide production, distorted mitochondrial structure and reduced mitochondrial oxidation and ATP production [36]. This can be seen by the decreased ATP synthase-linked ATP production in LMP1+ cells followed by increase in ATP levels after PARP inhibition ( Fig 6D ). In the LMP1- cells, we observed an increase in basal respiration upon olaparib treatment, similar to that seen in LMP1+/+ olaparib group. However, olaparib treatment in the LMP1- cells resulted in a decrease in maximal respiration (S10A Fig). We think the differences observed in the maximal respiration was due to the contrast in PARP1 activation states between LMP1- and LMP1+ cells and the resulting disparity in olaparib sensitivity between the two (discussed further below).

A) Schematic of mitochondrial stress test. Oxygen consumption rate (OCR) comparing B) LMP1+ vs LMP1- cells and C) LMP1+ untreated cells vs LMP+ cells treated with olaparib. D) Individual parameters for basal respiration (left), maximal respiration (middle) and ATP synthase-linked ATP synthesis (right). DG75 cells were pre-treated with 2.5 μM olaparib for 48 hrs and were then seeded to 300,000 cells/well and incubated for 1 h in XF base medium. Oxygen consumption rate was measured in XF base medium supplemented with glutamine (2 mM), glucose (10 mM), sodium pyruvate (1 mM) under basal conditions followed by the sequential addition of oligomycin (2 μM), FCCP (1 μM), and rotenone & antimycin A (2 μM), as indicated. Each data point represents an OCR measurement. Data are expressed as means ± SD, n = 6 technical replicates. The graphs are representative of four biological replicates. P values for significant differences (Student’s t-test) are summarized by three asterisks (pπ.001) and groups are compared to LMP1+ untreated samples.

Apart from Mitochondrial respiration, the other major cellular energy pathway is glycolysis. Due to the decreased reliance on mitochondrial respiration by LMP1, and knowing that HIFs activate transcription programs which induce glycolysis and inhibit mitochondrial activity [37], we wanted to determine if LMP1 promotes a switch to glycolytic metabolism. To accomplish this, we used the glycolytic rate test procedure to measure the OCR and ECAR. Both were detected under basal conditions followed by the sequential addition of 2μM rotenone + 2 μM antimycin A and 50 mM 2-deoxy-D-glucose. As shown in Fig 7B, 7D and 7E , LMP1 confers a ‘Warburg’ effect, significantly increasing basal and compensatory glycolysis in the cell under aerobic conditions. PARP inhibition subsequently decreased this effect ( Fig 7C, 7D and 7E ) but had no impact on LMP1- cells (S10B Fig). This suggests that LMP-mediated activation of PARP1 not only leads to diminished reliance on mitochondrial respiration, but also to an increase in aerobic glycolysis. How much of this is mediated distinctly through PARP1, or HIF-1α, or a combination of the two, needs to be elucidated with further experimentation.

A) Schematic of glycolytic rate assay. Glycolytic proton efflux rate (glycoPER) comparing B) LMP1+ vs LMP1- cells and C) LMP1+ untreated cells vs LMP+ cells treated with olaparib. Individual parameters for D) basal glycolysis and E) compensatory glycolysis. DG75 cells were pre-treated with 2.5 μM olaparib for 48 hrs and were then seeded to 300,000 cells/well and incubated for 1 h in XF base medium. glycoPER was measured in Seahorse XF Base Medium without phenol red with 2 mM glutamine, 10 mM glucose, 1 mM pyruvate, and 5.0 mM HEPES XF media. ECAR was detected under basal conditions followed by the sequential addition of 2μM rotenone + 2 μM antimycin A and 50 mM 2-deoxy-D-glucose (2-DG). Each data point represents an ECAR measurement. Data are expressed as means ± SD, n = 6 technical replicates. The graphs are representative of four biological replicates. P values for significant differences (Student’s t-test) are summarized by three asterisks (pπ.001) and groups are compared to LMP1+ untreated samples.

LMP1 provides a proliferative advantage that can be eradicated following PARP inhibition

Warburg metabolism is thought to enable rapid cell division through the creation of excess carbon obtained from increased glucose consumption, which can subsequently be used to fuel anabolic processes. This excess carbon can then be diverted into the various branching pathways that stem from glycolysis and subsequently used for the production of nucleotides, lipids, and proteins [38]. Activated T cells extensively and rapidly proliferate upon activation and have been shown to engage Warburg metabolism [38, 39]. B cells share certain fundamental metabolic characteristics with T cells, such as increased glucose uptake and induction of glycolysis after activation [40, 41].

As LMP1 appears to be engaging ‘Warburg metabolism’, and our IPA analysis suggested increased proliferation of cells with LMP1 ( Fig 8A ), we wanted to determine if this conferred a proliferative advantage. To ascertain this, we measured cellular proliferation by staining cells with CFSE (5(6)-Carboxyfluorescein N-hydroxysuccinimidyl ester) staining. CFSE Uptake at time zero was the same for both LMP1+ and LMP1- cells (S8B Fig). We then allowed cells to proliferate for 96 hrs before proceeding with FACS analysis. LMP1 presence led to increased proliferation vs LMP1- cells ( Fig 8B ), which was markedly curtailed following PARP1 inhibition ( Fig 8C ). In contrast, proliferation of LMP1- cells was only marginally reduced following PARP inhibition (S4A Fig). This olaparib-induced decrease in proliferation in LMP1+ cells coincided with in an arrest in G2/M ( Fig 8D ) but appeared to be independent of DNA damage, as we found no evidence of yH2A.x accumulation following intracellular staining and FACS analysis (S1E Fig). Furthermore, we found no evidence of PARP inhibition (1 μM 72 hrs) leading to apoptotic cell death, as determined by Annexin V staining (S1F Fig).

A) IPA analysis predicts LMP1 to activate proliferation pathways and PARP inhibition to inactivate proliferation pathways. B and C) Cells were stained by CFSE (5(6)-Carboxyfluorescein N-hydroxysuccinimidyl ester) and allowed to proliferate for 96 hrs- LMP1+ vs LMP1- CFSE labeled cells and LMP1+ untreated cells vs olaprib-treated LMP+ CFSE labeled cells were then detected by FACS analysis, respectively. D) Cell cycle analysis- LMP1+ cells were incubated with 1 μM olaparib for 72 hrs. Cells were then harvested, fixed and permeabilized in absolute ethanol and then incubated with propidium iodide (PI) and RNAse A for 30 mins at 37C and analyzed by FACS. E and F) Methylcellulose colony forming cell (CFC) assay- 500 LMP1+ cells, untreated and pre-treated with 2.5 μM olaparib for 96 hrs, were seeded in methylcellulose media and incubated for 14 days at 37ଌ. Images were captured following staining with crystal violet and unstained at 4X magnification, respectively.

We then used the methylcellulose colony forming cell (CFC) assay to determine the impact of LMP1 and PARP inhibition on the cells’ ability to proliferate and differentiate into colonies. Cells were pre-treated with 2.5 μM olaparib for 96 hrs. Following this pre-treatment, cells were checked for viability using the Annexin V assay (S8A Fig). After confirmation of cell viability, cells were seeded and incubated in CFC media for 14 days. As shown by Fig 8E and 8F , LMP1 enabled cells to form robust colonies. However, colonies were not able to form following olaparib treatment.


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Watch the video: Flow Cytometry Animation (May 2022).


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