Article pubs.acs.org/jpr
Quantitative Proteomic Characterization of Ethanol-Responsive Pathways in Rat Microglial Cells Harris Bell-Temin,† Ping Zhang,‡ Dale Chaput,† Michael A. King,§,# Min You,∥ Bin Liu,*,‡ and Stanley M. Stevens, Jr.*,† †
Department of Cell Biology, Microbiology, and Molecular Biology, University of South Florida, 4202 East Fowler Avenue, Tampa, Florida 33620, United States ‡ Department of Pharmacodynamics, §Department of Pharmacology and Therapeutics, University of Florida, 1600 SW Archer Rd., Gainesville, Florida 32610, United States # Department of Veterans Affairs Medical Center, 1601 SW Archer Road, Gainesville, Florida 32608, United States ∥ Department of Molecular Pharmacology and Physiology, University of South Florida, 12901 Bruce B. Downs Boulevard, Tampa, Florida 33612, United States S Supporting Information *
ABSTRACT: Long-term exposure to alcohol can have profound effects on the central nervous system including pathophysiological consequences associated with neuroinflammation. Along with astroglia, microglia play an important role in the neuroinflammatory response. Using a SILAC-labeled rat microglial cell line, an expression profile of 2994 proteins was identified in ethanol-treated microglial cells, where 160 and 69 protein groups were determined to be significantly upregulated and downregulated, respectively. In addition, SILAC-based proteomic analysis of lipopolysaccharide-treated microglial cells was performed in order to generate a reference data set representing a “classical” (M1) macrophage activation response in order to compare to the differential protein expression profile of ethanol-treated microglia. On the basis of this comparison as well as other validation experiments performed in this study, ethanol appears to induce partial activation of microglia that is devoid of conventional markers that indicate an M1 phenotype. This study is the first comprehensive proteomic analysis to assess the impact of acute ethanol exposure on microglial function and will provide a significant foundation that includes novel protein markers for future work aimed to characterize the molecular mechanisms associated with ethanol-induced microglial activation and its role in neurodegeneration. KEYWORDS: microglia, SILAC, ethanol, lipopolysaccharide, CSF1R, PU.1, p53
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INTRODUCTION
Compared to microglia, the effects of ethanol on astroglia have been more thoroughly studied. At higher concentrations, ethanol has been shown to inhibit astrocyte proliferation or even inflict cellular damages.11,12 In cerebral cortex of ethanolfed rats or ethanol-treated astrocyte cultures, increased expression of interleukin-1 beta (IL-1β), cyclooxygenase-2 (COX-2), and inducible NO synthase (iNOS) was observed.13−15 The effects of ethanol on microglia, on the other hand, are much less clear. Until recently, few and sometimes conflicting reports are in the literature concerning the effects of alcohol on microglia. Intermittent alcohol administration increased the number of microglia in rat cerebellum.16 Recent studies report the induction of microglia specific markers in specific brain regions of human alcoholics.17 Animal studies also suggest that ethanol can modulate microglial signal transduction leading to proliferation, activation, and production of
Alcohol abuse is known to cause cognitive impairment or dementia as a result of ethanol-induced aberrations in neurotransmission, synaptic plasticity and ultimately neurodegeneration.1−4 The mechanisms underlying ethanol-induced neurotoxicity are still not clearly understood. Although some studies suggest direct neurotoxic effects of ethanol and its metabolites as well as induction of a hyperglutamatergic state, increasing evidence supports a role for oxidative stress, especially the involvement of neuroinflammation in mediating alcohol’s neurotoxicity.5−8 Activation of microglia and astroglia is the key feature of neuroinflammation.9,10 Activated glia, especially microglia, produce pro-inflammatory factors including cytokines, reactive oxygen species (ROS), reactive nitrogen species (RNS) such as nitric oxide (NO), and lipid mediators. Overproduction and accumulation of these reactive soluble factors, over time, impact neurons to cause deleterious effect to their functionality and structural integrity. © 2013 American Chemical Society
Received: November 2, 2012 Published: March 15, 2013 2067
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proinflammatory cytokines.6,18−20 While these reports suggest a critical role for microglia in the effects of ethanol, other studies suggest that ethanol may inhibit microglial response to inflammatory stimuli.21 In addition, a number of studies have reported conflicting observations on the effect of alcohol on microglial ROS production.22,23 The identification of individual factors and/or pathways has yielded important information on the effects of ethanol on brain immune cells. However, a comprehensive and global understanding of these effects is lacking. In this study, we have used a proteomic approach to characterize the global responses to ethanol in a rat microglial cell model whose proteome has recently been cataloged by us.24 We have uncovered key ethanol responsive microglial pathways that may potentially lead us to gain a better understanding of ethanol neurotoxicity in humans.
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8% acrylamide gel cartridge as described previously.31 Samples were loaded in acetate loading buffer (Protein Discovery) in liquid phase, and ran across the gel at 50 V for 68 min and 100 V for 60 additional minutes. Samples were collected in liquid phase from the collection chamber on the far side of the gel tube at various timed intervals. Samples were desalted using solid phase enrichment C18 columns as described previously.24 Samples were loaded and washed in 0.1% formic acid and eluted using 90% acetonitrile/ 10% formic acid. Samples were concentrated in a vacuum concentrator (Thermo) prior to resuspension in 5 mM ammonium formate and 25% ACN for SCX fractionation. In an attempt to increase proteome coverage using complementary fractionation techniques, additional quantities of the original treatment samples were fractionated using a Dionex U3000 HPLC system fitted with a 200 mm × 1 mm I.D. polysulfethyl strong cation exchange column (PolyLC) as described previously.24 Samples were eluted on a gradient of 15 mM to 200 mM ammonium formate in 25% ACN for 30 min. Fractions were collected at 2 min intervals and peptide containing fractions in the +2/+3 charge range were selected based on UV trace intensity. Samples were concentrated again in a vacuum concentrator and resuspended in 0.1% formic acid prior to LC−MS/MS analysis.
EXPERIMENTAL METHODS
Chemicals and Reagents
All chemicals and reagents were purchased from Fisher Scientific (Pittsburgh, PA) unless otherwise specified. Cell Culture and Treatment
Rat highly aggressively proliferating immortalized (HAPI) microglial cells24−26 were grown in parallel cultures of heavy and light SILAC media at 37 °C and 5% CO2.27 SILAC media consisted of DMEM denuded of arginine and lysine (Cambridge Isotope), supplemented with 5% dialyzed fetal bovine serum (Cambridge Isotope), 100 U/mL penicillin, 100 μg/mL streptomycin, and 100 μg/mL each of unlabeled L-arginine and 13 L-lysine for the “Light” media or C6 L-lysine and 13C6 Larginine for the “Heavy” media (Cambridge Isotope). Media was changed every 48 h and cells were allowed six doublings in “Light” or “Heavy” SILAC media to achieve a high level (>98%) of labeled amino acid incorporation. Cells grown in “Light” media were then treated, in triplicate, for 12 h with media alone to establish a negative control. Cells grown in “Heavy” media were treated in triplicate for 12 h in either 50 mM ethanol or, as a positive control, 30 ng/mL lipopolysaccharide (LPS), a potent stimulator of microglia.28,29 At the end of the treatment, cells were washed three times, scraped in ice cold PBS, pelleted by centrifugation for 5 min at 500× g and then resuspended in 100 mM Tris-HCl, pH 7.6, 4% w/v SDS, and 100 mM dithiothreitol. Cells were lysed by heating for 5 min at 95 °C followed by a brief sonication. The cell lysate was cleared by centrifugation at 16000× g for 5 min and the supernatant was collected for further analysis.
LC−MS/MS and Statistical Analysis
Fractions were separated on a 10 cm × 75 μm I.D. reversed phase column packed with 5 μm C18 material with 300 Å pore size (New Objective) using 125 min gradients of 2−40% ACN in 0.1% formic acid. Inline mass spectrometric analysis was performed on an Orbitrap XL (Thermo). Survey scans were performed at a resolving power of 60000, and the top 5 most abundant peaks were selected for subsequent MS/MS analysis. Raw files were processed in MaxQuant 1.2.2.5 employing the Andromeda search algorithm and Perseus version 1.2.0.13 against the UniprotKB reference database for Rattus norvegicus, concatenated with randomized protein sequences.32,33 A second database of known contaminants provided with the MaxQuant suite was also employed. All fractions for each biological sample were combined for analysis. Constant modification of carbamidomethylation of cysteine and variable modifications of oxidized methionine and acetylated protein Ntermini were used. A false discovery rate of 1% was used for peptides and proteins. A minimum peptide length of 6 amino acids was used. Razor and unique peptides were used for identification and quantification. Normalized ratios from MaxQuant for each biological sample were input into the Perseus processing suite. Statistical significance of quantitation values was determined using a student’s two-tailed t test with a significance value of p < 0.05 respective and irrespective to intensity. Protein expression changes were considered statistically significant if the intensity weighted ratio mean of all biological samples was significant in addition to significance in two or more biological samples.
Digestion, Desalting and Fractionation
Protein concentrations were determined using the 660 nm Protein Assay supplemented with Ionic Detergent Compatibility Reagent supplementation (Pierce). Equal amounts of “Heavy” treatment and “Light” control lysates were combined and then digested using the FASP method.24,30 Briefly, 30 μL of combined lysate was added to a spin column prewetted with 8 M urea. Lysate buffer was exchanged to 8 M urea using centrifugation prior to iodoacetamide alkylation and then exchanged to 50 mM ammonium bicarbonate for trypsin digestion at a ratio of 1:100 (w/w). Digestion was carried out overnight in a humidified incubator at 37 °C and terminated with the addition of 5% formic acid. Fractionation using the GelFree fractionation system (Protein Discovery) was employed to separate by size on an
Pathway Analysis
Geometric means for total protein expression ratios across biological samples were calculated respective to intensity. Geometric means and Uniprot Protein identification numbers were uploaded to Ingenuity Pathway Analysis (IPA) to determine localization, molecular function, and protein interactions.34 Upstream regulator analysis was also performed within IPA where activity of potential upstream regulators is predicted based on the expression profile of known down2068
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Madison, WI). cDNA was prepared from total RNA using the High Capacity cDNA Archive kit (Applied Biosystems, Carlsbad, CA) with a Perkin-Elmer 9600 thermocycler following the manufacturer’s suggested protocols. Real-time qPCR analysis was performed using PCR primers for TNFα and IL-6 and a housekeeping gene (18S RNA) obtained from Applied Biosystems as the premade TaqMan Gene Expression Assay systems. Briefly, 50 ng of cDNA was mixed with the TaqMan primers, Fast Universal PCR Master Mix with the FAM dye-based MGB probe following the manufacturer’s suggested protocol. The PCR amplification reaction was performed using the Applied Biosystems Model 7000 Sequence Detection System and the following reaction conditions: 50 °C for 2 min, 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. The abundance of mRNA of TNFα and IL-6 genes was normalized against the abundance of the housekeeping gene (18S rRNA) amplified on the same 96-well plate under the same condition as target genes, using the relative cycle threshold (ΔCt) method as we previously described.38 For detection of p21 mRNA, quantitative RTPCR was performed using iTaq Universal SYBR Mix (BioRad) and bioinformatically validated Quantitect Primer Assays for rat p21 and GAPDH (Qiagen) using an Eppendorf Realplex2Mastercycler. Twenty μL reactions were used for RT-PCR which included 10 μL SYBR Green Master Mix, 2 μL Primer, 6 μL H2O, and 2 μL cDNA (1:10 cDNA for GAPDH, undiluted cDNA for p21). Activation of polymerase was done at 95 °C for 30 s, followed by 40 cycles of denaturation for 15 s at 95 °C, annealing for 15 s at 60 °C, and extension for 20 s at 60 °C during which data collection took place.
stream targets in relation to known upstream regulatormediated expression changes reported in the literature. This analysis determines the significance of overlap of the detected targets with the upstream regulator through a Fisher’s exact test in addition to implementation of a z-score algorithm to make predictions of the direction of upstream regulator activity change. Description of the z-score algorithm is available on the IPA Web site (www.ingenuity.com). Western Blotting
HAPI cells were seeded in 6-cm culture dishes (1 × 106 cells/ dish) and grown to near confluence in DMEM supplemented with 5% FBS. Cells were treated for 12 h with fresh media alone (control) or that containing ethanol or LPS. Afterward, cells were rinsed 3× with ice-cold PBS and then scraped into 250 μL/dish lysis buffer containing detergent and protease inhibitors.26,35 Cells were disrupted by brief sonication bursts and cell homogenate was centrifuged at 4 °C for 30 min at 20000× g. Supernatant was saved as total cell lysate and protein concentration was determined using the BCA reagents with bovine serum albumin (BSA) as a standard. Proteins were separated on SDS-PAGE gels and transferred to nitrocellulose membrane. Ponceau S staining of the blot was used to further confirm equal protein loading and proper transfer.26,35 Following blocking with 5% dry milk in PBS containing 0.1% Tween-20 (T-PBS), the blot was incubated overnight at 4 °C with a mouse monoclonal anti-iNOS antibody (1:2500, BD Bioscience), a rabbit polyclonal antiPU.1 antibody (1:500, Santa Cruz Biotechnology), or a rabbit polyclonal antiacetyl(ac)-p53 antibody (1:200, Millipore). Bound primary antibodies were detected by incubation with goat antimouse or antirabbit secondary antibodies (1:5000 to 1:10000) followed by chemilluminescence detection reagents (Pierce). For iNOS, the images were recorded with the BioRad ChemiDoc XRS imager analyzer and analyzed with the BioRad QuantityOne software. The band intensity was normalized against that of β-actin which was detected following stripping and reprobing as previously described.26,35 For PU.1 and acp53, images were recorded on film (Invitrogen) and quantified by densitometric analysis. The band intensity of PU.1 and acp53 was normalized against that of GAPDH detected with a rabbit polyclonal anti-GAPDH antibody (1:5000, Cell Signaling Technology) following stripping and reprobing.36
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RESULTS AND DISCUSSION
Proteomic Analysis of Lipopolysaccharide-treated Microglia
Across the 3 biological samples, MaxQuant identified 2992 protein groups; a protein group defines as the smallest unit discernible due to peptides shared between isoforms and other similar proteins, after the removal of false hits and common contaminants. In total, 17005 peptides were identified across the three biological samples, averaging 5.7 unique peptides per protein group. Biological samples A, B, and C included 2658, 2442, and 2650 protein groups, respectively. As shown in Figure 1A, 71.2% of all protein groups were represented in all three biological samples, and 87.6% of all protein groups were found in at least two of the biological samples. MaxQuant quantified 2958 protein groups with a discernible heavy to light isotope pair ratio, 98.9% of all protein group identifications. For the remaining 1.1% of protein groups, only one member of the SILAC pair was recognized by the MaxQuant software. In total, 16704 peptides were quantified across the three biological samples, 98.2% of all peptides identified. An average of 5.65 peptides were quantified per protein group. Biological samples A, B, and C included 2682, 2060, and 2396 quantified protein groups, respectively. 59.3% of quantified protein groups were found in all three biological samples, and 82.0% of quantified protein groups were found in at least two biological samples. The log2 distribution of the ratio values has a median fold change of 0.057 as shown in Figure 2A. After statistical analysis, 234 protein groups were found to have significant changes to protein expression level, with 176 exhibiting upregulation and 58 exhibiting downregulation. Seventy nine of the upregulated and 18 of the
Immunofluorescence Analysis
Cells (3 × 105/well) were seeded onto poly-D-lysine-coated 25 mm Thermanox plastic coverslips (NUNC, Rochester, NY) kept in 6-well culture plates and grown overnight in DMEM supplemented with 5% FBS.37 Following treatment, cells were rinsed with PBS and fixed for 10 min in 4% formaldehyde.35 After blocking for 2 h in 3% BSA, cells were incubated overnight at 4 °C with a rabbit polyclonal anti-CSF1R antibody (2.5 μg/mL, Abnova, Walnut, CA). Bound primary antibody was visualized by incubation with biotinylated goat-antirabbit IgG followed by avidin Texas Red. Fluorescent images were captured with an Olympus BH-2 epifluorescence microscope equipped with a Hitachi KP-D581 digital video camera. Images were analyzed and quantified using the ImagePro Plus software. Real-time qPCR Analysis
Cells were grown to near confluence in 6-cm culture dishes in DMEM-5% FBS. After treatment, total RNA was extracted from the cells with TRIzol reagent (Invitrogen, Grand Island, NY) and treated with RQ1 RNase-free DNase (Promega, 2069
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abundant source of protein was the nucleus (27.3%), with 739 protein groups. 186 (6.7%) protein groups identified were located in the plasma membrane, and 34 (1.2%) protein groups were localized to the extracellular space. Three hundred and nineteen (11.5%) protein groups had unknown or multiple compartments for localization. The most abundant molecular function assignment, as found in Supplemental Table 1 and Supplemental Figure 2, Supporting Information, was enzyme, followed by transporter and transcription regulator. Lipopolysaccharide and Classical Microglial Activation (M1 Phenotype)
As expected, lipopolysaccharide was able to activate the inducible nitric oxide synthase pathway in the HAPI microglial cells. In the LPS activation cascade, the toll like receptor (TLR)-2 and 4 act through MEK1 and PI3K to increase levels of nuclear factor-kappa B (NF-κB).39−43 NF-κB is a transcription factor which in turn increases levels of inducible nitric oxide synthase (iNOS).44,45 iNOS levels are also affected directly by TLR2 activation through MKK4/7, JNK and p38 MAPK.39,46,47 STAT1 also acts through the JAK/STAT pathway to increase levels of iNOS.48−51 Several members of the LPS activation cascade were identified in the LPS exposure data set, as shown in Figures 3A and B. Of these, TLR2, STAT1, NF-κB, and iNOS were significantly upregulated. iNOS had the sixth highest expression ratio in the data set with a fold change of 14.30 ± 0.90 (mean ± SEM). Independent Western blot validation showed that under the same treatment conditions used for the proteomic analysis (30 ng/mL, 12 h), LPS induced a 15.9 ± 1.8 fold increase in iNOS protein expression in HAPI microglial cells (Figure 3C). In response to LPS treatment, NADH-ubiquinone oxidoreductase 75 kDa subunit was shown to be significantly downregulated with an observed fold change of −2.99 ± 0.02. NADH-ubiquinone oxidoreductase is the first complex of the electron transport chain and oxidizes NADH to NAD+. Interestingly, a lack of NADH-ubiquinone oxidoreductase activity due to a disease state is implicated in the build-up of superoxide ions;52 this build-up in LPS-treated microglia could be part of the microglial oxidative burst response to bacteria or mitochondrial functional changes associated with classical activation of microglia, of which the downregulation of NADH-ubiquinone oxidoreductase may play an important role.
Figure 1. Venn diagrams of protein identification and quantification. Overlap of all proteins identified and quantified in the LPS and Ethanol treatment data sets. Proteins can be identified if either member of the SILAC pair can be identified. Proteins are quantified if both members of the SILAC pair can be identified by the MaxQuant processing suite.
Proteomic Analysis of Ethanol-treated Microglia
From the 3 biological samples treated with 50 mM ethanol, MaxQuant identified 3032 protein groups after removal of false random hits and common contaminants. A total of 19691 peptides were identified from the 3 biological samples. The protein groups averaged 6.5 unique peptides per protein group. In total, 2697, 2493 and 2680 protein groups were found in biological samples A, B, and C, respectively, 2175 protein groups, 71.2% of the total identifications, were found in all three biological samples, and 87.8% of identifications were found in at least two of three biological samples. MaxQuant was able to quantify protein expression for 2994 of the identified protein groups, 98.7% of all protein group identifications, from the ethanol treatment biological samples. A total of 19330 peptides had discernible isotope pairs for ratio generation, 98.1% of the total number of peptides identified. The protein groups had 6.5 quantified peptides per protein group. A total of 2658, 2442, and 2650 quantification values were generated for biological samples A, B, and C, respectively, 71.2% of all quantified protein groups were seen in all 3
Figure 2. Protein expression ratio distributions. Log2 distributions of the intensity weighted mean ratios of all quantified protein groups across the three biological samples for LPS and ethanol treatment data sets.
downregulated protein groups had expression level changes greater than 2-fold. Of the 2764 protein groups recognized by Ingenuity Pathway Analysis, 1486 (53.8%) protein groups were localized to the cytoplasm, as shown in Supplemental Table 1 and Supplemental Figure 1B, Supporting Information. The second most 2070
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Figure 3. iNOS pathway and constituent expression values. (A) Select proteins quantified in the LPS data set crucial to the production of inducible nitric oxide synthase, and (B) intensity weighted mean expression level ratios of the proteins in the pathway, significantly upregulated proteins marked **, p < 0.01, n ≥ 2.. Proteins with statistically significant upregulation in the LPS data set are shown in the pathway in bold italics. STAT was not confirmed to be in the dimerized confirmation, but has been shown as such for clarity. (C) Western blot analysis of LPS-induced iNOS protein expression in HAPI cells. Cells were treated for 12 h with media alone (0), 5 or 30 ng/mL LPS. Total cell lysate was analyzed for iNOS protein expression levels (normalized against β-actin) as described in the Methods. Results are mean ± SEM from 3 separate experiments. *, **, p < 0.05 and 0.005 respectively compared to control (ANOVA analysis).
groups were determined to be derived from the plasma membrane. Forty-five (1.6%) protein groups derived from the extracellular space. The remaining 309 (10.9%) proteins were of indeterminate localization or are found in multiple cellular compartments. The most common molecular function assignment was enzyme, followed by transporter and transcription regulator as shown in Supplemental Table 1 and Supplemental Figure 3, Supporting Information.
biological samples, and 87.6% of quantified protein groups were found in at least two biological samples. The log2 distribution of the normalized ratio values has a median fold change of 0.09 as shown in Figure 2B. Following statistical analysis, 160 protein groups were determined to be significantly upregulated, and 69 protein groups were determined to be significantly downregulated. Of these, 48 protein groups were upregulated by a 2-fold change or greater, and 20 were downregulated by a 2-fold change or greater. The smallest significant upregulation was 1.35, and the smallest significant downregulation was −1.29. Of the 2824 protein groups recognized by Ingenuity Pathway Analysis from the ethanol treatment samples, 1477 (52.3%) are cytoplasmic, as shown in Supplemental Table 1 and Supplemental Figure 1A. The second most abundant cellular compartment assignment was the nucleus, with 815 (28.9%) protein groups. One-hundred seventy-eight (6.3%) protein
Ethanol-induced Pathway Alterations in Microglia
Ethanol causes a widely observed increase in membrane fluidity.53−58 One of the regulatory pathways for membrane fluidity is the conversion of saturated fatty acids to unsaturated fatty acids through the activity of stearoyl-CoA desaturase. Our data set shows stearoyl-CoA desaturase has a statistically significant increase in protein expression and a fold change of 6.32 ± 0.03. Cholesterol biosynthesis is also an important pathway for maintenance of membrane fluidity; HMG-CoA 2071
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Figure 4. Comparison of expression of proteins from the LPS and ethanol data sets. Log2 intensity weighted mean expression level ratios of select proteins from the LPS and ethanol treatment data sets relating to microglial activation. Significantly up-regulated proteins marked **, p < 0.01, n ≥ 2, or *, p < 0.05, n ≥ 2.
Figure 5. Microglial homing pathway and constituent expression values. Select proteins proposed to be involved in the homing activation response of microglia following ethanol treatment. Significantly upregulated (n ≥ 2, p < 0.01) are shown on the pathway (A) in bold italics. Fold changes for the proteins are found in the bar chart (B), significance delineated **, n ≥ 2, p < 0.01.
reductase is the rate-controlling enzyme in this pathway,59,60 and is found in our ethanol data set to be upregulated by a factor of 5.81 ± 0.24. Cholesterol also plays an important role in the formation of lipid rafts, which serve as support structures for receptors, such as the upregulated TLR2 seen in our ethanol data set.61,62 Interestingly, the suppression of LPS-induced iNOS expression through the NF-kappa-B pathway through the action of the histone acetyltransferase p300 has been linked to ethanol treatment.21 In the ethanol data set, iNOS was not found at detectable levels, and NF-kappa-B showed no significant upregulation, as was seen in the LPS data set. Also of interest is the fact that TLR-2 was significantly upregulated in the ethanol data set and its downstream target NF-kappa-B was not. This observation is consistent with the previously shown importance of TLRs in microglial ethanol response.63−65 The co-observed phenomena of increased TLR2 and cholesterol synthesis proteins support the hypothesis that lipid rafts are crucial to the ethanol-induced activation of microglia.63 Also observed in the ethanol data set is a statistically significant upregulation of colony stimulating factor 1 receptor (CSF1R), the receptor for CSF1, by a fold change of 4.53 ± 0.56. Colony stimulating factor has been shown to have a downregulating effect on macrophage apoptotic pathways including those triggered by TNFα, and a neuroprotective effect by stimulating phagocytosis.66−68 Interestingly, CSF1R is a downstream target of the transcription factor, PU.1,69 which was another protein that was identified as significantly upregulated by a fold change of 2.39 ± 0.23 in microglia after ethanol exposure.
Microglia also produce various potentially neurotoxic factors including glutamate as part of their defense mechanism.70 Glutamate is removed via the action of glutamine synthetase, Glns, which catalyzes the condensation of glutamate and ammonia to form glutamine. In the ethanol data set, glutamine synthetase was found to be downregulated in the ethanol data set by a factor of −10.43 ± 0.26, and by a factor of −11.65 ± 0.59 in the LPS data set. This observation suggests the importance of glutamate build up in microglial activation response. The expression levels of these selected proteins in microglia after LPS and ethanol exposure are shown as a comparison in Figure 4. Ethanol and Microglial Homing Response
Microglia transit through multiple phases of activation following detection of injury or the presence of foreign substances as catalogued by Raivich et al.71 The resting phase (0) is characterized by microglial immune surveillance utilizing long branched processes. The alert phase (1) is the earliest stage of microglial activation and is marked by an increase in immunoreactivity and cell surface integrins relating to adhesion.72 The alert phase is followed by a homing phase, marked by increasing production of adhesion proteins and CSF1R as the microglia respond to neural injury. As mentioned previously, CSF1R and its regulator PU.1 were found to be significantly upregulated in the ethanol data set. CBFB (core binding factor, beta unit), was significantly upregulated and binds a promoter to stimulate CSF1R production as well.73 PU.1 also leads to upregulation of MCL1, an antiapototic protein involved in inflammatory response. PU.1 is upregulated 2072
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SILAC data set). Both the ethanol and LPS treatment sets have an additional band at ∼8 kD higher molecular weight compared to the control treatments (Figure 6A) which could be monoubiquitination, consistent with previously shown ubiquitination that can occur on PU.1 in a macrophage cell line following LPS exposure.81 The apparent discrepancy in magnitude of fold change when comparing the Western blot and SILAC analysis could be due to the fact that the peptide used in the SILAC quantitation was from both the modified and unmodified forms of PU.1 while only the major band in the Western was used for quantitation and that the intensity of the higher molecular weight minor band appeared to be far greater for ethanol and LPS-treated microglia than that of the control microglia (Figure 6A). It is important to note that monoubiquitination of transcription factors has been shown to modulate transcriptional activity. For example, monoubiquitination induces nuclear relocalization and increases transcriptional activity of the Forkhead box O transcription factor, FOXO4.82 Taken together, this modification is a plausible mechanism for future investigation into PU.1 activation in microglial cells. Moreover, the C/EBP-α/PU.1 pathway is an important network underlying microglial/macrophage activation where C/EBP-α (a target of miR-124 which is differentially expressed in activated microglia) and PU.1 upregulation, a C/ EBP-α target, can occur upon macrophage activation.83 In addition to PU.1, the proteomically identified upregulation of the PU.1 downstream target, CSF1R (Figure 4), was validated by immunofluorescence analysis that showed a significantly increased cellular expression of CSF1R in ethanol-treated microglial cells compared to the control cells (Figure 7).
by activated PI3K, which was not found to be significantly upregulated by our filtering method, nonetheless exhibits a highly reproducible fold change increase of 1.33 ± 0.02 across the three biological samples. The protein CD9 modulates cell adhesion in concert with integrins and activates PI3K, which in turn activates PU.1 and PU.1 target MCL1.74−76 CD9 copurifies with LGALS3BP (lectin, galactoside-binding, soluble, 3 binding protein), a protein involved in adhesion that also binds to integrins.77,78 A putative pathway using the proteins found in the ethanol data set and their fold changes above control levels can be seen in Figure 5. Interestingly, a recent study using a four-day alcohol binge animal model showed that microglia are only partially activated in vivo, up to the homing phase of activation which is consistent with our in vitro acute exposure model.79 The results from this SILAC analysis have now provided more detailed information at the molecular level regarding the ethanol-induced microglial response as well as this particular phase of activation in general. Upstream Regulator Activity Prediction in Ethanol-treated Microglia
As mentioned in the previous section, ethanol-treated microglia demonstrated expression changes in proteins involved in microglial activation, including a key transcription factor known to regulate macrophage function, PU.180 and its downstream targets such as CSF1R. PU.1, an ETS-domain transcription factor, was identified from the proteomic analysis of ethanol-treated microglia as upregulated (2.39 fold change). Representative extracted ion and base peak chromatograms of the SILAC pair for the detected PU.1 peptide marking the ∼2fold upregulation from ethanol treatment can be found in Supplemental Figure 4, Supporting Information. This observed upregulation was validated by Western blot analysis (Figure 6) and interestingly, PU.1 upregulation was also observed in LPStreated microglia (although PU.1 was not detected in the LPS
Figure 7. CSF1R expression in ethanol-treated microglia. Immunofluorescence analysis validated the increased expression of CSF1R identified from SILAC analysis of ethanol-treated microglia. Cells were treated for 12 h with 50 mM ethanol. (A) Representative fluorescence photomicrographs. (B) Quantitative analysis. Cells from two experiments performed in duplicate were analyzed and the fluorescence intensities of 921 to 2115 cells/condition were quantified. ***, p < 0.001 compared to control cells (ANOVA analysis with Duncan’s post hoc analysis).
Figure 6. PU.1 expression in LPS and ethanol-treated microglia. Western blot analysis results validated the increased expression of PU.1 identified from SILAC analysis of ethanol-treated microglia (n = 3, *, p < 0.05 t test). Although PU.1 was not identified in the LPS SILAC data set, increased expression was also observed via Western blotting (n = 3, **, p < 0.01 t test). 2073
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speculate that ethanol could induce this type of alternative activation phenotype in microglia where Myc could be an important molecular component of this process. Future work is needed to investigate M2 polarization markers such as Arg1, Fizz1 and Ym1 and secretion of anti-inflammatory cytokines in microglia after ethanol exposure at various time points. After further analysis of predicted activities of other transcription factors in ethanol-treated microglia, we identified potential inhibition of p53 activity without significant change in total p53 expression. p53 normally responds to cell stress by regulating the expression of genes associated with pathways such as cell cycle arrest, apoptosis, or metabolism. Moreover, it has recently been shown that p53 is important in regulating microglial activation where p53 deficiency induces markers and functional characteristics associated with alternative activation.86 We then set out to validate this finding and observed experimentally that p53 actually shows an increase in acetylation (lysines 373/382) as shown in both ethanol and LPS-treated microglia (Figure 9) as well as increased mRNA
On the other hand, treatment of microglial cells for 12 h with 5−100 mM ethanol did not have an effect on the induction of the gene expression of cytokines TNFα and IL-6 (Figure 8). In
Figure 8. Cytokine gene expression in ethanol and LPS-treated microglia. Cells were treated for 12 h with indicated concentrations of ethanol. The gene expression levels of TNFα and IL-6 were determined with real-time qPCR analysis, normalized against that of 18S rRNA and expressed as fold changes over control. **, p < 0.005 compared to control cells (0) (n = 3, ANOVA analysis with Bonferroni post hoc analysis).
contrast, LPS was a potent inducer of the gene expression of the two cytokines (Figure 8). Collectively, the measurement of pro-inflammatory cytokine production and iNOS expression indicate that ethanol-treated microglia are devoid of a classical activation response in vitro; however, these cells exhibit protein expression changes of known markers associated with partial activation (homing stage) as demonstrated by the SILAC data and biochemical validation. As stated previously, our observations are consistent with the previously shown importance of TLRs in microglial ethanol response63−65 and suppression of iNOS expression through the NF-kappa-B pathway that has been linked to the action of the histone acetyltransferase p300 in ethanol-treated microglia.21 One hypothesis suggested for ethanol-mediated NF-kappa-B suppression in microglia is that p300 is a rate-limiting factor as coactivator of NF-kappa-B transactivation, where competition with other ethanol-induced transcription factors that require p300 could occur.21 After IPA analysis to predict upstream regulators in ethanol-treated microglia (considering a p-value of overlap
