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Qinghua Sun, P. Yue, J. Deiuliis, C. Lumeng, Thomas Kampfrath, M. Mikolaj, Ying Cai, Michael Ostrowski, Bo Lu, S. Parthasarathy, R. Brook, S. Moffatt-Bruce, Lung-Chi Chen, S. Rajagopalan (2009)
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Abstract Previous studies supported a role of hypothalamic inflammation in fine ambient particulate matter (PM2.5) exposure-mediated diabetes development. We therefore investigated the effects of PM2.5 exposure on insulin resistance and the disorders of hepatic glucose and lipid metabolism via hypothalamic inflammation. KKAy mice, a genetically susceptible model of type II diabetes mellitus, were administered intra-cerebroventricularly with IKK2 inhibitor (IMD-0354) and were exposed to either concentrated PM2.5 or filtered air (FA) for 4 weeks simultaneously via a versatile aerosol concentration exposure system. At the end of the exposure, fasting blood glucose and serum insulin were evaluated before epididymal adipose tissue and liver were collected, flow cytometry, quantitative PCR and Western blot were performed at euthanasia. We observed that intracerebroventricular administration of IMD-0354 attenuated insulin resistance, inhibited macrophage polarization to M1 phenotype in epididymal adipose tissue in response to PM2.5 exposure. Although the treatment did not affect hepatic inflammation or endoplasmic reticulum stress, it inhibited the expression of the enzymes for gluconeogenesis and lipogenesis in the liver. Therefore, our current finding indicates an important role of hypothalamic inflammation in PM2.5 exposure-mediated hepatic glucose and lipid metabolism disorder. particulate matter, insulin resistance, gluconeogenesis, lipogenesis, hypothalamic inflammation, IKK2 According to the data from the Global Burden of Diseases Study 2015, exposure to ambient fine particulate matter particles (<2.5 μm in aerodynamic diameter, PM2.5) was the fifth-highest mortality risk factor in 2015 (Hayashi et al., 2007). Emerging evidence from both epidemiological and experimental studies indicates the adverse consequences of PM2.5 exposure on diabetes, including worsening of whole-body insulin sensitivity, glucose tolerance impairment, lipid accumulation, and glucose metabolism dysfunction (Hwang et al., 2014; Rajagopalan and Brook, 2012; Sun et al., 2009). As a critical target organ of insulin, liver pathogenesis in response to PM2.5 is important to consider. PM2.5 exposure leads to hepatic insulin resistance (IR) that was accompanied by endoplasmic reticulum (ER) stress-induced apoptosis (Laing et al., 2010), nonalcoholic steatohepatitis, impaired hepatic glucose metabolism (Zheng et al., 2012), SREBP1c-mediated transcriptional programming, and lipogenesis in the liver (Liu et al., 2014c). Classically, IR is a consequence of chronic inflammatory signaling in several major organs and tissues, such as the liver, white and/or brown adipose tissues, skeletal muscle, and vascular systems (Gregor and Hotamisligil, 2011; Hotamisligil, 2006; Liu et al., 2014c). Recently, the role of inflammation in the central nervous system, particularly the hypothalamus in diet-induced IR progress, has been noted in rodent models and human (Posey et al., 2009; Thaler et al., 2012). Our previous study demonstrated that PM2.5 exposure led to hyperglycemia and IR, which were accompanied by hypothalamic inflammation evidenced by increased mRNA levels of Interleukin-6 (IL-6), tumor necrosis factor α (TNFα), Inhibitor kappa B kinase 2 (IKK2), and enhanced microglial/astrocyte reactivity (Song et al., 2017). The inhibition of hypothalamic inflammation by intracerebroventricular (ICV) administration of IKK2 inhibitor (IMD-0354) rectified PM2.5-induced glucose intolerance, IR, energy metabolism dysfunction, and attenuated peripheral inflammation in response to PM2.5 exposure (Song et al., 2017). Whether central inhibition of IKK2 could reverse the dysfunction of glucose and lipid metabolism remains unknown. We therefore systematically investigated this issue in a genetic diabetic model subjected to air pollution exposure along with ICV treatment of IKK2 inhibitor. MATERIALS AND METHODS Animals and animal care KKAy mice of 7-week-old were purchased from Jackson Laboratories (Bar Harbor, Maine), which were maintained at 21°C on a 12-h light/12-h dark cycle with free access to water and food. The protocols and the use of animals were approved by and in accordance with the Ohio State University Animal Care and Use Committee. The animals were treated humanely and with regard to the alleviation of suffering. Ambient whole-body inhalational protocol and groups KKAy mice were treated ICV with IMD-0354 or vehicle Dimethyl sulfoxide (DMSO) and exposed to PM2.5 6 h/day, 5 days/week, for consecutive 4 weeks. Briefly, mice were exposed at the Polaris Facility (near roadway facility located within 250 m of a major interstate highway, Columbus, Ohio) in a concentrated exposure system. The chambers of the exposure system receive either concentrated PM2.5 directly from ambient air of Columbus site or filtered air (FA). There were 4 groups: FA and no IMD treatment (treatment with vehicle of DMSO, FA-VEH), PM2.5 and no IMD treatment (treatment with vehicle of DMSO, PM-VEH), FA and IMD treatment (ICV with IKK2 inhibitor IMD-0354, Sigma; FA-IMD), and PM2.5 and IMD treatment (ICV with IKK2 inhibitor IMD-0354, PM-IMD) (n = 8 for each group). Animal exposure and monitoring of the exposure environment and ambient aerosol were performed as previously described in Sun et al. (2009) and Xu et al. (2010). PM2.5 concentration measurement and element analysis To calculate the exposure mass concentrations of concentrated ambient PM2.5 in the exposure chambers, samples were collected on Teflon filters (PTFE, 37 mm, 2 µm pore; PALL Life Sciences, Ann Arbor, Michigan) and weighed before and after sampling in a temperature- and humidity-controlled weighing room using a Mettler Toledo Excellence Plus XP microbalance. Weight gains were used to calculate the exposure concentrations during the corresponding time period. Major elemental constituents were measured with inductively coupled plasma mass spectrometry (ICP-MS, ELEMENT2, ThermoFinnigan, San Jose, California). ICV drug infusion A stereotaxic apparatus was used to implant a cannula into the right lateral ventricle of the mice that were anesthetized with 2% isoflurane in air. Cannula (Plastics One, Roanoke, Virginia) positions were +0.02 posterior and −0.95 lateral to Bregma and extended 2.75 mm below the skull. The cannula was connected via tubing to an Alzet minipump (Model 1004, Durect, Cupertino, California) that was implanted subcutaneously in the scapular region and delivered IMD-0354 or the vehicle, both at a rate of 0.11 μl/h. The minipumps were implanted 1 day prior to the initiation of either PM2.5 or FA exposure. The IMD-0354-treated groups received a total of 600 ng of the inhibitor per day. Cannula placement was verified in the tissue via a cresylvilet stain. Measurements of blood glucose and insulin sensitivity Mice were fasted overnight directly prior to blood glucose and insulin measurements. A blood sample was collected from the vena caudalis and the blood glucose measurement was conducted with a Contour Blood Glucose Meter (Bayer, Mishawaka, IN). Insulin levels were determined using an Ultra Sensitive Mouse Insulin ELISA Kit (Crystal Chem Inc., Downers Grove, Illinois). Homeostasis model assessment for insulin resistance (HOMA-IR) was calculated based on 1 mg of insulin as equivalent to 24 IU, using the formula HOMA-IR = [fasting insulin concentration (ng/ml) × 24 × fasting glucose concentration (mg/dl)]/405 (Xu et al., 2010). Flow cytometric evaluation of inflammation in epididymal adipose tissues Epididymal adipose tissue from the mice was excised, minced, and digested with collagenase type II, and the stromal vascular fraction (SVF) was isolated as described previously. The SVF cells were centrifuged at 500 × g for 5 min. The resulting pellets were re-suspended in 1× red blood cell lysis buffer (Biolegend, San Diego, California), at room temperature for 3 min followed by addition of 1× PBS and centrifugation. Then, SVF cells were stained with antiCD11c, antiCD206, and antiF4/80, both followed by incubation at room temperature for 45 min. These antibodies were used to label M1 (F4/80+/CD11c+/CD206−) and M2 (F4/80+/CD11c−/CD206+) macrophages. The cells were subsequently washed with 1X PBS and resuspended in 1% neutral buffered formalin and run by flow cytometry (BD FACS LSR II flow cytometer, Becton Dickinson, San Jose, California). Data were analyzed using BD FACS Diva software (Becton Dickinson). All antibodies were purchased from Biolegend or BD Bioscience (Kampfrath et al., 2011; Zhong et al., 2013). Quantitative RT-PCR RT-PCR was performed using RNA extracted from the liver of the experimental mice. Total RNA was extracted with RNAiso Plus (TaKaRa, Shigo, Japan) using a homogenizer (IKA Works, Wilmington, North Carolina) according to manufacturer’s instructions. RNA was then reverse transcribed into cDNA with High Capacity cDNA Transcription kit (Invitrogen, Carlsbad, California). Gene expression for the genes of interest were determined in duplicate using the QuantStudioQ7 (Applied Biosystems).Relative gene expression of individual samples was calculated using the ΔCt method relative to β-actin. The sequences of all primers used are listed in Table 1. Table 1. Primers Used for Real-Time PCR Primer Forward Oligonucleotides Reverse Oligonucleotides PEPCK CCACAGCTGGTGCAGAACA GAAGGGTCGATGGCAAA FBPase AGGAAGCACAAAGCCAAGTGAAGG TGAGGATGAAGTGACCTTGGGCAT G6Pase CCATGCAAAGGACTAGGAACAA TACCAGGGCCGATGTCAAC PC GATGACCTCACAGCCAAGCA GGGTACCTCTGTGTCCAAAGGA CEBP/ɑ CAAGAACAGCAACGAGTACCG TCACGGCTCAGCTGTTCCAC GK GAAGACCTGAAGAAGGTGATGAGC GTCTATGTCTTCGTGCCTTACAGG LPK ACAGGGTTTTTGCATTCCTG TTGGTTCTTTCGAACCTTG ACL GCCAGCGGGAGCACATC CTTTGCAGGTGCCACTTCATC ACC1 AAGGCTATGTGAAGGATG CTGTCTGAAGAGGTTAGG ACC2 CTTGCTTCTCTTTCTGACTTG GGCTTCCACCTTACTGTTG FAS GGGTTCTAGCCAGCAGAGTC TCAGCCACTTGAGTGTCCTC DGAT1 TGGTGTGTGGTGATGCTGATC GCCAGGCGCTTCTCAA DGAT2 AGTGGCAATGCTATCATCATCGT TCTTCTGGACCCATCGGCCCCAGGA SCD1 GTCAGGAGGGCAGGTTTC GAGCGTGGACTTCGGTTC GPAT CAACACCATCCCCGACATC GTGACCTTCGATTATGCGATCA SREBP1 GGATCGCAGTCTGAGGA CGACAGGAAGGCAGGG HSL TGTGGCACAGACCTCTAAAT GGCATATCCGCTCTC LPL CTCAGATGCCCTACAAAGTGTTCC TCTCGAAGGCCTGGTTGTGT ATGL GGAGACCAAGTGGAACATCTCA AATAATGTTGGCACCTGCTTCA IL-1β TGGGCTGGACTGTTTCTAATG GTTGACAGCTAGGTTCTGTTCT IKKβ CCAAGAGACCAAAGGACAGAA CTGGAAGGCTGGGACATTAG F4/80 TGTCTGACAATTGGGATCTGCCCT TTGCATGTTCAGGGCAAACGTCTC TNFɑ CATCTTCTCAAAACTCGAGTGACAA TGGGAGTAGATAAGGTACAGCCC IL-6 GTGGCTAAGGACCAAGACCA GGTTTGCCGAGTAGACCTCA ATF4 ATGGCCGGCTATGGATGAT CGAAGTCAAACTCTTTCAGATCCATT ATF6 GTGACCTGTAGCTCTGTCATAAG CCTTTCGGACTCTGGGAATATC Xbp1 GAGTCCGCAGCAGGTG GTGTCAGAGTCCATGGGA Bip TTCTGCCATGGTTCTCACTAAA TGTTCTTCTCTCCCTCTCTCTT CHOP CTGCCTTTCACCTTGGAGAC CGTTTCCTGGGGATGAGATA GRP94 AATAGAAAGAATGCTTCGCC TCTTCAGGCTCTTCTTCTGG β-actin TGTGATGGTGGGAATGGGTCAGAA TGTGGTGCCAGATCTTCTCCATGT Primer Forward Oligonucleotides Reverse Oligonucleotides PEPCK CCACAGCTGGTGCAGAACA GAAGGGTCGATGGCAAA FBPase AGGAAGCACAAAGCCAAGTGAAGG TGAGGATGAAGTGACCTTGGGCAT G6Pase CCATGCAAAGGACTAGGAACAA TACCAGGGCCGATGTCAAC PC GATGACCTCACAGCCAAGCA GGGTACCTCTGTGTCCAAAGGA CEBP/ɑ CAAGAACAGCAACGAGTACCG TCACGGCTCAGCTGTTCCAC GK GAAGACCTGAAGAAGGTGATGAGC GTCTATGTCTTCGTGCCTTACAGG LPK ACAGGGTTTTTGCATTCCTG TTGGTTCTTTCGAACCTTG ACL GCCAGCGGGAGCACATC CTTTGCAGGTGCCACTTCATC ACC1 AAGGCTATGTGAAGGATG CTGTCTGAAGAGGTTAGG ACC2 CTTGCTTCTCTTTCTGACTTG GGCTTCCACCTTACTGTTG FAS GGGTTCTAGCCAGCAGAGTC TCAGCCACTTGAGTGTCCTC DGAT1 TGGTGTGTGGTGATGCTGATC GCCAGGCGCTTCTCAA DGAT2 AGTGGCAATGCTATCATCATCGT TCTTCTGGACCCATCGGCCCCAGGA SCD1 GTCAGGAGGGCAGGTTTC GAGCGTGGACTTCGGTTC GPAT CAACACCATCCCCGACATC GTGACCTTCGATTATGCGATCA SREBP1 GGATCGCAGTCTGAGGA CGACAGGAAGGCAGGG HSL TGTGGCACAGACCTCTAAAT GGCATATCCGCTCTC LPL CTCAGATGCCCTACAAAGTGTTCC TCTCGAAGGCCTGGTTGTGT ATGL GGAGACCAAGTGGAACATCTCA AATAATGTTGGCACCTGCTTCA IL-1β TGGGCTGGACTGTTTCTAATG GTTGACAGCTAGGTTCTGTTCT IKKβ CCAAGAGACCAAAGGACAGAA CTGGAAGGCTGGGACATTAG F4/80 TGTCTGACAATTGGGATCTGCCCT TTGCATGTTCAGGGCAAACGTCTC TNFɑ CATCTTCTCAAAACTCGAGTGACAA TGGGAGTAGATAAGGTACAGCCC IL-6 GTGGCTAAGGACCAAGACCA GGTTTGCCGAGTAGACCTCA ATF4 ATGGCCGGCTATGGATGAT CGAAGTCAAACTCTTTCAGATCCATT ATF6 GTGACCTGTAGCTCTGTCATAAG CCTTTCGGACTCTGGGAATATC Xbp1 GAGTCCGCAGCAGGTG GTGTCAGAGTCCATGGGA Bip TTCTGCCATGGTTCTCACTAAA TGTTCTTCTCTCCCTCTCTCTT CHOP CTGCCTTTCACCTTGGAGAC CGTTTCCTGGGGATGAGATA GRP94 AATAGAAAGAATGCTTCGCC TCTTCAGGCTCTTCTTCTGG β-actin TGTGATGGTGGGAATGGGTCAGAA TGTGGTGCCAGATCTTCTCCATGT Table 1. Primers Used for Real-Time PCR Primer Forward Oligonucleotides Reverse Oligonucleotides PEPCK CCACAGCTGGTGCAGAACA GAAGGGTCGATGGCAAA FBPase AGGAAGCACAAAGCCAAGTGAAGG TGAGGATGAAGTGACCTTGGGCAT G6Pase CCATGCAAAGGACTAGGAACAA TACCAGGGCCGATGTCAAC PC GATGACCTCACAGCCAAGCA GGGTACCTCTGTGTCCAAAGGA CEBP/ɑ CAAGAACAGCAACGAGTACCG TCACGGCTCAGCTGTTCCAC GK GAAGACCTGAAGAAGGTGATGAGC GTCTATGTCTTCGTGCCTTACAGG LPK ACAGGGTTTTTGCATTCCTG TTGGTTCTTTCGAACCTTG ACL GCCAGCGGGAGCACATC CTTTGCAGGTGCCACTTCATC ACC1 AAGGCTATGTGAAGGATG CTGTCTGAAGAGGTTAGG ACC2 CTTGCTTCTCTTTCTGACTTG GGCTTCCACCTTACTGTTG FAS GGGTTCTAGCCAGCAGAGTC TCAGCCACTTGAGTGTCCTC DGAT1 TGGTGTGTGGTGATGCTGATC GCCAGGCGCTTCTCAA DGAT2 AGTGGCAATGCTATCATCATCGT TCTTCTGGACCCATCGGCCCCAGGA SCD1 GTCAGGAGGGCAGGTTTC GAGCGTGGACTTCGGTTC GPAT CAACACCATCCCCGACATC GTGACCTTCGATTATGCGATCA SREBP1 GGATCGCAGTCTGAGGA CGACAGGAAGGCAGGG HSL TGTGGCACAGACCTCTAAAT GGCATATCCGCTCTC LPL CTCAGATGCCCTACAAAGTGTTCC TCTCGAAGGCCTGGTTGTGT ATGL GGAGACCAAGTGGAACATCTCA AATAATGTTGGCACCTGCTTCA IL-1β TGGGCTGGACTGTTTCTAATG GTTGACAGCTAGGTTCTGTTCT IKKβ CCAAGAGACCAAAGGACAGAA CTGGAAGGCTGGGACATTAG F4/80 TGTCTGACAATTGGGATCTGCCCT TTGCATGTTCAGGGCAAACGTCTC TNFɑ CATCTTCTCAAAACTCGAGTGACAA TGGGAGTAGATAAGGTACAGCCC IL-6 GTGGCTAAGGACCAAGACCA GGTTTGCCGAGTAGACCTCA ATF4 ATGGCCGGCTATGGATGAT CGAAGTCAAACTCTTTCAGATCCATT ATF6 GTGACCTGTAGCTCTGTCATAAG CCTTTCGGACTCTGGGAATATC Xbp1 GAGTCCGCAGCAGGTG GTGTCAGAGTCCATGGGA Bip TTCTGCCATGGTTCTCACTAAA TGTTCTTCTCTCCCTCTCTCTT CHOP CTGCCTTTCACCTTGGAGAC CGTTTCCTGGGGATGAGATA GRP94 AATAGAAAGAATGCTTCGCC TCTTCAGGCTCTTCTTCTGG β-actin TGTGATGGTGGGAATGGGTCAGAA TGTGGTGCCAGATCTTCTCCATGT Primer Forward Oligonucleotides Reverse Oligonucleotides PEPCK CCACAGCTGGTGCAGAACA GAAGGGTCGATGGCAAA FBPase AGGAAGCACAAAGCCAAGTGAAGG TGAGGATGAAGTGACCTTGGGCAT G6Pase CCATGCAAAGGACTAGGAACAA TACCAGGGCCGATGTCAAC PC GATGACCTCACAGCCAAGCA GGGTACCTCTGTGTCCAAAGGA CEBP/ɑ CAAGAACAGCAACGAGTACCG TCACGGCTCAGCTGTTCCAC GK GAAGACCTGAAGAAGGTGATGAGC GTCTATGTCTTCGTGCCTTACAGG LPK ACAGGGTTTTTGCATTCCTG TTGGTTCTTTCGAACCTTG ACL GCCAGCGGGAGCACATC CTTTGCAGGTGCCACTTCATC ACC1 AAGGCTATGTGAAGGATG CTGTCTGAAGAGGTTAGG ACC2 CTTGCTTCTCTTTCTGACTTG GGCTTCCACCTTACTGTTG FAS GGGTTCTAGCCAGCAGAGTC TCAGCCACTTGAGTGTCCTC DGAT1 TGGTGTGTGGTGATGCTGATC GCCAGGCGCTTCTCAA DGAT2 AGTGGCAATGCTATCATCATCGT TCTTCTGGACCCATCGGCCCCAGGA SCD1 GTCAGGAGGGCAGGTTTC GAGCGTGGACTTCGGTTC GPAT CAACACCATCCCCGACATC GTGACCTTCGATTATGCGATCA SREBP1 GGATCGCAGTCTGAGGA CGACAGGAAGGCAGGG HSL TGTGGCACAGACCTCTAAAT GGCATATCCGCTCTC LPL CTCAGATGCCCTACAAAGTGTTCC TCTCGAAGGCCTGGTTGTGT ATGL GGAGACCAAGTGGAACATCTCA AATAATGTTGGCACCTGCTTCA IL-1β TGGGCTGGACTGTTTCTAATG GTTGACAGCTAGGTTCTGTTCT IKKβ CCAAGAGACCAAAGGACAGAA CTGGAAGGCTGGGACATTAG F4/80 TGTCTGACAATTGGGATCTGCCCT TTGCATGTTCAGGGCAAACGTCTC TNFɑ CATCTTCTCAAAACTCGAGTGACAA TGGGAGTAGATAAGGTACAGCCC IL-6 GTGGCTAAGGACCAAGACCA GGTTTGCCGAGTAGACCTCA ATF4 ATGGCCGGCTATGGATGAT CGAAGTCAAACTCTTTCAGATCCATT ATF6 GTGACCTGTAGCTCTGTCATAAG CCTTTCGGACTCTGGGAATATC Xbp1 GAGTCCGCAGCAGGTG GTGTCAGAGTCCATGGGA Bip TTCTGCCATGGTTCTCACTAAA TGTTCTTCTCTCCCTCTCTCTT CHOP CTGCCTTTCACCTTGGAGAC CGTTTCCTGGGGATGAGATA GRP94 AATAGAAAGAATGCTTCGCC TCTTCAGGCTCTTCTTCTGG β-actin TGTGATGGTGGGAATGGGTCAGAA TGTGGTGCCAGATCTTCTCCATGT Western blotting Liver tissues were homogenized with RIPA total protein extraction lysis buffer (Bioworld Technology, Louis Park, MN) on ice. Equal quantities (100 µg) of tissue protein were separated by 10% SDS-PAGE, and then transferred to immobilon-P polyvinylidenedifluoride membrane (Bio-Rad, Hercules, California). The membranes were immunoblotted with different primary antibodies followed by incubation with horseradish peroxidase-conjugated corresponding secondary antibodies. The primary antibodies included P-Akt, Akt (Cell Signaling Technology), FBPase, G6Pase, and phosphoenolpyruvate carboxy kinase (PEPCK) (Santa Cruz). The bands were visualized with enhanced chemiluminescence, and the autoradiograph was quantitated by densitometric analysis with ImageJ software. Beta-actin or tubulin was used as a loading control reference. Data analysis Data are expressed as means ± SEM unless otherwise indicated. For the analysis, 2-way ANOVA followed by stratified analysis was used with PM2.5 exposure, treatment group, and PM2.5 × treatment interaction as independent variables. A p-value of < .05 was deemed statistically significant. The analyses were performed using Graphpad Prism software (Version 5). RESULTS PM2.5 Concentration and Compositional Assessment As shown in Figure 1, ambient mean daily PM2.5 concentration at the study site was 6.26 ± 1.1 µg/m3. Mean concentration of PM2.5 in the exposure chamber was 73.6 ± 21.0 µg/m3, which represents 11.8-fold concentration of the ambient levels. Mean concentration of PM2.5 in the FA chamber was 2.7 ± 0.4 µg/m3. The major composition of PM2.5 included nonmetals (S), alkaline earth metals (Ca and Mg), alkali metals (K and Na), transition metals (Fe and Zn), and poor metals (Al), which can be found in Table 2. Table 2. Elemental Constituents During the Exposure Period by ICP-MS Elements FA Ambient PM2.5 Concentrated PM2.5 Mean SD Mean SD Mean SD S32 39.80 10.93 641.40 412.66 4812.33 3568.81 Ca44 97.69 21.68 104.30 27.44 825.43 276.99 Fe57 5.92 0.91 62.06 26.94 488.22 208.13 K39 31.39 10.74 56.63 22.39 377.02 159.94 Na23 72.80 23.18 47.62 17.67 295.14 118.79 Mg24 6.70 1.15 17.81 6.02 159.94 54.84 Zn66 4.36 2.38 19.20 9.90 144.56 77.18 Al27 20.21 5.71 16.99 7.26 144.31 27.47 P31 2.15 0.51 5.72 2.18 46.28 17.96 Ba137 0.61 0.06 4.45 1.82 38.94 16.61 Cu63 3.22 3.99 4.19 1.64 35.76 12.19 Pb208 2.39 0.48 3.36 1.48 22.14 11.15 Mn55 0.25 0.07 2.49 1.04 19.53 7.89 Cr52 8.18 0.89 3.84 0.48 15.81 1.92 Ti47 0.08 0.04 1.19 0.53 9.75 4.14 Se77 0.00 0.02 0.76 0.46 6.76 3.79 Sb123 0.05 0.01 0.82 0.42 6.54 3.46 Sr88 0.13 0.02 0.72 0.28 5.72 1.80 As75 0.03 0.01 0.74 0.52 5.36 4.11 Mo95 0.23 0.06 0.50 0.29 3.44 2.15 Ni60 0.22 0.06 0.43 0.31 2.79 1.28 V51 0.02 0.00 0.18 0.18 1.40 1.47 Cd111 0.36 0.08 0.23 0.08 1.39 0.53 Rb85 0.05 0.01 0.08 0.04 0.62 0.34 Ce140 0.01 0.00 0.04 0.02 0.33 0.17 Co59 0.05 0.01 0.04 0.02 0.33 0.05 La139 0.00 0.00 0.03 0.01 0.22 0.09 Elements FA Ambient PM2.5 Concentrated PM2.5 Mean SD Mean SD Mean SD S32 39.80 10.93 641.40 412.66 4812.33 3568.81 Ca44 97.69 21.68 104.30 27.44 825.43 276.99 Fe57 5.92 0.91 62.06 26.94 488.22 208.13 K39 31.39 10.74 56.63 22.39 377.02 159.94 Na23 72.80 23.18 47.62 17.67 295.14 118.79 Mg24 6.70 1.15 17.81 6.02 159.94 54.84 Zn66 4.36 2.38 19.20 9.90 144.56 77.18 Al27 20.21 5.71 16.99 7.26 144.31 27.47 P31 2.15 0.51 5.72 2.18 46.28 17.96 Ba137 0.61 0.06 4.45 1.82 38.94 16.61 Cu63 3.22 3.99 4.19 1.64 35.76 12.19 Pb208 2.39 0.48 3.36 1.48 22.14 11.15 Mn55 0.25 0.07 2.49 1.04 19.53 7.89 Cr52 8.18 0.89 3.84 0.48 15.81 1.92 Ti47 0.08 0.04 1.19 0.53 9.75 4.14 Se77 0.00 0.02 0.76 0.46 6.76 3.79 Sb123 0.05 0.01 0.82 0.42 6.54 3.46 Sr88 0.13 0.02 0.72 0.28 5.72 1.80 As75 0.03 0.01 0.74 0.52 5.36 4.11 Mo95 0.23 0.06 0.50 0.29 3.44 2.15 Ni60 0.22 0.06 0.43 0.31 2.79 1.28 V51 0.02 0.00 0.18 0.18 1.40 1.47 Cd111 0.36 0.08 0.23 0.08 1.39 0.53 Rb85 0.05 0.01 0.08 0.04 0.62 0.34 Ce140 0.01 0.00 0.04 0.02 0.33 0.17 Co59 0.05 0.01 0.04 0.02 0.33 0.05 La139 0.00 0.00 0.03 0.01 0.22 0.09 Concentration unit, ng/mg. Table 2. Elemental Constituents During the Exposure Period by ICP-MS Elements FA Ambient PM2.5 Concentrated PM2.5 Mean SD Mean SD Mean SD S32 39.80 10.93 641.40 412.66 4812.33 3568.81 Ca44 97.69 21.68 104.30 27.44 825.43 276.99 Fe57 5.92 0.91 62.06 26.94 488.22 208.13 K39 31.39 10.74 56.63 22.39 377.02 159.94 Na23 72.80 23.18 47.62 17.67 295.14 118.79 Mg24 6.70 1.15 17.81 6.02 159.94 54.84 Zn66 4.36 2.38 19.20 9.90 144.56 77.18 Al27 20.21 5.71 16.99 7.26 144.31 27.47 P31 2.15 0.51 5.72 2.18 46.28 17.96 Ba137 0.61 0.06 4.45 1.82 38.94 16.61 Cu63 3.22 3.99 4.19 1.64 35.76 12.19 Pb208 2.39 0.48 3.36 1.48 22.14 11.15 Mn55 0.25 0.07 2.49 1.04 19.53 7.89 Cr52 8.18 0.89 3.84 0.48 15.81 1.92 Ti47 0.08 0.04 1.19 0.53 9.75 4.14 Se77 0.00 0.02 0.76 0.46 6.76 3.79 Sb123 0.05 0.01 0.82 0.42 6.54 3.46 Sr88 0.13 0.02 0.72 0.28 5.72 1.80 As75 0.03 0.01 0.74 0.52 5.36 4.11 Mo95 0.23 0.06 0.50 0.29 3.44 2.15 Ni60 0.22 0.06 0.43 0.31 2.79 1.28 V51 0.02 0.00 0.18 0.18 1.40 1.47 Cd111 0.36 0.08 0.23 0.08 1.39 0.53 Rb85 0.05 0.01 0.08 0.04 0.62 0.34 Ce140 0.01 0.00 0.04 0.02 0.33 0.17 Co59 0.05 0.01 0.04 0.02 0.33 0.05 La139 0.00 0.00 0.03 0.01 0.22 0.09 Elements FA Ambient PM2.5 Concentrated PM2.5 Mean SD Mean SD Mean SD S32 39.80 10.93 641.40 412.66 4812.33 3568.81 Ca44 97.69 21.68 104.30 27.44 825.43 276.99 Fe57 5.92 0.91 62.06 26.94 488.22 208.13 K39 31.39 10.74 56.63 22.39 377.02 159.94 Na23 72.80 23.18 47.62 17.67 295.14 118.79 Mg24 6.70 1.15 17.81 6.02 159.94 54.84 Zn66 4.36 2.38 19.20 9.90 144.56 77.18 Al27 20.21 5.71 16.99 7.26 144.31 27.47 P31 2.15 0.51 5.72 2.18 46.28 17.96 Ba137 0.61 0.06 4.45 1.82 38.94 16.61 Cu63 3.22 3.99 4.19 1.64 35.76 12.19 Pb208 2.39 0.48 3.36 1.48 22.14 11.15 Mn55 0.25 0.07 2.49 1.04 19.53 7.89 Cr52 8.18 0.89 3.84 0.48 15.81 1.92 Ti47 0.08 0.04 1.19 0.53 9.75 4.14 Se77 0.00 0.02 0.76 0.46 6.76 3.79 Sb123 0.05 0.01 0.82 0.42 6.54 3.46 Sr88 0.13 0.02 0.72 0.28 5.72 1.80 As75 0.03 0.01 0.74 0.52 5.36 4.11 Mo95 0.23 0.06 0.50 0.29 3.44 2.15 Ni60 0.22 0.06 0.43 0.31 2.79 1.28 V51 0.02 0.00 0.18 0.18 1.40 1.47 Cd111 0.36 0.08 0.23 0.08 1.39 0.53 Rb85 0.05 0.01 0.08 0.04 0.62 0.34 Ce140 0.01 0.00 0.04 0.02 0.33 0.17 Co59 0.05 0.01 0.04 0.02 0.33 0.05 La139 0.00 0.00 0.03 0.01 0.22 0.09 Concentration unit, ng/mg. Figure 1. View largeDownload slide PM2.5 concentrations at the study site during the exposure time period for the experimental groups of FA and concentrated PM2.5 (PM) with ambient air PM2.5 (AA) monitoring simultaneously. Figure 1. View largeDownload slide PM2.5 concentrations at the study site during the exposure time period for the experimental groups of FA and concentrated PM2.5 (PM) with ambient air PM2.5 (AA) monitoring simultaneously. Effect of PM2.5 Exposure on IR in IMD-0354-Treated Mice There was no significant difference in blood glucose between the groups prior to the assignment to exposure protocols (Figure 2A). After 4 weeks of PM2.5 exposure, PM-VEH displayed elevated fasting blood glucose, circulating insulin levels, and HOMA-IR index (Figs. 2A–C). The changes induced by PM2.5 were prevented by central IKK2 inhibition. Consistent with the metabolic changes, phosphorylated AKT (p-AKT, Ser473) was reduced in the liver of PM-VEH mice compared with FA-VEH mice, but this was not observed in the PM2.5-exposed mice treated with IKK2 inhibitor (Figure 2D). These results suggest that IKK2 treatment is protective from PM2.5-exaggerated abnormalities in blood glucose and insulin sensitivity. Figure 2. View largeDownload slide Effects of IMD-0354 ICV infusion on systemic and IR in the KKAy mice of FA without IMD (FA-VEH), FA with IMD (FA-IMD), PM2.5 without IMD (PM-VEH), and PM2.5 with IMD (PM-IMD). A–C, Blood glucose, insulin and HOMA-IR after 6-hour fasting at the end of 4-week (wk) PM2.5 exposure (n = 8) D, Western blotting of P-AKT/total AKT in the liver (n = 5). *p < .05, ***p < .001 when compared PM-VEH group with FA-VEH group. #p < .05, ##p < .01, ###p < .001 when compared PM-IMD group with PM-VEH group. Figure 2. View largeDownload slide Effects of IMD-0354 ICV infusion on systemic and IR in the KKAy mice of FA without IMD (FA-VEH), FA with IMD (FA-IMD), PM2.5 without IMD (PM-VEH), and PM2.5 with IMD (PM-IMD). A–C, Blood glucose, insulin and HOMA-IR after 6-hour fasting at the end of 4-week (wk) PM2.5 exposure (n = 8) D, Western blotting of P-AKT/total AKT in the liver (n = 5). *p < .05, ***p < .001 when compared PM-VEH group with FA-VEH group. #p < .05, ##p < .01, ###p < .001 when compared PM-IMD group with PM-VEH group. Effect of PM2.5 Exposure on Body and Organ Weights in IMD-0354-Treated Mice After 4 weeks of PM2.5 exposure, mice exhibited a slight increase in body weight, which did not differ significantly between mice with central IMD-0354 treatment (Figure 3A). However, epididymal adipose mass was significantly increased in PM-exposed mice and this increase was prevented by IKK2 inhibition (Figure 3C), even when correcting for body mass (Figure 3D). Interestingly, IKK2 inhibition increased the weight of interscapular adipose tissue of the mice exposed to PM2.5, but showed no effect on that of the mice exposed to FA (Figs. 3E and 3F). There was no significant difference between the groups in liver weight (Figure 3B). Figure 3. View largeDownload slide Effects of IMD-0354 ICV infusion on the weight of body, organ, and tissue in the KKAy mice of FA without IMD (FA-VEH), FA with IMD (FA-IMD), PM2.5 without IMD (PM-VEH), and PM2.5 with IMD (PM-IMD). A and B, Body and liver weights of the mice at the end of 4-week PM2.5 exposure. epididymal adipose mass (C), interscapular adipose mass (E), and the percentage of adipose mass to body weight (D, F) at the end of 4-week PM2.5 exposure. *p < .05 when compared PM-VEH group with FA-VEH group, #p < .05, ##p < .01 when compared PM-IMD group with PM-VEH group (n = 8). Figure 3. View largeDownload slide Effects of IMD-0354 ICV infusion on the weight of body, organ, and tissue in the KKAy mice of FA without IMD (FA-VEH), FA with IMD (FA-IMD), PM2.5 without IMD (PM-VEH), and PM2.5 with IMD (PM-IMD). A and B, Body and liver weights of the mice at the end of 4-week PM2.5 exposure. epididymal adipose mass (C), interscapular adipose mass (E), and the percentage of adipose mass to body weight (D, F) at the end of 4-week PM2.5 exposure. *p < .05 when compared PM-VEH group with FA-VEH group, #p < .05, ##p < .01 when compared PM-IMD group with PM-VEH group (n = 8). IKK2 Inhibition Modulates Adipose Inflammation in Response to PM2.5 F4/80+ is widely used to label adipose tissue macrophages (ATMs) with F4/80+/CD11c+/CD206− and F4/80+/CD11c-/CD206+ as markers of M1 and M2 macrophages, respectively. As shown in Figure 4B, there was no significant difference between the groups in F4/80+ cells in the epididymal adipose tissue. Figures 4A and 4C depict a PM2.5-induced increase in the population of CD11c+/CD206− accompanied by a decrease in the population of CD11c−/CD206−. The increased population of CD11c+/CD206− was completely blocked by central IKK2 inhibition. However, we observed no difference between the groups in M2 cells (CD11c−/CD206+cells) or double positive (CD11c+/CD206+) cells from epididymal adipose tissue (Figure 4C).These results suggest that although PM2.5 exposure did not alter the amount of macrophages, it induced macrophages polarization to M1 phenotypes which were partially suppressed by central IKK2 inhibition. Figure 4. View largeDownload slide Effect of IMD-0354 ICV infusion on the inflammation in the epididymal adipose tissue in the KKAy mice of FA without IMD (FA-VEH), FA with IMD (FA-IMD), PM2.5 without IMD (PM-VEH), and PM2.5 with IMD (PM-IMD). A, Representative flow cytometric dot plots showing macrophages from epididymal adipose tissue at the end of 4-week PM2.5 exposure. B, The percentage of F4/80+over live cells. C, The percentage of macrophages with different markers over F4/80+cells. *p < .05 when compared PM-VEH group with FA-VEH group, #p < .05 when compared PM-IMD group with PM-VEH group (n = 6–8). Figure 4. View largeDownload slide Effect of IMD-0354 ICV infusion on the inflammation in the epididymal adipose tissue in the KKAy mice of FA without IMD (FA-VEH), FA with IMD (FA-IMD), PM2.5 without IMD (PM-VEH), and PM2.5 with IMD (PM-IMD). A, Representative flow cytometric dot plots showing macrophages from epididymal adipose tissue at the end of 4-week PM2.5 exposure. B, The percentage of F4/80+over live cells. C, The percentage of macrophages with different markers over F4/80+cells. *p < .05 when compared PM-VEH group with FA-VEH group, #p < .05 when compared PM-IMD group with PM-VEH group (n = 6–8). IKK2 Inhibition Modulates Hepatic Glucose Metabolism in Response to PM2.5 To investigate the mechanisms regulating hyperglycemia in response to PM2.5, we examined the pathways involved in gluconeogenesis and glycolysis. Figure 5A shows PM2.5 upregulated the expression of rate-limiting enzymes involved in gluconeogenesis, including PEPCK, FBPase, G6pase, and pyruvate carboxylase (PC) at mRNA levels (Figure 5A). Although we did not observe increased protein levels of PEPCK, the upregulation of FBPase and G6Pase proteins was detected (Figs. 5A–C). Central IKK2 inhibition reduced protein but not gene levels of FBPase and G6Pase in the PM2.5-exposed mice (Figs. 5A–C). Interestingly, the expression of FBPase at the protein level was upregulated in response to central IKK2 inhibition treatment in the FA-exposed animals (Figs. 5B and 5C). In addition, we observed significant increase in the expression of key glycolytic enzymes, glucokinase (GK), and L-type pyruvate kinase (LPK) at the mRNA level, which were blocked by central IKK2 inhibition. These results suggest that enhanced gluconeogenesis likely contributes to hyperglycemia in response to PM2.5 exposure. Figure 5. View largeDownload slide Effect of IMD-0354 ICV infusion on gluconeogenesis in the liver in the KKAy mice of FA without IMD (FA-VEH), FA with IMD (FA-IMD), PM2.5 without IMD (PM-VEH), and PM2.5 with IMD (PM-IMD). A, mRNA levels for the enzymes of gluconeogenesis at the end of 4-week PM2.5 exposure (n = 6–8). B and C, Representative bands and analysis of protein levels for the enzymes of gluconeogenesis (n = 3–6). *p < .05, **p < .01, ***p < .001, when compared PM-VEH group with FA-VEH group, #p < .05, ##p < .01, when compared PM-IMD group with PM-VEH group (n = 6–8). Figure 5. View largeDownload slide Effect of IMD-0354 ICV infusion on gluconeogenesis in the liver in the KKAy mice of FA without IMD (FA-VEH), FA with IMD (FA-IMD), PM2.5 without IMD (PM-VEH), and PM2.5 with IMD (PM-IMD). A, mRNA levels for the enzymes of gluconeogenesis at the end of 4-week PM2.5 exposure (n = 6–8). B and C, Representative bands and analysis of protein levels for the enzymes of gluconeogenesis (n = 3–6). *p < .05, **p < .01, ***p < .001, when compared PM-VEH group with FA-VEH group, #p < .05, ##p < .01, when compared PM-IMD group with PM-VEH group (n = 6–8). IKK2 Inhibition Modulates Hepatic Lipid Metabolism in Response to PM2.5 We then examined gene involvement in lipid metabolism in the liver. The expression of key lipid synthesis enzymes, Acetone-cyanohydrin lyase (ACL), acetyl-CoA carboxylase 1 (ACC1), ACC2, fatty acid synthase (FAS), diacylglycerol acyl transferase (DGAT1), DGAT2, stearoyl-Coenzyme A desaturase 1 (SCD1), and Glycerol-3-phosphate 1-O-acyltransferase (GPAT), were all significantly increased in the liver of PM2.5-exposed mice compared with FA-exposed mice. Central IKK2 inhibition did not significantly reduce expression of these genes, although it partially normalized upregulation of ACL and SCD1 in response to PM2.5 exposure (Figure 6A). However, PM2.5 exposure significantly increased the expression of ACL and SCD1 at protein level, and this increase was attenuated by central IKK2 inhibition (Figs. 6B and 6C).There was a clear trend toward increase in SREBP1 expression, a key transcription factor involved in the activation of lipogenic genes (p = .06), which was not affected by IKK2 inhibitor treatment (Figure 6C).There were no significant differences between the groups in gene expression of the enzymes involved in lipolysis including HSL, LPL and ATGL. These results indicate that central IKK2 inhibition did correct the upregulation of enzymes for lipogenesis (ACL and SCD1) but not lipolysis, which were caused by PM2.5 exposure. Figure 6. View largeDownload slide Effect of IMD-0354 ICV infusion on lipid metabolism in the liver in the KKAy mice of FA without IMD (FA-VEH), FA with IMD (FA-IMD), PM2.5 without IMD (PM-VEH), and PM2.5 with IMD (PM-IMD). A, mRNA levels for the enzymes of lipogenesis at the end of 4-week PM2.5 exposure (n = 6). Representative bands (B) and analysis (C) of protein levels for the enzymes of lipogenesis (n = 3–6). D, mRNA levels for the enzymes of lipolysis (n = 6). *p < .05, **p < .01, ***p < .001, when compared PM-VEH group with FA-VEH group, ###p < .001, when compared PM-IMD group with PM-VEH group. Figure 6. View largeDownload slide Effect of IMD-0354 ICV infusion on lipid metabolism in the liver in the KKAy mice of FA without IMD (FA-VEH), FA with IMD (FA-IMD), PM2.5 without IMD (PM-VEH), and PM2.5 with IMD (PM-IMD). A, mRNA levels for the enzymes of lipogenesis at the end of 4-week PM2.5 exposure (n = 6). Representative bands (B) and analysis (C) of protein levels for the enzymes of lipogenesis (n = 3–6). D, mRNA levels for the enzymes of lipolysis (n = 6). *p < .05, **p < .01, ***p < .001, when compared PM-VEH group with FA-VEH group, ###p < .001, when compared PM-IMD group with PM-VEH group. Hepatic Inflammation and ER Stress in Response to PM2.5 Exposure Recent evidences suggest that inflammation and ER stress are induced by PM2.5 exposure (Laing et al., 2010; Zheng et al., 2012). To determine whether PM2.5 could induce inflammation or ER stress in the liver of diabetic mice, we first examined the expression of inflammatory genes including IL-1β, TNFα, IL-6, F4/80, and IKK2 and found no difference between the groups (Figure 7A). Next, we examined induction of ER stress markers Bip, GRP94, and markers of subsequent pathways including CHOP, xbp1, ATF4/ATF6 in the liver of mice. As shown in Figure 7B, PM2.5 exposure or central IKK2 inhibition induced no significant difference in these ER stress molecules in the KKAy mice. Figure 7. View largeDownload slide Effect of IMD-0354 ICV infusion on inflammation and ER stress in the liver in the KKAy mice of FA without IMD (FA-VEH), FA with IMD (FA-IMD), PM2.5 without IMD (PM-VEH), and PM2.5 with IMD (PM-IMD). A, mRNA levels for inflammatory genes in the liver at the end of 4-week PM2.5 exposure. B, mRNA levels for ER stress genes in the liver at the end of 4-week PM2.5 exposure (n = 6). Figure 7. View largeDownload slide Effect of IMD-0354 ICV infusion on inflammation and ER stress in the liver in the KKAy mice of FA without IMD (FA-VEH), FA with IMD (FA-IMD), PM2.5 without IMD (PM-VEH), and PM2.5 with IMD (PM-IMD). A, mRNA levels for inflammatory genes in the liver at the end of 4-week PM2.5 exposure. B, mRNA levels for ER stress genes in the liver at the end of 4-week PM2.5 exposure (n = 6). DISCUSSION In this study, we delineated the effects of central IKK2 inhibition on IR and hepatic glucose/lipid metabolism in a genetically susceptible model of type II diabetes mellitus. Cerebroventricular administration of IKK2 inhibitor mediated the following PM2.5-induced effects: (1) decreased fasting blood glucose levels, serum insulin levels and IR; (2) restored lipid accumulation and macrophages polarization in the epididymal adipose tissue depot; (3) attenuated gluconeogenesis, glycolysisand lipid synthesis in the liver. We have previously reported that PM2.5 exposure elevated inflammatory markers in multiple brain regions. For example, long term PM2.5 exposure elevated inflammatory markers in the hippocampus of C57BL/6 mice (Fonken et al., 2011). Furthermore, we found that PM2.5 exposure caused hypothalamic inflammation in KKAy mice, which was ameliorated by ICV administration of IKK2 inhibitor, IMD-0354 (Song et al., 2017).The central nervous system, and particularly the hypothalamus, plays a pivotal role in maintaining energy homeostasis. Indeed, the inhibition of central inflammation prevented the exaggeration of type II diabetes in the KKAy mice, which was evidenced by improved glucose tolerance and insulin sensitivity (Liu et al., 2014b). Although we did not measure the concentration of circulating IMD-0354 to rule out systemic absorption, low concentration of ICV administration of IMD-0354 in this study is about 1000-fold lower than that used for peripheral (Onai et al., 2004). Thus, it portrays a decreased likelihood for “spillover” to the systemic circulation and guarantees that the observed effects of ICV IMD-0354 in these studies from the inhibition of central inflammation. Akt phosphorylation in response to insulin is a well-known marker of insulin sensitivity. Although not significant, we noticed a light trend toward an increase in hepatic Akt phosphorylation upon central IMD treatment in the mice exposed to FA (FA-IMD compared with FA-VEH). Based on the role of hypothalamus inflammation in diet-induced obesity and energy dysfunction, the increased basic hepatic Akt phosphorylation could be due to the genetically modified diabetic model of KKAy mice used in this study, which showed severe obesity and metabolic dysfunction themselves. Importantly, p-AKT was reduced by PM2.5 exposure, which was reversed by central IMD treatment. However, examining Akt phosphorylation without insulin stimulation restrains us from drawing definite conclusion of the improvement in IR. Nevertheless, basal Akt phosphorylation was further supported by the insulin levels in serum and HOMA-IR in this study, indicating that IMD treatment is protective from PM2.5-exaggerated abnormalities in insulin sensitivity. Previous studies have shown that low-grade inflammation in peripheral tissues develops as a consequence of obesity. However, hypothalamic inflammatory signaling was evident in the rodents within 1–3 days of high fat diet onset, prior to the development of obesity (Thaler et al., 2012). This leads us to hypothesize the initial role of hypothalamic inflammation in PM2.5-induced peripheral response and IR. At the end of the experiment, we observed that central IMD-0354 treatment reduced adipose accumulation and M1 macrophages polarization in the epididymal adipose tissue in the KKAy mice exposed to PM2.5. These results suggest central IKK2 activation precedes and mediates PM2.5-induced polarization of M1 macrophages in visceral adipose tissue, contributing to the pathogenesis of diabetes in response to PM2.5 exposure. Due to multiple time interventions and technical restraints, we were unfortunately unable to examine hypothalamic and peripheral inflammation at early time points following PM2.5 exposure. Examining inflammatory markers in the first several days following PM2.5 exposure might support our assumption that PM2.5 inhalation induces central inflammation and subsequently regulates peripheral inflammation and IR. In addition, the adipose mass data were consistent with our previous set of exposure with KKAy mice exposed to PM2.5 for 5 weeks (Liu et al., 2014a). PM2.5 exposure increased epididymal adipose (known as white adipose tissue, WAT) mass, but showed no effect on the brown interscapular adipose mass (BAT). However, IKK2 inhibition decreased epididymal adipose mass and increased BAT mass. Interscapular BAT, an energy-expending tissue that produces heat, is associated with low body mass index, low total adipose tissue content and a lower risk of type 2 diabetes mellitus (Lidell et al., 2014). These results supported the theory again that WAT and BAT may be mutually regulated maintain energy balance. Taken together, our study does demonstrate the important role of hypothalamus via IKK2 in PM2.5-mediated diabetes development. This hypothesis merits further confirmation in other animal and clinical investigations. We have previously reported an important association between PM2.5 inhalation and abnormal insulin signaling in the liver (Liu et al., 2014c, 2017). One of the important components of this effect is CCR2-dependent recruitment and the activation of inflammation in the liver (Liu et al., 2014c), the other factor is the direct effect on hepatic glucose metabolism (Liu et al., 2017). The upregulation of several enzymes involved in triglyceride synthesis in response to PM2.5 exposure indicates that air pollution may induce lipid synthesis. However, only increased expressions of ACL and SCD1 were attenuated by hypothalamic IKK2 inhibition. In agreement with this study, the enhancement of the expression in rate-limiting enzymes for lipid synthesis in the PM2.5-exposed mice was abolished by the inhibition of inflammation (in the absence of CCR2, a well-known receptor regulating macrophage chemotaxis/inflammation) and accompanied by improved IR (Liu et al., 2014d). However, some of the PM2.5-induced upregulation of gene expression (FAS, DGAT1, DGAT2, and GPAT) was not alleviated by central IKK2 inhibition. These results indicate that in addition to being mediated by central inflammation, PM2.5-induced lipogenesis may be also due to the direct peripheral (instead of central) effects. SREBP1, especially SREBP1c is the transcription factor regulating the expression of rate-limiting enzyme for lipid synthesis. However, different from our previous work (Liu et al., 2014c), we observed no difference in the expression of SREBP1 or SREBP1c among those groups. This difference may be due to the different exposure duration and different animal models. C57BL/6 mice were exposed for 17 weeks (Liu et al., 2014c), but KKAy mice were exposed to PM2.5 for only consecutive 4 weeks in this study. This exposure period may not be long enough to increase SREBP or SREBP1c expression in diabetic animals in response to PM2.5. Unexpectedly, we observed no significant difference in the gene expression of inflammation or ER stress. Thus, although hypothalamic IKK2 inhibition did not affect the examined molecules for hepatic inflammation or ER stress, it did inhibit lipogenesis in the liver. Whether there is any other molecule of inflammation which could be alleviated by central IMD treatment awaits further study. Increased circulating glucose levels suggests there are enhancements in glucose production, which was confirmed by our previous studies (Liu et al., 2014c, 2017). This study supports these finding as PM2.5 upregulated the expression of gluconeogenesis enzymes, including PEPCK, G6Pase, FBPase, and PC at different levels of protein or mRNA. Interestingly, we found sharp upregulation of GK and LPK in response to PM2.5 exposure. Given the function of these genes in glucose decomposition, we assume a self-compensatory regulation to make up for the increased blood glucose induced by PM2.5 exposure. Strangely, we observed basal upregulation of PEPCK (mRNA level) and FBPase (protein level) in FA-IMD group, which could not be explained with the current data. Importantly, the glucose dysregulation induced by PM2.5 was effectively alleviated by the inhibition of hypothalamic inflammation. That is, IKK2 inhibition corrected blood glucose and improved IR in the KKAy mice. It is well known that hypothalamic inflammation disrupts key signaling pathways to affect the central control of blood pressure (Lebailly et al., 2015) and metabolism (Nguyen et al., 2013). Understanding the mechanisms underlying the detrimental effects of hypothalamic inflammation on peripheral organs remains incomplete. The hypothalamic-pituitary-adrenal axis and autonomic nervous systems are 2 pathways through which the hypothalamus exerts regulatory effects on peripheral responses (Deng et al., 2013). For example, Chida et al. provided scientific evidence to demonstrate the mechanism by which stress exacerbates liver diseases. The efferent sympathetic/adrenomedullary system mainly contributed to stress-induced exacerbation in liver diseases via catecholamines. In contrast, the efferent parasympathetic nervous system elicits inhibitory effects on the development of hepatic inflammation (Kohsaka et al., 2007). Furthermore, hypothalamic-parasympathetic circuits were identified as modulating lipid metabolism and hepatic function through inflammation and ER stress independent of changes in food intake or body weight (Prasai et al., 2013). Vagal denervation could be performed to assess its role in hypothalamic inflammation-mediated liver metabolism in the studies. In summary, this study demonstrates the intricate effects of air pollution on glucose and lipid metabolism in the liver. Hypothalamic inflammation may play a pivotal role in the adverse effects of PM2.5 by modulating peripheral inflammation. Additional experiments are needed to further clarify the inflammation-independent hepatic metabolic abnormalities in response to PM2.5 exposure. 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Toxicological Sciences – Oxford University Press
Published: Apr 9, 2018
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