Complementary Immunometabolic Effects of Exercise and PPARβ/δ Agonist in the Context of Diet-Induced Weight Loss in Obese Female Mice

Sébastien Le Garf, Joseph Murdaca, Isabelle Mothe-Satney, Brigitte Sibille, Gwenaëlle Le Menn, Giulia Chinetti, Jaap G Neels, Anne-Sophie Rousseau, Sébastien Le Garf, Joseph Murdaca, Isabelle Mothe-Satney, Brigitte Sibille, Gwenaëlle Le Menn, Giulia Chinetti, Jaap G Neels, Anne-Sophie Rousseau

Abstract

Regular aerobic exercise, independently of weight loss, improves metabolic and anti-inflammatory states, and can be regarded as beneficial in counteracting obesity-induced low-grade inflammation. However, it is still unknown how exercise alters immunometabolism in a context of dietary changes. Agonists of the Peroxisome Proliferator Activated-Receptor beta/delta (PPARβ/δ) have been studied this last decade as "exercise-mimetics", which are potential therapies for metabolic diseases. In this study, we address the question of whether PPARβ/δ agonist treatment would improve the immunometabolic changes induced by exercise in diet-induced obese female mice, having switched from a high fat diet to a normal diet. 24 mice were assigned to groups according to an 8-week exercise training program and/or an 8-week treatment with 3 mg/kg/day of GW0742, a PPARβ/δ agonist. Our results show metabolic changes of peripheral lymphoid tissues with PPARβ/δ agonist (increase in fatty acid oxidation gene expression) or exercise (increase in AMPK activity) and a potentiating effect of the combination of both on the percentage of anti-inflammatory Foxp3+ T cells. Those effects are associated with a decreased visceral adipose tissue mass and skeletal muscle inflammation (TNF-α, Il-6, Il-1β mRNA level), an increase in skeletal muscle oxidative capacities (citrate synthase activity, endurance capacity), and insulin sensitivity. We conclude that a therapeutic approach targeting the PPARβ/δ pathway would improve obesity treatment.

Keywords: inflammation; peroxisome proliferator-activated receptor; regulatory T cells; training.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Experimental design of the study. (A) 30 mice had free access to a Normal chow Diet (ND) (n = 6) or High Fat Diet (HFD) (n = 24) for 12 weeks. At T0, they all received a ND for 8 weeks. HFD mice were then randomly assigned in one of four groups: only return to ND (HFD-ND, n = 6), return to ND plus exercise training (HFD-ND-EX, n = 6), return to ND plus PPARβ/δ agonist GW0742 treatment (HFD-ND-GW, n = 6), or return to ND plus combined treatment (HFD-ND-EX-GW, n = 6). ND fed mice were maintained on a ND and were trained to be considered as a reference group (ND-EX, n = 6). At T0 and T1, glucose tolerance test (GTT) was performed for ND-ex, HFD-ND, and HFD-ND-GW groups, and treadmill endurance test was performed in trained mice (ND-EX, HFD-ND-EX, and HFD-ND-EX-GW). (B) Over time representation of weight gain during the 12-week high fat diet (HFD) compared to the normal chow diet (ND). (C) Kinetics of weight variation during the 8-wk treatment protocol compared to ND-EX. Data are expressed as mean ± sd.; µ p < 0.05 vs. ND; £ p < 0.05 vs. all groups; $ p < 0.05 vs. ND-EX; # p < 0.05 GW0742 effect.
Figure 2
Figure 2
Glucose tolerance curves and insulin plasma concentrations at T0 (after 12-week HFD) and T1 (after 8-week-returning to a ND). (A) Glucose tolerance test (GTT) at T0, i.e. after 12-weeks HFD (n = 12) or ND (n = 6); (B) Plasma insulin Area Under the Curve (AUC) during GTT at T0; (C) HOMA-IR index calculated with basal blood glucose (mmol/L) and blood insulin during GTT at T0. (D) GTT at T1 after returning to ND only (HFD-ND, n = 6) or combined with a PPARβ/δ agonist (GW0742) treatment (HFD-ND-GW, n = 6). (E) Plasma insulin (AUC) during GTT at T1; (F) HOMA-IR index at T1. Data are shown as mean ± SD. µ p < 0.05 vs. ND at T0.
Figure 3
Figure 3
Carnitine palmitoyltransferase 1a (CPT1a) mRNA and AMP-activated protein kinase (AMPK) protein levels in skeletal muscle and lymph nodes. (A) CPT1a mRNA level in vastus lateralis and (B) in lymph nodes. (C) and (D) Phosphorylated (Thr172) AMPK and total AMPK protein levels measured by western blot in vastus lateralis (C) and in lymph nodes (D). Data are shown as mean ± SD. (n = 6 per group). Blots are shown for n = 2 per group. * p < 0.05 exercise training effect (2-ways ANOVA); # p < 0.05 GW0742 effect (2-way ANOVA); $ p < 0.05 vs. ND-EX (one-way ANOVA).
Figure 4
Figure 4
CD4+ expressing Foxp3 T cells in lymph nodes and TGF-β mRNA levels. (A) Gating strategy of flow cytometry analysis of Foxp3+ on CD4+ cells from lymph nodes. (B) Frequency of CD3+CD4+Foxp3+T cells from lymph nodes after return to ND (T1). (C) Mean Fluorescence Intensity (MFI) in the APC fluorescence channel (gated Foxp3+ in A). (D) mRNA levels of TGF-β relative to 36B4 in lymph nodes. Data are shown as mean ± SD (n = 6 per group). # p < 0.05, GW0742 effect and * p < 0.05, interaction effect between exercise and GW0742 (2-way ANOVAs). $ p < 0.05 vs. ND-EX and £ p < 0.05 vs. all groups (one-way ANOVAs).
Figure 5
Figure 5
Inflammatory and metabolic states of skeletal muscle. (A) Relative mRNA levels of IL-6, TNF-α, and MCP-1. Data are expressed as arbitrary units of expression (A.U.) relatively to 36B4 in the vastus lateralis. (B,C) Macrophage infiltration by immunohistology staining with Alexa-Fluor488 (staining of actin filament) conjugated phalloidin, CD68 (staining of myeloid cells), and DAPI (staining of cell nucleus) in tibialis lateralis anterior. White borders show the selected area for magnification. White arrows represent infiltrated macrophages. Data are shown as macrophage density (cell number per mm²) for ND-EX (n = 3), HFD-ND (n = 6), HFD -ND-EX (n = 4), HFD-ND-GW (n = 6), and HFD-ND-EX-GW groups (n = 6). (D) Citrate synthase activity is expressed in µmol/mL/mg of vastus lateralis tissue. (E) Endurance performance is expressed as the time to exhaustion in minutes of treadmill running (Tlim) for exercise trained groups (ND-EX, HFD-ND-EX, HFD-ND-EX-GW). Data are expressed as mean ± SD. n = 6 per group. # p < 0.05 GW0742 effect; * p < 0.05 exercise training effect (2-way ANOVAs); $ p < 0.05 vs. ND-EX; £ p < 0.05 vs. all groups (one-way ANOVAs).

References

    1. Kivimaki M., Kuosma E., Ferrie J.E., Luukkonen R., Nyberg S.T., Alfredsson L., Batty G.D., Brunner E.J., Fransson E., Goldberg M., et al. Overweight, obesity, and risk of cardiometabolic multimorbidity: Pooled analysis of individual-level data for 120,813 adults from 16 cohort studies from the USA and Europe. Lancet Public Health. 2017;2:e277–e285. doi: 10.1016/S2468-2667(17)30074-9.
    1. Hotamisligil G.S. Inflammation, metaflammation and immunometabolic disorders. Nature. 2017;542:177–185. doi: 10.1038/nature21363.
    1. Khan I.M., Perrard X.Y., Brunner G., Lui H., Sparks L.M., Smith S.R., Wang X., Shi Z.Z., Lewis D.E., Wu H., et al. Intermuscular and perimuscular fat expansion in obesity correlates with skeletal muscle T cell and macrophage infiltration and insulin resistance. Int. J. Obes. (Lond) 2015;39:1607–1618. doi: 10.1038/ijo.2015.104.
    1. Goodpaster B.H. Mitochondrial deficiency is associated with insulin resistance. Diabetes. 2013;62:1032–1035. doi: 10.2337/db12-1612.
    1. Gleeson M., Bishop N.C., Stensel D.J., Lindley M.R., Mastana S.S., Nimmo M.A. The anti-inflammatory effects of exercise: Mechanisms and implications for the prevention and treatment of disease. Nat. Rev. Immunol. 2011;11:607–615. doi: 10.1038/nri3041.
    1. Chin S.H., Kahathuduwa C.N., Binks M. Physical activity and obesity: What we know and what we need to know. Obes. Rev. 2016;17:1226–1244. doi: 10.1111/obr.12460.
    1. Cohen S., Danzaki K., MacIver N.J. Nutritional effects on T-cell immunometabolism. Eur. J. Immunol. 2017;47:225–235. doi: 10.1002/eji.201646423.
    1. Hotamisligil G.S. Foundations of immunometabolism and implications for metabolic health and disease. Immunity. 2017;47:406–420. doi: 10.1016/j.immuni.2017.08.009.
    1. Kadoglou N.P., Iliadis F., Angelopoulou N., Perrea D., Ampatzidis G., Liapis C.D., Alevizos M. The anti-inflammatory effects of exercise training in patients with type 2 diabetes mellitus. Eur. J. Cardiovasc. Prev. Rehabil. 2007;14:837–843. doi: 10.1097/HJR.0b013e3282efaf50.
    1. Pedersen B.K. Anti-inflammatory effects of exercise: Role in diabetes and cardiovascular disease. Eur. J. Clin. Investig. 2017;47:600–611. doi: 10.1111/eci.12781.
    1. Handzlik M.K., Shaw A.J., Dungey M., Bishop N.C., Gleeson M. The influence of exercise training status on antigen-stimulated IL-10 production in whole blood culture and numbers of circulating regulatory T cells. Eur. J. Appl. Physiol. 2013;113:1839–1848. doi: 10.1007/s00421-013-2614-y.
    1. Yuan N., Zhang H.F., Wei Q., Wang P., Guo W.Y. Expression of CD4+CD25+Foxp3+ regulatory T cells, interleukin 10 and transforming growth factor beta in newly diagnosed type 2 diabetic patients. Exp. Clin. Endocrinol. Diabetes. 2018;126:96–101.
    1. Han J.M., Patterson S.J., Speck M., Ehses J.A., Levings M.K. Insulin inhibits IL-10-mediated regulatory T cell function: Implications for obesity. J. Immunol. 2014;192:623–629. doi: 10.4049/jimmunol.1302181.
    1. Berod L., Friedrich C., Nandan A., Freitag J., Hagemann S., Harmrolfs K., Sandouk A., Hesse C., Castro C.N., Bahre H., et al. De novo fatty acid synthesis controls the fate between regulatory T and T helper 17 cells. Nat. Med. 2014;20:1327–1333. doi: 10.1038/nm.3704.
    1. Cluxton D., Petrasca A., Moran B., Fletcher J.M. Differential regulation of human treg and Th17 cells by fatty acid synthesis and glycolysis. Front. Immunol. 2019;10:115. doi: 10.3389/fimmu.2019.00115.
    1. Michalek R.D., Gerriets V.A., Jacobs S.R., Macintyre A.N., MacIver N.J., Mason E.F., Sullivan S.A., Nichols A.G., Rathmell J.C. Cutting edge: Distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets. J. Immunol. 2011;186:3299–3303. doi: 10.4049/jimmunol.1003613.
    1. Howie D., Cobbold S.P., Adams E., Ten Bokum A., Necula A.S., Zhang W., Huang H., Roberts D.J., Thomas B., Hester S.S., et al. Foxp3 drives oxidative phosphorylation and protection from lipotoxicity. JCI Insight. 2017;2:e89160. doi: 10.1172/jci.insight.89160.
    1. Norata G.D., Caligiuri G., Chavakis T., Matarese G., Netea M.G., Nicoletti A., O’Neill L.A., Marelli–Berg F.M. The cellular and molecular basis of translational immunometabolism. Immunity. 2015;43:421–434. doi: 10.1016/j.immuni.2015.08.023.
    1. Neels J.G., Grimaldi P.A. Physiological functions of peroxisome proliferator-activated receptor beta. Physiol. Rev. 2014;94:795–858. doi: 10.1152/physrev.00027.2013.
    1. Luquet S., Lopez-Soriano J., Holst D., Fredenrich A., Melki J., Rassoulzadegan M., Grimaldi P.A. Peroxisome proliferator–activated receptor delta controls muscle development and oxidative capability. FASEB J. 2003;17:2299–2301. doi: 10.1096/fj.03-0269fje.
    1. Mothe–Satney I., Murdaca J., Sibille B., Rousseau A.S., Squillace R., Le Menn G., Rekima A., Larbret F., Pele J., Verhasselt V., et al. A role for Peroxisome Proliferator-activated receptor beta in T cell development. Sci. Rep. 2016;6:34317. doi: 10.1038/srep34317.
    1. Kanakasabai S., Chearwae W., Walline C.C., Iams W., Adams S.M., Bright J.J. Peroxisome proliferator–activated receptor delta agonists inhibit T helper type 1 (Th1) and Th17 responses in experimental allergic encephalomyelitis. Immunology. 2010;130:572–588. doi: 10.1111/j.1365-2567.2010.03261.x.
    1. Mothe–Satney I., Piquet J., Murdaca J., Sibille B., Grimaldi P.A., Neels J.G., Rousseau A.S. Peroxisome Proliferator Activated Receptor Beta (PPARbeta) activity increases the immune response and shortens the early phases of skeletal muscle regeneration. Biochimie. 2017;136:33–41. doi: 10.1016/j.biochi.2016.12.001.
    1. Le Menn G., Neels J.G. Regulation of immune cell function by PPARs and the connection with metabolic and neurodegenerative diseases. Int. J. Mol. Sci. 2018;19:1575. doi: 10.3390/ijms19061575.
    1. Lovejoy J.C., Sainsbury A., Stock Conference Working G. Sex differences in obesity and the regulation of energy homeostasis. Obes. Rev. 2009;10:154–167. doi: 10.1111/j.1467-789X.2008.00529.x.
    1. Reue K. Sex differences in obesity: X chromosome dosage as a risk factor for increased food intake, adiposity and co-morbidities. Physiol. Behav. 2017;176:174–182. doi: 10.1016/j.physbeh.2017.02.040.
    1. Williams R.L., Wood L.G., Collins C.E., Callister R. Effectiveness of weight loss interventions--is there a difference between men and women: A systematic review. Obes. Rev. 2015;16:171–186. doi: 10.1111/obr.12241.
    1. Bradley R.L., Jeon J.Y., Liu F.F., Maratos–Flier E. Voluntary exercise improves insulin sensitivity and adipose tissue inflammation in diet-induced obese mice. Am. J. Physiol. Endocrinol. Metab. 2008;295:E586–E594. doi: 10.1152/ajpendo.00309.2007.
    1. Blagih J., Coulombe F., Vincent E.E., Dupuy F., Galicia–Vazquez G., Yurchenko E., Raissi T.C., van der Windt G.J., Viollet B., Pearce E.L., et al. The energy sensor AMPK regulates T cell metabolic adaptation and effector responses in vivo. Immunity. 2015;42:41–54. doi: 10.1016/j.immuni.2014.12.030.
    1. Newton R., Priyadharshini B., Turka L.A. Immunometabolism of regulatory T cells. Nat. Immunol. 2016;17:618–625. doi: 10.1038/ni.3466.
    1. Lendoye E., Sibille B., Rousseau A.S., Murdaca J., Grimaldi P.A., Lopez P. PPARbeta activation induces rapid changes of both AMPK subunit expression and AMPK activation in mouse skeletal muscle. Mol. Endocrinol. 2011;25:1487–1498. doi: 10.1210/me.2010-0504.
    1. Griffin C., Hutch C.R., Abrishami S., Stelmak D., Eter L., Li Z., Chang E., Agarwal D., Zamarron B., Varghese M., et al. Inflammatory responses to dietary and surgical weight loss in male and female mice. Biol. Sex. Differ. 2019;10:16. doi: 10.1186/s13293-019-0229-7.
    1. Valentine R.J., McAuley E., Vieira V.J., Baynard T., Hu L., Evans E.M., Woods J.A. Sex differences in the relationship between obesity, C-reactive protein, physical activity, depression, sleep quality and fatigue in older adults. Brain. Behav. Immun. 2009;23:643–648. doi: 10.1016/j.bbi.2008.12.003.
    1. Jung D.Y., Ko H.J., Lichtman E.I., Lee E., Lawton E., Ong H., Yu K., Azuma Y., Friedline R.H., Lee K.W., et al. Short-term weight loss attenuates local tissue inflammation and improves insulin sensitivity without affecting adipose inflammation in obese mice. Am. J. Physiol. Endocrinol. Metab. 2013;304:E964–E976. doi: 10.1152/ajpendo.00462.2012.
    1. Kim M.S., Kim I.Y., Sung H.R., Nam M., Kim Y.J., Kyung D.S., Seong J.K., Hwang G.S. Metabolic dysfunction following weight regain compared to initial weight gain in a high-fat diet-induced obese mouse model. J. Nutr. Biochem. 2019;69:44–52. doi: 10.1016/j.jnutbio.2019.02.011.
    1. Iglesias J., Barg S., Vallois D., Lahiri S., Roger C., Yessoufou A., Pradevand S., McDonald A., Bonal C., Reimann F., et al. PPARbeta/delta affects pancreatic beta cell mass and insulin secretion in mice. J. Clin. Investig. 2012;122:4105–4117. doi: 10.1172/JCI42127.
    1. Yoo T., Ham S.A., Lee W.J., Hwang S.I., Park J.A., Hwang J.S., Hur J., Shin H.C., Han S.G., Lee C.H., et al. Ligand–dependent interaction of PPARdelta with T–Cell protein tyrosine phosphatase 45 enhances insulin signaling. Diabetes. 2018;67:360–371. doi: 10.2337/db17-0499.
    1. Hori S., Nomura T., Sakaguchi S. Control of regulatory T cell development by the transcription factor Foxp3. Science. 2003;299:1057–1061. doi: 10.1126/science.1079490.
    1. Angelin A., Gil–de–Gomez L., Dahiya S., Jiao J., Guo L., Levine M.H., Wang Z., Quinn W.J., 3rd, Kopinski P.K., Wang L., et al. Foxp3 reprograms T cell metabolism to function in low–glucose, high–lactate environments. Cell Metab. 2017;25:1282–1293. doi: 10.1016/j.cmet.2016.12.018.
    1. Gerriets V.A., Kishton R.J., Nichols A.G., Macintyre A.N., Inoue M., Ilkayeva O., Winter P.S., Liu X., Priyadharshini B., Slawinska M.E., et al. Metabolic programming and PDHK1 control CD4+ T cell subsets and inflammation. J. Clin. Investig. 2015;125:194–207. doi: 10.1172/JCI76012.
    1. Lobo T.F., Borges C.M., Mattar R., Gomes C.P., de Angelo A.G.S., Pendeloski K.P.T., Daher S. Impaired Treg and NK cells profile in overweight women with gestational diabetes mellitus. Am. J. Reprod. Immunol. 2018;79:e12810. doi: 10.1111/aji.12810.
    1. Liu Y., Zhang P., Lim J., Kulkarni A.B., Perruche S., Chen W. A critical function for TGF-beta signaling in the development of natural CD4+CD25+Foxp3+ regulatory T cells. Nat. Immunol. 2008;9:632–640. doi: 10.1038/ni.1607.
    1. Priyadharshini B., Loschi M., Newton R.H., Zhang J.W., Finn K.K., Gerriets V.A., Huynh A., Rathmell J.C., Blazar B.R., Turka L.A. Cutting edge: TGF-beta and phosphatidylinositol 3-Kinase signals modulate distinct metabolism of regulatory T cell subsets. J. Immunol. 2018;201:2215–2219. doi: 10.4049/jimmunol.1800311.
    1. Mihaylova M.M., Shaw R.J. The AMPK signalling pathway coordinates cell growth, autophagy and metabolism. Nat. Cell Biol. 2011;13:1016–1023. doi: 10.1038/ncb2329.
    1. Sun L., Fu J., Zhou Y. Metabolism controls the balance of Th17/T–regulatory cells. Front. Immunol. 2017;8:1632. doi: 10.3389/fimmu.2017.01632.
    1. Narkar V.A., Downes M., Yu R.T., Embler E., Wang Y.X., Banayo E., Mihaylova M.M., Nelson M.C., Zou Y., Juguilon H., et al. AMPK and PPARdelta agonists are exercise mimetics. Cell. 2008;134:405–415. doi: 10.1016/j.cell.2008.06.051.
    1. Turner N., Bruce C.R., Beale S.M., Hoehn K.L., So T., Rolph M.S., Cooney G.J. Excess lipid availability increases mitochondrial fatty acid oxidative capacity in muscle: Evidence against a role for reduced fatty acid oxidation in lipid-induced insulin resistance in rodents. Diabetes. 2007;56:2085–2092. doi: 10.2337/db07-0093.
    1. De Wilde J., Mohren R., van den Berg S., Boekschoten M., Dijk K.W., de Groot P., Muller M., Mariman E., Smit E. Short–term high fat–feeding results in morphological and metabolic adaptations in the skeletal muscle of C57BL/6J mice. Physiol. Genom. 2008;32:360–369. doi: 10.1152/physiolgenomics.00219.2007.
    1. Hancock C.R., Han D.H., Chen M., Terada S., Yasuda T., Wright D.C., Holloszy J.O. High-fat diets cause insulin resistance despite an increase in muscle mitochondria. Proc. Natl. Acad. Sci. USA. 2008;105:7815–7820. doi: 10.1073/pnas.0802057105.
    1. Stephenson E.J., Camera D.M., Jenkins T.A., Kosari S., Lee J.S., Hawley J.A., Stepto N.K. Skeletal muscle respiratory capacity is enhanced in rats consuming an obesogenic Western diet. Am. J. Physiol. Endocrinol. Metab. 2012;302:E1541–E1549. doi: 10.1152/ajpendo.00590.2011.
    1. Steinberg G.R. Cellular Energy Sensing and Metabolism–Implications for Treating Diabetes: The 2017 Outstanding Scientific Achievement Award Lecture. Diabetes. 2018;67:169–179. doi: 10.2337/dbi17-0039.
    1. Weinhold M., Shimabukuro–Vornhagen A., Franke A., Theurich S., Wahl P., Hallek M., Schmidt A., Schinkothe T., Mester J., von Bergwelt–Baildon M., et al. Physical exercise modulates the homeostasis of human regulatory T cells. J. Allergy. Clin. Immunol. 2016;137:1607–1610. doi: 10.1016/j.jaci.2015.10.035.
    1. Rousseau A.S., Sibille B., Murdaca J., Mothe–Satney I., Grimaldi P.A., Neels J.G. alpha–Lipoic acid up–regulates expression of peroxisome proliferator-activated receptor beta in skeletal muscle: Involvement of the JNK signaling pathway. FASEB J. 2016;30:1287–1299. doi: 10.1096/fj.15-280453.

Source: PubMed

스폰서 및 공동 작업자

건강 상태

약물 개입

3
구독하다