Exercise of high intensity ameliorates hepatic inflammation and the progression of NASH - PubMed (original) (raw)

Exercise of high intensity ameliorates hepatic inflammation and the progression of NASH

Gavin Fredrickson et al. Mol Metab. 2021 Nov.

Abstract

Objective: Non-alcoholic fatty liver disease (NAFLD) covers a wide spectrum of liver pathology ranging from simple fatty liver to non-alcoholic steatohepatitis (NASH). Notably, immune cell-driven inflammation is a key mechanism in the transition from fatty liver to the more serious NASH. Although exercise training is effective in ameliorating obesity-related diseases, the underlying mechanisms of the beneficial effects of exercise remain unclear. It is unknown whether there is an optimal modality and intensity of exercise to treat NAFLD. The objective of this study was to determine whether high-intensity interval training (HIIT) or moderate-intensity continuous training (MIT) is more effective at ameliorating the progression of NASH.

Methods: Wild-type mice were fed a high-fat, high-carbohydrate (HFHC) diet for 6 weeks and left sedentary (SED) or assigned to either an MIT or HIIT regimen using treadmill running for an additional 16 weeks. MIT and HIIT groups were pair-fed to ensure that energy intake was similar between the exercise cohorts. To determine changes in whole-body metabolism, we performed insulin and glucose tolerance tests, indirect calorimetry, and magnetic resonance imaging. NASH progression was determined by triglyceride accumulation, expression of inflammatory genes, and histological assessment of fibrosis. Immune cell populations in the liver were characterized by cytometry by time-of-flight mass spectrometry, and progenitor populations within the bone marrow were assessed by flow cytometry. Finally, we analyzed the transcriptional profile of the liver by bulk RNA sequencing.

Results: Compared with SED mice, both HIIT and MIT suppressed weight gain, improved whole-body metabolic parameters, and ameliorated the progression of NASH by reducing hepatic triglyceride levels, inflammation, and fibrosis. However, HIIT was superior to MIT at reducing adiposity, improving whole-body glucose tolerance, and ameliorating liver steatosis, inflammation, and fibrosis, without any changes in body weight. Improved NASH progression in HIIT mice was accompanied by a substantial decrease in the frequency of pro-inflammatory infiltrating, monocyte-derived macrophages in the liver and reduced myeloid progenitor populations in the bone marrow. Notably, an acute bout of MIT or HIIT exercise had no effect on the intrahepatic and splenic immune cell populations. In addition, bulk mRNA sequencing of the entire liver tissue showed a pattern of gene expression confirming that HIIT was more effective than MIT in improving liver inflammation and lipid biosynthesis.

Conclusions: Our data suggest that exercise lessens hepatic inflammation during NASH by reducing the accumulation of hepatic monocyte-derived inflammatory macrophages and bone marrow precursor cells. Our findings also indicate that HIIT is superior to MIT in ameliorating the disease in a dietary mouse model of NASH.

Keywords: Exercise; HIIT; Inflammation; NAFLD; NASH.

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Figures

Figure 1

HIIT is superior to MIT in decreasing adiposity and glucose tolerance in NASH mice. (A) Experimental design. (B) Distance covered in a maximal running capacity test at week 6 (pre-exercise intervention) and week 20 (post-exercise intervention; n = 12 mice per group). (C) Bodyweight at weeks 6 and 20 (left) and percent change in body weight (right; n = 12 mice per group). (D) Fat mass (left) and lean mass (right) determined by MRI (n = 12 mice per group). (E) Glucose tolerance test (GTT, left) with corresponding areas under the curves (AUC, right; n = 11–12 mice per group). (F) Insulin tolerance test (ITT, left) with corresponding areas under the curves (AUC, right; n = 5–6 mice per group). (G) Energy expenditure (left) during the full day, light phase, and dark phase (middle) and linear regression by lean mass (right, n = 12 mice per group). (H) Respiratory exchange ratio (RER, left) with corresponding daily RER (right; n = 12 mice per group). (I) Ambulatory activity (left) with corresponding hourly ambulatory activity per day (right; n = 12 mice per group). Data are presented as mean ± SEM. Statistical significance is denoted by (∗p ≤ 0.05, ∗∗p ≤ 0.01, ∗∗∗p ≤ 0.001, and ∗∗∗∗p ≤ 0.0001).

Figure 2

HIIT is more effective in ameliorating NASH progression. (A) Liver mass (n = 12 mice per group). (B) Liver triglyceride content (n = 6 mice per group). (C) Representative hematoxylin and eosin (H&E) liver stain (scale bar, 200 μm). (D) RT-PCR gene expression analysis of liver inflammatory genes (Mcp1, Sele, Icam1, IL1B, TNFa, and Inos; n = 6–12 mice per group). (E) Representative Mason's trichrome liver stain (scale bar, 200 μm). (F) Quantification of the area with collagen deposition (n = 3 mice per group). (G) Interstitial fibrosis scoring (n = 3 mice per group). (H) RT-PCR expression analysis of liver pro-fibrogenesis genes (Col1a1, Timp1, Mmp2, Tgfb1, and Acta2; n = 5–12 mice per group). (I) Representative immunohistochemistry staining (left) and quantification (right) of αSMA expression (n = 6 mice per group). (J) Serum ALT and AST levels (n = 5–6 mice per group). Data are presented as mean ± SEM. Statistical significance is denoted by (∗p ≤ 0.05, ∗∗p ≤ 0.01, and ∗∗∗p ≤ 0.001).

Figure 3

Chronic HIIT reduces the accumulation of inflammatory MoMFs in the NASH liver. (A) Representative viSNE plots from liver CyTOF data showing unsupervised clustering and expression of markers used in gating. Gates were drawn based on the expression of commonly used markers to distinguish B cells, monocytes (Mono), Kupffer Cells (KC), monocyte-derived macrophages (MoMF), dendritic cells (DC), neutrophils (PMN), natural killer (NK) cells, natural killer T Cells (NKT), double-negative T Cells (DN T), and CD4 and CD8 T cells. (B) Quantification of hepatic non-lymphocyte immune cell subsets from CyTOF data in A, shown as a frequency of hepatic CD45+ cells (n = 6 mice per group). (C) Quantification of hepatic lymphocyte immune cell subsets from CyTOF data in A, shown as a frequency of hepatic CD45+ cells (n = 6 mice per group). (D) Representative gates for hepatic MoMF subsets: Pro-inflammatory (CCR2+ Ly6Chi) and anti-inflammatory (CCR2- Ly6Clo). Gated on MoMF population from viSNE plot A (left). Quantification of hepatic MoMF subsets, shown as a frequency of hepatic MoMFs (right, n = 6 mice per group). (E) Representative gates for LPS stimulation Flow data showing IFNγ+ and IL-6+ MoMF populations. Gated on MoMF population (left). Quantification of IFNγ+, IL-6+, TNFα+, and IL-10+ MoMFs as a frequency of total MoMFs from LPS stimulation Flow data (right, n = 6 mice per group). Data are presented as mean ± SEM. Statistical significance is denoted by (∗p ≤ 0.05, ∗∗p ≤ 0.01, and ∗∗∗p ≤ 0.001).

Figure 4

HIIT reduces hematopoiesis and the systemic supply of leukocytes. (A) Quantification of total bone marrow leukocytes (n = 6 mice per group). (B) Representative gating strategy for lin− Sca-1+ cKit+ cells (LSKs, left). Quantification of total bone marrow LSKs (right, n = 6 mice per group). (C) Representative gating strategy for megakaryocyte-erythrocyte progenitors (MEPs) and common myeloid progenitors (CMPs) gated from the cKit+ and Sca-1- population in panel B (left). Quantification of total bone marrow MEPs and CMPs (right, n = 6 mice per group). (D) Representative gating strategy for granulocyte-monocyte progenitors (GMPs) and monocyte-dendritic progenitors (MDPs) gated from the CD16/32hi and CD34+ population in panel C (left). Quantification of total bone marrow GMPs and MDPs (right, n = 6 mice per group). (E) Quantification of monocytes, neutrophils (PMNs), dendritic cells (DCs), CD4 T cells, CD8 T cells, and B cells in blood shown as the total cell number per 200 μL (n = 6 mice per group). (F) Quantification of dendritic cells (DCs), neutrophils (PMNs), natural killer cells (NK), CD4 T cells, CD8 T cells, and B cells in the spleen shown as the total number of cells (n = 6 mice per group). Data are presented as mean ± SEM. Statistical significance is denoted by (∗p ≤ 0.05 and ∗∗p ≤ 0.01).

Figure 5

Exercise of different intensities alters the hepatic transcriptional landscape. (A) Venn diagram representing the total number of differentially expressed genes (DEGs) for the comparisons of HIIT to SED and MIT to SED from a bulk RNA sequencing of liver tissue (n = 5–6 mice per group). (B) Volcano plot of all genes from the comparison of HIIT to SED, shown as the average fold change by the associated log(pval). A positive fold change indicates that HIIT had a higher expression of that gene than SED (n = 5–6 mice per group). (C) Volcano plot of all genes from the comparison of MIT to SED, shown as the average fold change by the associated log(pval). A positive fold change indicates that MIT had a higher expression of that gene than SED (n = 5–6 mice per group). (D) Top upstream regulators from an upstream regulator analysis of all DEGs. Red indicates the top upstream regulators from the analysis of the DEGs from the comparison of HIIT to SED. Blue indicates the top upstream regulator from the analysis of the DEGs from the comparison of MIT to SED. (E) Top gene ontology (GO) terms from a GO enrichment analysis of the DEGs from the comparison of HIIT to SED. (F) Top pathways from pathway analysis of the DEGs from the comparison of HIIT to SED. (G) Top pathways from pathway analysis of the DEGs from the comparison of MIT to SED. (H) RT-PCR analysis of liver lipogenesis genes (Pparg, Dgat1, Acaca, and Acacb; n = 6 mice per group). (I) RT-PCR analysis of liver lipid catabolism genes (Ppara, Acox1, Lipe, and Cpt1a; n = 6 mice per group). Data are presented as mean ± SEM. Statistical significance is denoted by (∗p ≤ 0.05, ∗∗p ≤ 0.01, and ∗∗∗p ≤ 0.001).

References

1. Younossi Z.M., Koenig A.B., Abdelatif D., Fazel Y., Henry L., Wymer M. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology. 2016;64(1):73–84. - PubMed
1. Mann J.P., Anstee Q.M. NAFLD: PNPLA3 and obesity: a synergistic relationship in NAFLD. Nature Reviews Gastroenterology & Hepatology. 2017;14(9):506–507. - PubMed
1. Parthasarathy G., Revelo X., Malhi H. Pathogenesis of nonalcoholic steatohepatitis: an overview. Hepatology Communications. 2020;4(4):478–492. - PMC - PubMed
1. Buzzetti E., Pinzani M., Tsochatzis E.A. The multiple-hit pathogenesis of non-alcoholic fatty liver disease (NAFLD) Metabolism - Clinical and Experimental. 2016;65(8):1038–1048. - PubMed
1. Fabbrini E., Sullivan S., Klein S. Obesity and nonalcoholic fatty liver disease: biochemical, metabolic, and clinical implications. Hepatology. 2010;51(2):679–689. - PMC - PubMed

Exercise of high intensity ameliorates hepatic inflammation and the progression of NASH - PubMed (original) (raw)