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Persistent changes in liver methylation and microbiome composition following reversal of diet-induced non-alcoholic-fatty liver disease

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Abstract

Non-alcoholic fatty liver disease (NAFLD) is a metabolic liver disease that is thought to be reversible by changing the diet. To examine the impact of dietary changes on progression and cure of NAFLD, we fed mice a high-fat diet (HFD) or high-fructose diet (HFrD) for 9 weeks, followed by an additional 9 weeks, where mice were given normal chow diet. As predicted, the diet-induced NAFLD elicited changes in glucose tolerance, serum cholesterol, and triglyceride levels in both diet groups. Moreover, the diet-induced NAFLD phenotype was reversed, as measured by the recovery of glucose intolerance and high cholesterol levels when mice were given normal chow diet. However, surprisingly, the elevated serum triglyceride levels persisted. Metagenomic analysis revealed dietary-induced changes of microbiome composition, some of which remained altered even after reversing the diet to normal chow, as illustrated by species of the Odoribacter genus. Genome-wide DNA methylation analysis revealed a “priming effect” through changes in DNA methylation in key liver genes. For example, the lipid-regulating gene Apoa4 remained hypomethylated in both groups even after introduction to normal chow diet. Our results support that dietary change, in part, reverses the NAFLD phenotype. However, some diet-induced effects remain, such as changes in microbiome composition, elevated serum triglyceride levels, and hypomethylation of key liver genes. While the results are correlative in nature, it is tempting to speculate that the dietary-induced changes in microbiome composition may in part contribute to the persistent epigenetic modifications in the liver.

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Acknowledgements

We thank our colleagues, Alicia Kang, Llanto Elma Faylon, Kok Huan Teo, and Norhashimah Binte Sulaiman from Nanyang Technological University for their assistance in animal maintaining and data collections that greatly contributed the manuscript. We thank Li Yiqing from National University Health System for his assistance with animal experiments. We thank Saraf Sahil, Loh Jie Hua, and Sam Xin Xiu from Singapore General Hospital for their great help in steatosis scoring. We also thank Zenia Tiang from the Genome Institute of Singapore with her help in library submission for sequencing. This work was supported by the LKC School of Medicine Start Up Grant, MOE TIER 1 Grant, Grant from SCELSE and EU Grant TORNADO awarded to Sven Pettersson. This work also was supported a CSIRC award (National Medical Research Council Singapore) and a SPF Grant (Biomedical Research Council Singapore) awarded to Roger Foo.

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  1. Hyejin Kim and Oliver Worsley co-first authors in this manuscript.

Authors and Affiliations

  1. Lee Kong Chian School of Medicine, Nanyang Technological University, 11 Mandalay Road, Singapore, 308232, Singapore

    Hyejin Kim & Sven Pettersson

  2. Department of Human Genetics, Genome Institute of Singapore, 60 Biopolis Street, Singapore, 138672, Singapore

    Oliver Worsley, Wilson Tan & Roger SY Foo

  3. Division of Surgical Oncology, National Cancer Centre Singapore, 11 Hospital Drive, Singapore, 169610, Singapore

    Edwin Yang, Ai Leng Liang & Pierce Kah Hoe Chow

  4. Duke-NUS Graduate Medical School, 8 College Road, Singapore, 169857, Singapore

    Edwin Yang & Pierce Kah Hoe Chow

  5. Singapore Centre on Environmental Life Science Engineering, 60 Nanyang Drive, Singapore, 637551, Singapore

    Rikky Wenang Purbojati, Daniela I. Drautz Moses, Stephan C. Schuster & Sven Pettersson

  6. Department of Neurology, National Neuroscience Institute, Singapore, Singapore

    Septian Hartono

  7. Princess Margaret Cancer Centre, University Health Network, Toronto, Canada

    Vanessa Fan

  8. Department of Medicine, Cardiovascular Research Institute, National University Health System, 1E Kent Ridge Rd, Singapore, 119228, Singapore

    Roger SY Foo

  9. Department of Hepato-Pancreato-Biliary and Transplantation Surgery, Singapore General Hospital, Outram Road, Singapore, 169608, Singapore

    Pierce Kah Hoe Chow

  10. Division of Cellular and Molecular Research, National Cancer Centre Singapore, 11 Hospital Drive, Singapore, 169610, Singapore

    Sven Pettersson

  11. Department of Anatomical Pathology, Singapore General Hospital, Singapore, Singapore

    Tony Kiat Hon Lim

  12. Department of Neurobiology, Care sciences and Society, Karolinska Institutet, Bioclincium, J30:10, Akademiska stråket 1, 17164, Stockholm, Sweden

    Sven Pettersson

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  1. Hyejin Kim
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Supplementary material 1 (PDF 355 kb) Figure S1. Illustration of the experimental schematic

18_2019_3114_MOESM2_ESM.pdf

Supplementary material 2 (PDF 393 kb) Figure S2. Daily food intakes for each diet. (A) Amount of daily food intake per mouse for 15 weeks. Food intake measured 2 times/week for 24 h and makes average for 1 week. (B) Daily caloric intake. *P < 0.05 for NC vs. HFD, two-way ANOVA (C) Liver weight, normalized to the tibia length (TA). *P < 0.05 for NC vs. HFD, one-way ANOVA

18_2019_3114_MOESM3_ESM.pdf

Supplementary material 3 (PDF 436 kb) Figure S3. IPGTT in mice show diet effects on glucose tolerance. Blood glucose levels were measured during IPGTTs, administered after (A) test diets and (B) reversal diets *P < 0.05 for NC vs. HFD, # < 0.05 for NC vs. HFrD, two-way ANOVA. IL18 (C) TLR4 (D) gene expression measured with quantitative RT-PCR, and normalized to housekeeping gene expression levels. * P≤0.05 for NC vs. HFD or HFrD, R_NC vs. R_HFD, one-way ANOVA

18_2019_3114_MOESM4_ESM.pdf

Supplementary material 4 (PDF 10945 kb) Figure S4. Changes in gut microbiome induced by diet changes. (A) Bubble chart shows top 30 bacterial genera from each diet group. Bubble sizes represent the relative size of gut bacteria populations. Bar graphs show changes in (B) Prevotella *P < 0.05 NC vs. HFD or HFrD; #P < 0.05 HFD vs. R_HFD; and HFrD vs. R_HFrD; one-way ANOVA; (C) Akkermansia *P < 0.05 NC vs. HFD or HFrD; #P < 0.05 HFD vs. R_HFD; and HFrD vs. R_HFrD, one-way ANOVA; and (D) Parabacteroides goldsteinii *P < 0.05 NC vs. HFrD; #P < 0.05 R_HFD vs. R_NC or R_HFrD. (E) Normalized abundance of butyrate kinase in gut microbiome; #P < 0.05 R-HFD vs. R_NC or R_HFrD

18_2019_3114_MOESM5_ESM.pdf

Supplementary material 5 (PDF 475 kb) Figure S5. Sites of differential methylation in DNA from mice fed HFD and HFrD diets. (A) Pie charts show the distribution of sites, where significant dmCpGs are found in DNA, during test diets and after returning to NC. UTR = untranslated region. (B) Traces show the average DNA methylation levels around the TSS and TES regions, where all significant dmCpGs are found, TSS = transcription start site, TES = transcription end site. dmCpG significance level was P≤0.05

18_2019_3114_MOESM6_ESM.pdf

Supplementary material 6 (PDF 386 kb) Figure S6. Heat map shows changes in gene expression levels for enzymes critical in DNA methylation, for each diet. Expression was measured with quantitative RT-PCR, normalized to housekeeping gene expression levels, and respective controls (NC and R_NC). Red indicates minimal change in gene expression; blue is a ~ twofold increase in gene expression

18_2019_3114_MOESM7_ESM.pdf

Supplementary material 7 (PDF 681 kb) Figure S7. (A) Representative western blot of APOA4 in the liver (n = 2) (B) Relative intensity of APOA4 in the liver (n = 8-10 mice per group). Levels were normalized with corresponding β-actin levels

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Kim, H., Worsley, O., Yang, E. et al. Persistent changes in liver methylation and microbiome composition following reversal of diet-induced non-alcoholic-fatty liver disease. Cell. Mol. Life Sci. 76, 4341–4354 (2019). https://doi.org/10.1007/s00018-019-03114-4

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