SUPPLEMENTARY DATA Bioinformatic analysis of ChIP-seq data Define the panel of genes associated with energy storage and expenditure We searched the genes list from NCBI Gene, Gene Ontology and UniProt database with the terms “obesity”, “energy homeostasis”, "energy metabolism", “energy storage”, and “energy expenditure” by two independent investigators, and through which we identified 742 genes from all these databases (supplementary Table 8). We then selected the genes associated either with energy storage or expenditure by searching the literatures from PubMed and Google Scholar with the term “gene name" and "obesity" or "energy metabolism" or "energy homeostasis" and "adipose tissue" or "adipocyte”. For a particular gene, if its deficiency protected mice from obesity or overexpression exacerbated obesity, we then categorized it into the panel of energy storage. On the contrary, if its deficiency or overexpression generated opposite phenotype, we classified it into the panel of energy expenditure. We next searched them in the BioGPS database to check their expression in the adipose tissue or adipocyte. If the transcriptional activity index is lower than 50, the gene was considered without expression in adipose tissue. Those genes were also excluded in which they are only expressed in the central nervous system to involve in energy metabolism. The above screenings allowed us to characterize 216 genes associated with energy storage (supplementary Table 9) and 159 genes associated with energy expenditure (supplementary Table 10). Characterization of differential methylated genes. We first compared the signal of chip-seq data in the promoter region to check the DNA methylation state of two panels. Promoter region of those differential methylated genes was defined as 5kb upstream TSS sites. We failed to detect a significant difference for most of the genes between HF and ND fed mice. To enhance the discrimination, a cut-off value (defined >1.5-fold) was then set up for data analysis. We calculated MBD2 ChIP-seq enrichment levels over energy storage and energy expenditure gene loci with normalization to the Fragments Per Kilobase Of Exon Per Million Fragments Mapped (FPKM) value. For a particular differential methylated gene, at least a two-fold difference between ND and HF samples was characterized. We divided TSS region of those genes into 100 bins and normalized MBD2 ChIP-seq signal with the FPKM value. Isolation of mature adipocytes Isolation of adipocytes was performed as described previously (1-3). Briefly, male mice were sacrificed by CO2 inhalation, and the epididymal adipose tissue depots were washed in cold Dulbecco’s PBS supplemented with 0.5% BSA, followed by digestion with 1 mg/mL type II collagenase in the presence of 5 mmol/L CaCl2. Tissue homogenates were incubated at 37°C for 30 min with shaking. After centrifugation, the floating oil were removed ,buoyant adipocytes were collected, filtered and collected as the adipocyte fraction. ©2016 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-0151/-/DC1 SUPPLEMENTARY DATA References: 1. Aune UL, Ruiz L, Kajimura S: Isolation and differentiation of stromal vascular cells to beige/brite cells. J Vis Exp, 2013 2. Orr JS, Puglisi MJ, Ellacott KL, Lumeng CN, Wasserman DH, Hasty AH: Toll-like receptor 4 deficiency promotes the alternative activation of adipose tissue macrophages. Diabetes 61:2718-2727, 2012 3. H X, GT B, Q Y, G T, D Y, CJ C, J S, A N, JS R, LA T, H C: Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. Journal of Clinical Investigation, 2003 4. Zhong J, Yu Q, Yang P, Rao X, He L, Fang J, Tu Y, Zhang Z, Lai Q, Zhang S, Kuczma M, Kraj P, Xu JF, Gong F, Zhou J, Wen L, Eizirik DL, Du J, Wang W, Wang CY: MBD2 regulates TH17 differentiation and experimental autoimmune encephalomyelitis by controlling the homeostasis of T- bet/Hlx axis. J Autoimmun 53:95-104, 2014 ©2016 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-0151/-/DC1 SUPPLEMENTARY DATA Supplementary Figure S1. Results for the Area Above Curve (AAC) of Insulin tolerance tests. ©2016 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-0151/-/DC1 SUPPLEMENTARY DATA Supplementary Figure S2. Metabolic index measured in CLAMS metabolic cages. A: Metabolic data for mean oxygen consumption (VO2) and carbon dioxide production (VCO2) of HFD-induced mice at each time point（Left, Middle）; The average VO2 and VCO2 of mice under HFD (Right). B: Metabolic data for mean oxygen consumption (VO2) and carbon dioxide production (VCO2) of mice under normal diet at each time point （Left, Middle）; The average VO2 and VCO2 of mice under normal diet (Right). ©2016 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-0151/-/DC1 SUPPLEMENTARY DATA Supplementary Figure S3. Real Time PCR results for genes associated with energy metabolism in the liver and skeletal muscle after 16wk of HFD or ND induction (n=8 per group). ©2016 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-0151/-/DC1 SUPPLEMENTARY DATA Supplementary Figure S4. HFD induced Mbd2-/- mice show higher expression of genes relevant to lipolysis and -oxidative. Western blot results for analysis of p-HSL, ATGL (lipolysis) and CPT1 (- oxidation) in the liver (A) and skeletal muscle (B). The expression of p-HSL and CPT1 was higher in HFD-induced Mbd2-/- mice as compared with WT controls. ©2016 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-0151/-/DC1 SUPPLEMENTARY DATA Supplementary Figure S5. The total 5-mC levels of genomic DNA isolated from mature adipocyte of HFD- and ND-induced mice. Mature adipocytes were isolated from epididymal adipose tissues after 16wk of HFD or ND induction as described, and the 5-mC levels were measured using a MethylFlash Methylated DNA Quantification kit. ©2016 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-0151/-/DC1 SUPPLEMENTARY DATA Supplementary Figure S6. Bisulfite DNA sequencing analysis of the selected Raptor and Ucp1 promoter region. A: Results for a selected peak region located within the Raptor promoter from MBD2 ChIP-seq analysis. B: Results for a selected peak region located within the Ucp1 promoter from MBD2 ChIP-seq analysis. Genomic DNA was isolated from epididymal adipose tissues, and was then subjected to bisulfite DNA sequencing as described. C: Results for the same Raptor promoter region using mature adipocytes genomic DNA isolated from epididymal adipose tissues. D: Results for the same Ucp1 promoter region using mature adipocyte genomic DNA isolated from epididymal adipose tissues. Unfilled cycles represent unmethylated cytosines, while filled cycles represent methylated cytosines. A total of 20 clones were analyzed for each sample. ©2016 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-0151/-/DC1 SUPPLEMENTARY DATA Supplementary Figure S7. Results for bioinformatic analysis of the potential transcription factor binding sites within the Raptor promoter enriched from the ChIP-seq data. The Raptor promoter region enriched from ChIP-seq data was subjected to Transfac and PROMO analysis to predict potential transcription factor binding sites. A potential Cebpα binding site was identified, which contains the - 3881 CpG site. ©2016 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-0151/-/DC1 SUPPLEMENTARY DATA Supplementary Figure S8. Results for bioinformatic analysis of the potential transcription factor binding sites within the Ucp1 promoter enriched from the ChIP-seq data. Similar as above, the Ucp1 promoter region enriched from ChIP-seq data was subjected to Transfac and PROMO analysis to predict potential transcription factor binding sites. A potential ATF6 binding site was identified, which contains the -3302 CpG site. ©2016 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-0151/-/DC1 SUPPLEMENTARY DATA Supplementary Figure S9. HFD is more potent to induce the expression of genes relevant to energy storage. A: Relative fold enrichment of peak regions in the promoter of genes relevant to energy storage and energy expenditure. Dok1 (tyrosine kinases-1), Fasn (Fatty acid synthase), Trem2 (Triggering receptor expressed on myeloid cells 2), Thbs1 (Thrombospondin 1), Agpat4 (1-acylglycerol-3-phosphate O-acyltransferase 4) and Mir103-2 are relevant to energy storage, while Leptin, Gpr12 (G protein- coupled receptor 12), Ppargc1a (peroxisome proliferator-activated receptor-gamma coactivator 1 alpha), Parp1 (Poly(ADP-ribose)polymerase-1), FATP-1 (fatty acid transport protein-1) and Vegf-a ( vascular endothelial growth factor A) are associated with energy expenditure. B: Real Time PCR results in the epididymal adipose tissues for analysis of the above selected genes. The results confirmed the ChIP-seq data as evidenced by the higher expression of energy storage genes after HFD induction (n=8 per group). ©2016 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db16-0151/-/DC1