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Nutrición Hospitalaria

versión On-line ISSN 1699-5198versión impresa ISSN 0212-1611

Nutr. Hosp. vol.30 no.1 Madrid jul. 2014 

ORIGINAL / Obesidad


Genome-wide screen of promoter methylation identifies novel markers in diet-induced obese mice

El cribado pangenómico de la metilación del promotor identifica marcadores nuevos en ratones con obesidad inducida por la dieta



Chaonan Fan, Hua Dong, Kesong Yan2, Wenwen Shen1, Cui Wang1, Lulu Xia1, Dawei Zhan2* and Kemin Qi1*

1Nutrition Research Unit. Beijing Pediatric Research Institute. Beijing Children's Hospital. Capital Medical University. Beijing. China.
2Laboratory Animal Center. Hospital 304 affiliated with the General Hospital of the People's Liberation Army. Beijing. China.
These authors contributed equally to this work.
*Address correspondence to Kemin Qi and Dawei Zhan.

This work was supported by the Nutricia Research Foundation (The Netherlands; grants 2009-15 and 2010-E4 to K.Q.), Funds for High-Level Technical Talents in the Beijing Health System (Discipline Backbone 2009-3-40 to K.Q.) and the Research Funds of Profession Quota Budget from the Beijing Municipal Science and Technology Commission (2012-bjsekyjs-2 to K.Q.).





Objective: To investigate the genome-wide promoter methylation and gene expression for the identification of methylation markers in obesity.
Methods: Using a high-fat, diet-induced obese mouse model, we performed comprehensive DNA methylation profiling of gene promoters to determine the differentially methylated genes using methylated DNA immunoprecipitation followed by hybridization to the NimbleGen MM8 CpG plus Promoter Microarray. We further integrated epigenomics data with gene expression profiling to identify promoters exhibiting an association between methylation status and the expression of downstream genes.
Results: A total of 24 hypermethylated promoters and 42 hypomethylated promoters in epididymal fat were selected as methylation markers, which were associated with downregulated and upregulated gene expression, respectively. The promoter methylation and differential gene expression of three markers (Mmp2, Foxj3 and Ube2q2) in the fat were validated by sequencing bisulfite-modified DNA and real-time reverse transcriptase PCR. The genes with these differentially methylated promoters and the associated transcriptional expression in the fat were primarily involved in biological activities in lipid metabolism and storage, cellular differentiation, immunity and the pathogenesis of obesity-related complications.
Conclusions: This study represents the first effort to determine methylation markers in obese mice that may regulate gene transcription in obesity. Our approach has potential relevance for clinical applications by identifying markers useful in elucidating the mechanisms of obesity pathogenesis and its complications.

Key words: Obesity. Genome-wide DNA methylation. Gene expression. High-fat diet. Mouse.


Objetivo: Investigar la metilación pangenómica del promotor y la expresión génica para la identificación de los marcadores de metilación en la obesidad.
Métodos: Empleando un modelo de ratón con obesidad inducida por la dieta con alto contenido en grasa, realizamos un perfil exhaustivo de la metilación del ADN de los genes promotores para determinar los genes metilados diferencialmente utilizando la inmunoprecipitación del ADN metilado seguida de la hibridación del NimbleGen MM8 CpG y el Promoter Microarray. Posteriormente, integramos los datos de la epigenómica con el perfil de expresión génica para identificar los promotores que mostraban una asociación entre el estado de metilación y la expresión de los genes sucesivos.
Resultados: Se seleccionó un total de 24 promotores hipermetilados y 42 promotores hipometilados en la grasa epididimaria como marcadores de la metilación, que se asociaron con la expresión génica regulada al alza y a la baja, respectivamente. La metilación del promotor y la expresión génica diferencial de tres marcadores (Mmp2, Foxj3 y Ube2q2) de la grasa se validaron mediante secuenciación del ADN modificado por bisulfito y por PCR de la transcriptasa reversa en tiempo real. Los genes con estos promotores metilados de forma diferencial y la expresión transcripcional asociada en la grasa estaban implicados primariamente en las actividades biológicas del metabolismo y almacenamiento de los lípidos, la diferenciación celular, la inmunidad y la patogenia de las complicaciones relacionadas con la obesidad.
Conclusiones: Este estudio representa el primer intento por determinar los marcadores de la metilación en los ratones obesos que pueden regular la transcripción génica en la obesidad. Nuestro abordaje tiene una relevancia potencial por sus aplicaciones clínicas al identificar marcadores útiles en la dilucidación de los mecanismos de la patogenia de la obesidad y sus complicaciones.

Palabras clave: Obesidad. Metilación pangenómica del ADN. Expresión génica. Dieta rica en grasa. Ratón.



In recent years, epigenetic modifications such as DNA methylation and histone modifications have been highlighted in chronic non-communicable diseases due to their role in chromatin structure and gene expression related to physiologic and pathologic processes and their potential use as markers for disease onset, progression, diagnosis and prognosis1-4. DNA methylation is the only known modification that targets the DNA itself. Compared to histones, which undergo a variety of post-translational modifications under different conditions, DNA methylation is relatively stable over a longer period. The methylation of cytosine residues at CpG dinucleotides in gene promoters or CpG islands is well described and known to have profound effects on the regulation of gene expression1,3,5.

Emerging evidence indicates that epigenetic changes are closely associated with obesity and related complications. The candidate gene methylation studies in animal models and humans have demonstrated methylation changes in promoters of varied genes implicated in obesity, appetite control and/or metabolism, insulin signaling, immunity, growth and circadian clock regulation6-9. Genome-wide analyses in human peripheral blood cells have detected obesity-associated differentially methylated sites of genes with a wide diversity of functions, such as immune response, cell differentiation and the regulation of transcription9. Additionally, the methylation profiles of obese individuals can be modified by reductions in body weight/fat mass. For example, promoter methylation levels of the leptin and tumor necrosis factor-alpha (TNF-α) genes may predict the susceptibility to weight loss as well as comorbidities such as hypertension or type 2 diabetes10,11. Thus, the role of epigenetic modifications in obesity is a fruitful area for further research. Thus far, the epigenome-wide association with gene expression in obesity has never been explored.

The methylation microarrays and high-throughput sequencing technologies allow for large-scale epigenome-wide association studies (EWASs) and the determination of epigenomic events in the development of metabolic diseases12. In the present study, we used the methylated DNA immunoprecipitation (MeDIP) approach, which generates an enrichment of methylated genomic fragments by means of an anti-5-methylcytosine antibody combined with the hybridization of the fragments to a whole-genome promoter array. We then performed the MEDME (modeling experimental data with MeDIP enrichment) postprocessing routine to determine the methylation levels in diet-induced obese (DIO) mice. Finally, an analysis of the potential methylation markers, along with additional gene expression experiments, revealed promoter features that appeared to be relevant for transcriptional regulation. The identification of methylation changes in specific genes will provide important targets for further study into the underlying mechanisms and the therapeutic potential for obesity and its comorbidities.


Materials and methods

Diets and animals

Based on the high-fat diet formula (D12492) for DIO mice from Research Diets Inc. (New Brunswick, NJ), a high-fat diet (HFD) (34.9% fat by wt., 60% kcal) with soy oil and lard oil as the main sources of fat was designed with a standard diet (SD) (4.3% fat by wt; 10% kcal) as a control. Forty male C57BL/6J mice at 3 to 4 weeks of age were obtained from the Laboratory Animal Center, Hospital 304 affiliated with the General Hospital of the People's Liberation Army in China. All mice were housed at the animal facilities in a temperature-, light- and humidity-controlled room with a 12-h (h) light/12-h dark cycle. After one week of recovery from transportation, mice were classified into two groups (20 mice in each group) and fed with one of the two types of diets for 4 months. The body weight was measured weekly to confirm that the HFD-fed mice gained more weight than the SD-fed mice. At the end of the experiment, fasted mice were anesthetized by an intraperitoneal injection of Avertin (2,2,2-tribro-moethanol, T-4840-2; Sigma-Aldrich Chemie GmbH, Steinheim, Germany; 125 mg/kg) to obtain blood samples by heart puncture. An evaluation of blood triglyceride levels and leptin concentration was performed at the end of the experiment to confirm that the HFD-fed mice developed an obese phenotype. The mice were then immediately euthanized by decapitation. The epididymal fat and the brain were dissected free of the surrounding tissue, immediately frozen in liquid N2 and stored at -80oC until analysis. All of the animal experiments were conducted in accordance with the guidelines of the Animal Care and Use Committee of Hospital 304 affiliated with the General Hospital of the People's Liberation Army in China.

Gene-expression microarrays and data analysis

Total RNA from the adipose tissue or the brain hypothalamus was quantified using a NanoDrop ND-1000 spectrophotometer, and the RNA integrity was assessed using standard denaturing agarose gel electrophoresis. For microarray analysis, an Agilent Array platform was employed. The sample preparation and microarray hybridization were performed based on the manufacturer's standard protocols. Briefly, 1 μg of total RNA from each sample was amplified and transcribed into fluorescent cRNA using Agilent's Quick Amp Labeling protocol (version 5.7, Agilent Technologies). The labeled cRNAs were hybridized onto the Whole Genome Oligo Array (4x44K, Agilent Technologies) with over 39,000 mouse genes and transcripts represented. After the slides were washed, the arrays were scanned with an Agilent Scanner G2505B.

Agilent Feature Extraction software (version was used to analyze the acquired array images. Quantile normalization and subsequent data processing were performed using the GeneSpring GX v11.5.1 software package (Agilent Technologies). After quantile normalization of the raw data, differentially expressed genes with statistical significance were identified through Volcano Plot filtering between the two groups, with a threshold of fold change at ≥ 1.5 and P ≤ 0.05. Pathway analysis and GO analysis were applied to determine the roles of these differentially expressed genes in these biological pathways or GO terms. Finally, hierarchical clustering was performed to determine the distinguishable gene expression pattern among samples.

DNA méthylation microarrays and data analysis Genomic DNA extraction

Genomic DNA (gDNA) was extracted from samples using a DNeasy Blood & Tissue Kit (Qiagen, Fremont, CA). A total of 10 μg gDNA of each sample was sonicated to approximately 200 - 1000 bp with a Bioruptor sonicator (Diagenode) on "Low" mode for 10 cycles of 30 seconds "ON" and 30 seconds "OFF." The gDNA and each sheared DNA were agarose analyzed.


Then, 1 μg of sonicated gDNA was used for immuno-precipitation using a mouse monoclonal anti-5-methylcytosine antibody (Diagenode). For this procedure, DNA was heat-denatured at 94oC for 10 min, rapidly cooled on ice and immunoprecipitated with 1 μL primary antibody overnight at 4oC with rocking agitation in 400 pL immunoprecipitation buffer (0.5% BSA in PBS). To recover the immunoprecipitated DNA fragments, 200 pL of anti-mouse IgG magnetic beads was added and incubated for an additional 2 h at 4oC with agitation. After immunoprecipitation, a total of five immunoprecipitation washes were performed with ice-cold immunoprecipitation buffer. The washed beads were resuspended in TE buffer with 0.25% SDS and 0.25 mg/mL proteinase K for 2 h at 65oC and then allowed to cool to room temperature. MeDIP DNA was purified using Qiagen MinElute columns (Qiagen).

Whole-genome amplification

The MeDIP-enriched DNA was amplified using a GenomePlex® Complete Whole Genome Amplification (WGA2) kit from Sigma-Aldrich. The amplified DNA samples were then purified with a QIAquick PCR purification kit (Qiagen).

DNA labeling and array hybridization

The NimbleGen Dual-Color DNA Labeling Kit (NimbleGen Systems, Inc., Madison, WI, USA) was used for labeling according to the manufacturer's guidelines. Briefly, 1 μg DNA of each sample was incubated for 10 min at 98oC with 1 OD of Cy3-9mer primer (IP sample) or Cy5-9mer primer (Input sample). Then, 100 pmol of deoxynucleoside triphosphates and 100 U of the Klenow fragment (New England Biolabs, USA) were added, and the mix was incubated at 37oC for 2 h. The reaction was halted by adding 0.1 volume of 0.5 M EDTA, and the labeled DNA was purified by isopropanol/ethanol precipitation and hybridized to genomic promoter arrays. For array hybridization, a NimbleGen MM8 CpG plus Promoter Microarray was used, which is a single array design containing all known CpG islands annotated by UCSC and all well-characterized RefSeq promoter regions (from approximately -1300 bp to +500 bp of the TSSs) entirely covered by approximately 385,000 probes. Scanning was performed with the Axon GenePix 4000B microarray scanner.

Raw data were extracted as pair files using NimbleScan software. We performed mediancentering, quantile normalization and linear smoothing using the Bioconductor software packages Ringo, limma and MEDME, respectively. After normalization, normalized log2-ratio data were created for each sample. From the normalized log2-ratio data, a sliding-window peak-finding algorithm provided by NimbleScan v2.5 aodrqEW (Roche-NimbleGen) was applied to determine the enriched peaks with specified parameters (sliding window width: 750 bp; miniprobes per peak: 2; P value minimum cutoff: 2; maximum spacing between nearby probes within peak: 500 bp). The identified peaks were mapped to the following genomic features: transcripts and CpG Islands.

Promoters or regions were further classified into three categories to distinguish strong CpG islands, weak CpG islands and sequences with a low abundance of CpG sites13. High CpG promoters/regions (HCP) contain a 500-bp sequence with GC content above 55%, a CpG ratio above 0.75 and a CpG observed to expected ratio greater than 0.6. Intermediate CpG promoters/regions (ICP) contain a region below 500 bp and have moderate CpG richness (GC content less than 55%) with a CpG ratio between 0.48 and 0.75 and a CpG observed to expected ratio between 0.4 and 0.6. Low CpG promoters/regions (LCP) do not have a region of 500 bp with a CpG ratio above 0.48 or a CpG observed to expected ratio above 0.414.

Identification of biomarkers

Overlapping between gene expression and DNA methylation was conducted using the R gplots package ( to screen the possible biomarkers involved in the pathogenesis of obesity. Only promoters whose downstream gene was differentially expressed at least 1.5-fold (P < 0.05) through Volcano Plot filtering were considered. For the last filter, methylation markers in adipose tissue of DIO mice were required to display an inverse relationship to gene expression, i.e., hyper- and hypomethylated states being down- and upregulated, respectively.

Sequencing of bisulfite-modified DNA

The methylation levels of the selected site-specific CpG sites from the three genes (Ube2q2, Foxj3 and Mmp2) were determined by bisulfite sequencing. The target regions of the relevant genes and the primers used for amplification are listed in table I. Briefly, DNA from the epididymal fat was isolated and purified with the DNA Purification Kit (cat. no. dN 1008, Biofuture Group Inc., Beijing, China) and modified with bisulfate using the Methylamp TM DNA Modification Kit (cat. no. P-1001, Epigentek Group Inc., Brooklyn, NY). The methyl-modified DNA was amplified by nested PCR, and products were sequenced directly. DNA methylation was calculated from the amplitude of cytosine and thymine within each CpG dinucleotide, C/(C+T), as described by Lewin et al. 15.

Gene expression by quantitative RT-PCR

Total RNA was extracted from mouse epididymal fat using TRIzol Reagent (cat. no. 15596-026, Invitrogen, Carlsbad, CA, USA), and cDNA was prepared from the total RNA using the SuperScriptTM III First-Strand Synthesis System for RT-PCR (cat. no. 18080-051, Invit-rogen, Carlsbad, CA, USA) according to the procedures provided by the manufacturer. The genes' mRNA levels in the fat were measured using real-time quantitative RT-PCR with an ABI PRISM 7300 sequence detection system (Applied Biosystems, Foster City, CA, USA).

The oligonucleotide primers were designed with the assistance of PrimerExpress software: Ube2q2 (NM_ 180600) (Forward: GACCAAACCTGCTT-CCTG; Reverse GTGCCAACCATTCCATAA), Foxj3 (NM_172699) (Forward: AGGTCCTCAG-TAGTTTGC; Reverse: AGACTGCGATACTC-CAAC), Mmp2 (NM_008610) (Forward: CCCC-GATGCTGATACTGA; Reverse: CTGTCCGCCA-AATAAACC), beta-actin (NM_007393) (forward: ACCAGTTCGCCATGGATGAC; reverse: TGCCG-GAGCCGTTGTC). The co-amplification of mouse beta-actin mRNA, an invariant internal control, was performed in all the samples. The assays were performed in triplicate, and the results were normalized to the betaactin mRNA levels using the 2ACT method.

Statistical analysis

All statistical analyses for gene expression microarray and DNA methylation microarray were conducted with R statistical packages v2.15.0 ( and SPSS (Statistical Package for the Social Sciences) version 11.5 for Windows. For site-specific CpG sites from the three genes, Student's f-test was used to investigate whether their methylation or expression levels differ between the obese and the lean group. The two-sided level of significance was set at P < 0.05.



Differences in promoter methylation between obese mice and lean mice

DNA methylation was determined using MeDIP followed by hybridization to the NimbleGen MM8 CpG plus Promoter Microarray, which probes 385,000 RefSeq mouse promoters. In adipose tissues between the obese mice and the control mice, 626 differentially methylated promoters were found, with 277 gene promoters hypermethylated and 349 hypomethylated in obese mice. In the brain hypothalamus of obese mice, 646 differentially methylated promoters were found, with 332 hypermethylated and 314 hypomethylated compared to that of control mice (table II).

Identification of gene methylation markers in obesity

Gene expression was evaluated for each methylation profile to assess the promoter methylation relevant to transcriptional repression. An analysis of the gene expression microarray showed that compared to the lean mice, there were 3284 genes transcriptionally upregulated and 3390 genes downregulated in the adipose tissue; in the brain, 275 genes were transcriptionally upregulated and 120 genes were downregulated. An overlapping analysis of gene expression and promoter methylation provided a list of differently methylated genes whose transcriptional expressions were associated with their promoter methylation profiles in the adipose tissue of DIO mice compared to the control mice (table III and table IV). A total of 24 hypermethylated promoters and 42 hypomethylated promoters in adipose tissue were selected as methylation markers. In the brain hypothalamus, only the promoter of gene tmem204 was hypermethylated and that of gene olfr485 was hypomethylated, which were associated with downregulated and upregulated gene expression, respectively.

Then, gene expression was evaluated for each methylation profile to assess promoter CpG content relevant to transcriptional repression. The results indicated that the expression levels of genes under the control of HCPs is negatively related to the promoter methylation levels (trend P value 0.009), whereas no significant correlation was found between the methylation levels of ICPs or LCPs and gene transcriptional expression (P = 0.364 and 0.677, respectively) (fig. 1). These filters increased the likelihood of identifying promoters with a dysregulated methylation pattern causally related to the differential expression of downstream genes.

GO and Pathway analysis indicated the specific roles of these differentially methylated and expressed genes in the biological pathways or GO terms. The genes with hypermethylated promoters and underexpression were potentially relevant to metabolic processes (GO: 0008152) including lipid metabolism and storage (Abhd5, 4921517L17Rik); the insulin-like growth receptor signaling pathway (Atxn7); obesity-linked diseases such as diabetes mellitus (Foxj3, Hmg20a) and thrombosis (Zfp161); cellular processes (GO: 0009987) including protein modification, such as lipidation and ubiquitination (Atxn7, Hecw2, Ube2q2, Dpm1); nuclear import/export pathways (Xpo4, Npm1) and transcription activator/repressors (Cebpd, Mamstr); cellular component organization (GO: 0071840), including nucleosome and cell junction assembly (Nap111, Pard3), spliceosome (Clk4), telomere organization and maintenance (Tnks2) and microtubule cytoskeleton organization (Npm1); and growth (GO: 0040007), including cell differentiation of the epidermis, lung alveolus, neural crest cell and bone (Lamas, Npm1, Errfil, Co19a1); and gland morphogenesis (Lama5). The genes that were hypomethylated in promoters with transcriptional overexpression were associated with metabolic processes (GO: 0008152), including lipid metabolism (Asah1, Acaa2); biological regulation (GO: 0065007), including the positive regulation of fat cell differentiation (Sh3pxd2b) and the negative regulation of T and B cell proliferation and immunity (Pawr, Trpm4, Usp25); signaling (GO: 0023052), including the insulin-like growth receptor signaling pathway (wdr11) and the TLR4-dependent innate immune response (Usp25); response to stimulus (GO: 0050896), including the response to estradiol stimulus (Mmp2), macrophage colony stimulating factor (csflr), tumor from hypoxia (Car9) and ischemic stroke (Arl6ip6); and localization (GO: 0051179), including activating delayed rectifier K+ current (Kcnh2) and the G2/M transition checkpoint (Fzr1).

Further validation of promoter methylation and gene expression

Three genes (Ube2q2, Foxj3 and Mmp2) were chosen for further validation of promoter methylation and gene expression in the adipose tissue. The methylation status of the promoters of these three genes was evaluated by sequencing the bisulfite-converted DNA. An increased total averaged methylation level of CG sites was found in promoters of the Ube2q2 and Foxj3 genes in DIO mice compared to the control mice. The total averaged CG site methylation level in the Mmp2 promoter was significantly lower in DIO obese mice than in the control mice (fig. 2). Quantitative real-time RT-PCR validated the inverse correlation between gene expression (fig. 3) and promoter methylation.



Recent studies have demonstrated that DNA methylation in promoters of varied genes is closely associated with obesity and its complications6-9. In the present study, we performed comprehensive DNA methylation profiling of gene promoters in DIO mice and control lean mice to determine the differentially methylated genes using MeDIP followed by hybridization to the NimbleGen MM8 CpG plus Promoter Microarray. We further integrated epigenomic data with gene expression profiling to identify promoters that exhibited an association between methylation status and the expression of downstream genes. We identified 66 methylation markers in the adipose tissue of DIO mice, of which 24 were hypermethylated and 42 were hypomethylated.

Previous experiments have indicated that the relationship between promoter methylation and gene expression is related to the CpG density of the promoter14. To examine this relationship, we divided promoters into three types according to CpG content: high CpG promoters (HCPs), low CpG promoters (LCPs) and intermediate CpG promoters (ICPs). A stronger correlation was shown between DNA methylation levels and transcriptional repression in HCPs compared with ICPs and LCPs. Weber et al. reported that LCPs show no significant correlation between gene activity and the abundance of methylated cytosines, implying that low concentrations of methylated cytosines do not preclude gene activity, whereas the activity of ICPs and HCPs was negatively correlated with their DNA methylation status14.

The GO and Pathway analyses suggested that the genes with differentiated methylation and transcriptional expression were associated with biological activities in lipid metabolism and storage, adipocyte differentiation, immunity and the pathogenesis of obesity-related complications. However, the epigenetic control and involvement in obesity have not yet been established for any of the methylation markers found in this study. Interestingly, among these markers, there were 11 hypomethylated and 13 hypermethylated promoters that have been previously studied, almost all of which are involved in the pathogenesis of cancers such as follicular lymphoma and Burkitt's lymphoma, breast cancer, neuroblastoma, myeloid leukemia and others16-18. This finding implies that these differentially methylated genes most likely play important roles in the abnormal adipocyte proliferation and differentiation in obese individuals, which should be further studied.

We assessed the methylation status of Mmp2, Foxj3 and Ube2q2 in the adipose tissue of DIO mice and found a significant methylation change between the obese and control groups. The protein encoded by the Mmp2 gene is a matrix metalloproteinase, which degrades type IV collagen, the component of the basement cellular membrane, and is essential for proper extracellular matrix remodeling in adipose tissue19. It has been reported that Mmp2 may facilitate the hypertrophic development of adipocytes, the formation of adipose clusters20, and the early phase of murine preadipocyte differentiation21. Moreover, preadipocyte treatment with MMP inhibitors or antibodies markedly decreases adipocyte differentiation by inhibiting the lipogenesis of triglycerides and its hydrolysis22. In addition, Mmp2 is strongly upregulated by macrophage-secreted factors, inducing a major inflammatory response in human adipocytes23. In the current study, the methylation fraction of the Mmp2 promoter was reduced with the correlated upregulation of transcription in DIO mice, which is consistent with the findings that the increased expression of Mmp2 is associated with overweight/obese subjects24,25.

The product of the Foxj3 gene is a forkhead/winged helix transcription factor that functions to regulate cellular fate determination, proliferation and differentiation26. Foxj3 may regulate a network of zinc finger proteins (ZFPs)27, which is a key transcriptional regulator involved in adipocyte proliferation and differentiation28. Additionally, Foxj3 activates the transcriptional expression of the Mef2c (myocyte enhancer factor) gene involved in adipogenesis29. Grant et al. reported that the knockdown of Foxj3 and Foxkl eliminated cell cycle-dependent oscillations and resulted in decreased cell proliferation rates30. In response to feeding with a high-calorie diet, Foxj3 expression is downregulated, with a concomitant decrease in Mef2c expression31. In keeping with this finding, we found that high-fat feeding to mice down-regulated the expression of Foxj3, most likely by enhancing the methylation level of the Foxj3 promoter.

The Ube2q2 protein, one of the functional ubiquitin-conjugating enzymes, is responsible for the intermediate phase of the ubiquitination pathway, which is fundamental in the regulation of protein stability and turnover. In adipocytes, the ubiquitin-dependent proteasomal pathway controls several highly regulated proteins, such as fat-specific protein 27 (Fsp27), peroxisome proliferator-activated receptor-gamma (Ppargamma) and pigment epithelium-derived factor (Pedf), which participate in lipid storage, adipocyte development and insulin sensitivity, respectively32-34. Transcriptome analysis has demonstrated that the expression of several genes in the hypothalamus, including Ube2q2, is modulated by high fat intake35. In the present study, our findings showed that the expression of Ube2q2 in the adipose tissue of DIO mice was decreased, which may be attributable to the higher methylation of its promoter.

In summary, we have shown that the integration of high-throughput data from the genome-wide integrative analysis of promoter methylation and gene expression in DIO mice, compared to the lean controls, can identify new DNA methylation markers related to obesity. The aberrant methylated promoters reported here have potential relevance in clinical applications as markers for elucidating the mechanisms of obesity pathogenesis (abnormal lipid metabolism, storage and adipocyte proliferation and differentiation, etc.) and its complications.


Conflict of Interest

There are no competing financial interests related to the work described.



1. Choi SW and Friso S. Epigenetics: a new bridge between nutrition and health. Adv Nutr 2010; 1 (1): 8-16.         [ Links ]

2. Burdge GC, Lillycrop KA. Bridging the gap between epigenetics research and nutritional public health interventions. Genome Med 2010; 2: 80.         [ Links ]

3. Gabory A, Attig L, Junien C. Developmental programming and epigenetics. Am J Clin Nutr 2011; 94: 1943S-1952S.         [ Links ]

4. Esteller M. Relevance of DNA methylation in the management of cancer. Lancet Oncol 2003; 4: 351-8.         [ Links ]

5. Blackledge NP, Klose R. CpG island chromatin: a platform for gene regulation. Epigenetics 2011; 6: 147-52.         [ Links ]

6. Okamura M, Inagaki T, Tanaka T, Sakai J. Role of histone methylation and demethylation in adipogenesis and obesity. Organogenesis 2010; 6: 24-32.         [ Links ]

7. Pinnick KE, Karpe F. DNA methylation of genes in adipose tissue. Proc Nutr Soc 2011; 70: 57-63.         [ Links ]

8. Drummond EM1, Gibney ER. Epigenetic regulation in obesity. Curr Opin Clin Nutr Metab Care 2013; 16 (4): 392-7.         [ Links ]

9. Van Dijk SJ, Molloy PL, Varinli H, Morrison JL, Muhlhausler BS; members of EpiSCOPE. Epigenetics and human obesity. Int J Obes (Lond) 2014 Feb 25. doi: 10.1038/ijo.2014.34. (Epub ahead of print).         [ Links ]

10. Cordero P, Campion J, Milagro FI, Goyenechea E, Steemburgo T, Javierre BM, Martinez JA. Leptin and TNF-alpha promoter methylation levels measured by MSP could predict the response to a low-calorie diet. J Physiol Biochem 2011; 67 (3): 463-70.         [ Links ]

11. Campión J, Milagro FI, Goyenechea E, Martínez JA. TNF-alpha promoter methylation as a predictive biomarker for weight-loss response. Obesity (Silver Spring) 2009; 17 (6): 1293-7.         [ Links ]

12. Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat Rev Genet 2011; 12 (8): 529-41.         [ Links ]

13. Takai D, Jones PA. Comprehensive analysis of CpG islands in human chromosomes 21 and 22. Proc Natl Acad Sci USA 2002; 99: 3740-5.         [ Links ]

14. Weber M, Hellmann I, Stadler MB, Ramos L, Paabo S, Rebhan M, Schubeler D. Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat Genet 2007; 39: 457-66.         [ Links ]

15. Lewin J, Schmitt AO, Adorján P, Hildmann T, Piepenbrock C. Quantitative DNA methylation analysis based on four-dye trace data from direct sequencing of PCR amplificates. Bioinformatics 2004, 20: 3005-12.         [ Links ]

16. Bethge N1, Honne H, Hilden V, Trøen G, Eknæs M, Liestøl K, Holte H, Delabie J, Smeland EB, Lind GE. Identification of highly methylated genes across various types of B-cell non-hodgkin lymphoma. PLoS One 2013; 8 (11): e79602.         [ Links ]

17. Yang K1, Shen J, Xie YQ, Lin YW, Qin J, Mao QQ, Zheng XY, Xie LP. Promoter-targeted double-stranded small RNAs activate PAWR gene expression in human cancer cells. Int J Biochem Cell Biol 2013; 45 (7): 1338-46.         [ Links ]

18. Carén H1, Fransson S, Ejeskär K, Kogner P, Martinsson T. Genetic and epigenetic changes in the common 1p36 deletion in neuroblastoma tumours. Br J Cancer 2007; 97 (10): 1416-24.         [ Links ]

19. Dalamaga M, Diakopoulos KN, Mantzoros CS. The role of adiponectin in cancer: a review of current evidence. Endocr Rev 2012; 33 (4): 547-94.         [ Links ]

20. DiMascio L, Voermans C, Uqoezwa M, Duncan A, Lu D, Wu J, Sankar U, Reya T. Identification of adiponectin as a novel hemopoietic stem cell growth factor. J Immunol 2007; 178 (6): 3511-3520.         [ Links ]

21. Yokota T, Meka CS, Medina KL, Igarashi H, Comp PC, Takahashi M, Nishida M, Oritani K, Miyagawa J, Funahashi T, Tomiyama Y, Matsuzawa Y, Kincade PW. Paracrine regulation of fat cell formation in bone marrow cultures via adiponectin and prostaglandins. J Clin Invest 2002; 109 (10): 1303-10.         [ Links ]

22. Bouloumié A, Sengenès C, Portolan G, Galitzky J, Lafontan M. Adipocyte produces matrix metalloproteinases 2 and 9: involvement in adipose differentiation. Diabetes 2001; 50 (9): 2080-6.         [ Links ]

23. O'Hara A, Lim FL, Mazzatti DJ, Trayhurn P. Microarray analysis identifies matrix metalloproteinases (MMPs) as key genes whose expression is upregulated in human adipocytes by macrophage-conditioned medium. Pflugers Arch 2009; 458 (6): 1103-14.         [ Links ]

24. Morgan AR, Han DY, Thompson JM, Mitchell EA, Ferguson LR. Analysis of MMP2 promoter polymorphisms in childhood obesity. BMC Res Notes. 2011; 4: 253.         [ Links ]

25. Han DH, Kim SK, Kang S, Choe BK, Kim KS, Chung JH. Matrix Metallopeptidase 2 Gene Polymorphism is Associated with Obesity in Korean Population. Korean J Physiol Pharmacol 2008; 12 (3): 125-9.         [ Links ]

26. Cao JJ. Effects of obesity on bone metabolism. J Orthop Surg Res 2011; 6: 30.         [ Links ]

27. Cao JJ, Sun L, Gao H. Diet-induced obesity alters bone remodeling leading to decreased femoral trabecular bone mass in mice. Ann N Y Acad Sci 2010; 1192: 292-7.         [ Links ]

28. Wei S, Zhang L, Zhou X, Du M, Jiang Z, Hausman GJ, Bergen WG, Zan L, Dodson MV. Emerging roles of zinc finger proteins in regulating adipogenesis. Cell Mol Life Sci 2013; 70 (23): 4569-84.         [ Links ]

29. Sharma G, Goalstone ML. Dominant negative FTase (DNFTalpha) inhibits ERK5, MEF2C and CREB activation in adipogenesis. Mol Cell Endocrinol 2005; 245 (1-2): 93-104.         [ Links ]

30. Grant GD, Gamsby J, Martyanov V, Brooks L 3rd, George LK, Mahoney JM, Loros JJ, Dunlap JC, Whitfield ML. Live-cell monitoring of periodic gene expression in synchronous human cells identifies Forkhead genes involved in cell cycle control. Mol Biol Cell 2012; 23 (16): 3079-93.         [ Links ]

31. Alexander MS, Shi X, Voelker KA, Grange RW, Garcia JA, Hammer RE, Garry DJ. Foxj3 transcriptionally activates Mef2c and regulates adult skeletal muscle fiber type identity. Dev Biol 2010; 337 (2): 396-404.         [ Links ]

32. Nian Z, Sun Z, Yu L, Toh SY, Sang J, Li P. Fat-specific protein 27 undergoes ubiquitin-dependent degradation regulated by tria-cylglycerol synthesis and lipid droplet formation. J Biol Chem 2010; 285 (13): 9604-15.         [ Links ]

33. Kilroy GE, Zhang X, Floyd ZE. PPAR-gamma AF-2 domain functions as a component of a ubiquitin-dependent degradation signal. Obesity (Silver Spring) 2009; 17 (4): 665-73.         [ Links ]

34. Dai Z, Qi W, Li C, Lu J, Mao Y, Yao Y, Li L, Zhang T, Hong H, Li S, Zhou T, Yang Z, Yang X, Gao G, Cai W. Dual regulation of adipose triglyceride lipase by pigment epithelium-derived factor: A novel mechanistic insight into progressive obesity. Mol Cell Endocrinol 2013; 377 (1-2): 123-34.         [ Links ]

35. De Giorgio MR, Yoshioka M, St-Amand J. Feeding induced changes in the hypothalamic transcriptome. Clin Chim Acta 2009; 406 (1-2): 103-7.         [ Links ]



Kemin Qi.
Nutrition Research Unit.
Beijing Pediatric Research Institute.
Beijing Children's Hospital.
Capital Medical University.
No. 56 Nan-li-shi Road.
100045 Beijing. China.

Recibido: 15-IV-2014.
Aceptado: 25-V-2014.

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