Skip to main content
Download PDF

Abnormal DNA methylation of EBF1 regulates adipogenesis in chicken

BMC Genomics volume 26, Article number: 275 (2025) Cite this article

Abstract

Background

DNA methylation influences gene expression and is involved in numerous biological processes, including fat production. It is involved in lipid generation in numerous animal species, including poultry. However, the effect of DNA methylation on adipogenesis in chickens remains unclear.

Results

A total of 12 100-day-old chickens were divided into high and low-fat groups based on their abdominal fat ratios. Subsequently, genome-wide bisulfite sequencing (WGBS) was performed on their abdominal fat, and 1877 differentially methylated region (DMR) genes were identified, among which SLC45A3, EBF1, PLA2G15, and ACAD9 were associated with lipid metabolism. Interestingly, EBF1 showed a lower level of DNA methylation and higher mRNA expression in the low-fat group, as determined by comprehensive RNA-seq analysis. Cellular verification showed that EBF1 expression was upregulated by 5-azacytidine (5-Aza) and downregulated by betaine. EBF1 facilitated the differentiation of immortalized chicken preadipocyte 1 (ICP-1) through the PPAR-γ pathway, thereby affecting chicken adipogenesis.

Conclusion

A combination of WGBS and RNA-seq analyses revealed 48 DMGs in the abdominal fat tissue of chickens. Notably, the DNA methylation status of EBF1 was inversely related to its mRNA expression. Mechanistically, DNA methylation regulates EBF1 expression, which in turn mediates the differentiation of ICP-1 through the PPARγ pathway. This study provides a theoretical framework for investigating the effects of DNA methylation on adipogenesis in chickens.

Peer Review reports

Background

The growing concern about obesity as a global health issue has been exacerbated by its increasing prevalence [1, 2]. According to statistics, in 2015, 107.7 million children and 603.7 million adults were obese [3]. The prevalence of obesity has doubled in over 70 countries since 1980, with a continuous upward trend observed in the majority of other countries [4]. Obesity has been identified as a warning signal for a range of diseases, including diabetes [5, 6], cardiovascular disease-related chronic diseases [7,8,9], osteoarthritis [10, 11], and even cancer [12, 13] The formation and deposition of fat involves a variety of factors, including alterations in gene expression and pathway network regulation related to lipogenesis and metabolism [14].

Adipose tissue plays a pivotal role in lipid deposition and metabolism, serving not only as an energy reserve but also as a vital endocrine and metabolic organ [15, 16]. Epigenetic modification is a significant factor in cell differentiation, development, gene expression regulation, and disease occurrence [17,18,19]. DNA methylation represents a form of epigenetic modification, and an increasing number of studies have demonstrated that DNA methylation plays a pivotal role in the process of adipose tissue formation and deposition in chickens [20,21,22,23,24]. For example, studies comparing the whole genome methylation of the backfat tissue of Landrace pigs (leaner) and Rongchang pigs (fatty) revealed, that the promoters of PLIN1, BDKRB2, NSDHL, APOL1 and APOL4 in lean backfat tissue were hypermethylated (93.75%) [25]. It has been demonstrated that DNA methylation in the PLIN1 promoter region regulates PLIN1 expression in chickens [26]. Zhang et al. demonstrated that the transcription of KLF7 in chicken abdominal adipose tissue may be inhibited by promoter DNA methylation [27]. Cui et al. have demonstrated that P1 methylation enhances PPARγ1 expression, at least in part, by impeding the binding of NRF1 to the P1 promoter [28]. In poultry, approximately 85% of commercial broilers accumulate excess adipose tissue, particularly abdominal fat, which negatively impacts the efficiency of chicken production [29]. The public tends to prefer low-fat chickens because a high-fat chicken diet can encourage weight gain and pose a threat to public health. Therefore, research on obesity in chicken models serves two purposes, to gain new insights into the mechanism of fat deposition and to provide possible remedies to the public obesity problem. Regarding meat quality traits, some researchers have combined WGBS and RNA-seq to identify differences in chicken intramuscular fat [30], but no similar studies have been conducted to investigate the impact of DNA methylation of EBF1 on chicken adipogenesis.

The transcription factor EBF1 is a key regulator of B cell development and maturation [31]. Furthermore, studies have demonstrated that EBF1 is a pivotal factor in the regulation of mature adipocyte metabolism and the development of brown fat [32, 33]. However, the precise role of DNA methylation of EBF1 in the deposition of abdominal fat in chickens remains uncertain. This study aimed to explore the role of DNA methylation in abdominal fat deposition in chickens using WGBS. The present study demonstrates, for the first time that DNA methylation and EBF1expression are associated with fat deposition. The combined analysis of RNA-seq and WGBS revealed that EBF1 had a higher degree of DNA methylation modification and lower mRNA expression in the high-fat group. Sequencing data demonstrated the potential role of DNA methylation of EBF1 in abdominal fat deposition in chickens and provided a foundation for further studies on the genetic regulation mechanism of fat deposition.

Results

Output and quality control of WGBS data for abdominal fat tissue

To elucidate the epigenetic mechanism underlying abdominal fat deposition, abdominal fat tissues from six high-fat (HF) and low-fat (LF) groups were collected for WGBS. A total of 233.5G (average of 38.9G per sample) and 245.9G (average of 41.0G per sample) raw data were generated in the two groups of WGBS (Table 1). After quality control, the Q30 value was > 0.86. Unique mapped reads of the HF and LF groups accounted for 74% and 73.8% of the chicken genome, respectively. The average sequencing depth was approximately 30× (Fig. 1A). The effective coverage of CpG sites was approximately 80% (Fig. 1B and Supplementary File 1: Tables S1, and S2). These findings suggested the reliability of the sequencing outcomes.

Table 1 The summary of data generated by genome-wide bisulfite sequencing
Full size table
Fig. 1
figure 1

Quality analysis of DNA methylation data in chicken between the high-fat and low-fat groups. (A) The average sequencing depth. (B) The effective coverage of CpG sites

Full size image

Global DNA Methylation Patterns in high and low-fat chickens

In LF chicken abdominal fat tissue, the number of cytosines in the CHG and CHH hypermethylated states (50-100%) was greater than that in HF chicken, whereas the opposite was true in the hypomethylated state (10-50%). The number of CG-type cytosine in HF chicken abdominal fat tissue containing “bumps” in the region close to 100% methylation, was significantly higher than that in the LF (Fig. 2A). The total DNA methylation levels of cytosine (C) in the HF and LF chickens were 3.35% and 3.40%, respectively (Table 2). In HF broilers, 58.40% CG, 0.67% CHG, and 0.68% CHH were methylated, whereas in LF broilers, 58.95% CG, 0.65% CHG, and 0.65% CHH were methylated. A comparison of the overall methylcytosine content of HF and LF indicates that LF exhibits a higher level of methylation than HF. However, the proportion of CG, CHG, and CHH in HF and LF is found to be similar. (Figure 2B and C). The results of the motif analysis indicate that there was no preference for bases near methylated cytosines between HF and LF. However, the methylated cytosines exhibited a preference for being located in CHG and CHH (H = A > T). (Fig. 2D).

Table 2 Total methylation level statistics of abdominal lipid genome
Full size table
Fig. 2
figure 2

Preference for methylated cytosine between the High-fat and Low-fat groups. (A)The number of cytosine analyses of CG, CHG, and CHH. (B, C) Analysis of the proportion of CG, CHG, and CHH. (D) Markedly different motif types in the High-fat group and the Low-fat groups

Full size image

Functional characterization of differentially methylated genes (DMGs)

A total of 1877 differentially methylated regions (DMRs) were identified (Supplementary File 1: Table S3). The circos map displays the P-values of the DMRs, the number of DMRs, and the average methylation levels of differences in the chicken chromosomes (1–33 and Z) (Fig. 3A). A statistical analysis of the location of DMRs in adipose tissue revealed that the majority of DMRs are situated in intron and distal intergenic regions, followed by exon. A small number of DMRs were also identified in other regions (Fig. 3B). Of these DMRs, 56.05% were hypomethylated and 43.95% were hypermethylated in HF chicken abdominal fat tissue. The average length of DMRs was approximately 200 bp (Fig. 3D). In order to investigate the potential biological role of DMGs, a total of 1455 DMGs were analyzed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The findings of this study revealed that the DMGs were primarily enriched in integral components of the membrane, the nucleus, ATP binding, metabolic pathways, calcium signaling pathways, mitogen-activated protein kinase (MAPK) signaling pathways, the regulation of actin cytoskeleton, and the Wnt signaling pathway (Fig. 3C and E). In addition, several fat-related pathways were enriched (Supplementary File 1: Table S4). Among these, SLC45A3, EBF1, PLA2G15, and ACAD9 were found to be associated with lipid metabolism.

Fig. 3
figure 3

Functional analysis of differentially methylated genes. (A) The distribution of DMRs on chromosomes (B) Location analysis of differentially methylation regions. (D) The average length of DMGs. (C, E) The GO enrichment analysis and KEGG pathways of DMGs

Full size image

Comprehensive analysis of RNA-Seq and WGBS

To explore whether the candidate DMGs were related to abdominal fat deposition, we comprehensively analyzed the WGBS and RNA-Seq data obtained from the same sample in our previous study [34]. RNA-Seq results showed that many lipid-related genes, such as LPIN1, MOGAT2, DGAT2, ABHD5, ACBD5, and FABP1 were differentially expressed. Our comprehensive analysis revealed 12 (hypermethylated and significantly down-regulated) and 22 (hypomethylated and significantly up-regulated) DMR genes in the abdominal fat tissue of HF and LF groups (Fig. 4A). A cluster analysis of differentially methylated differential genes revealed that 26 genes were up-regulated and 22 genes were down-regulated in HF groups (Fig. 4B). GO enrichment analysis of differential genes with differential DNA methylation was performed. A review of the top ten enrichment items reveals that the primary enrichment is observed in metal ion binding, positive regulation of transcription, and positive regulation of transcription (Fig. 4C). Protein-protein interaction (PPI) network analysis revealed a strong correlation between these genes (Fig. 4D). Studies have shown that EBF1 is associated with fat development [32, 33]. In the HF group, EBF1 methylation lever exhibited a significant decrease in methylation, while mRNA expression exhibited a significant increase.

Fig. 4
figure 4

Combined analysis of RNA-seq and WGBS. (A) Four quadrants between differential expression genes and DMGs. (B) A cluster analysis of differential genes with differential DNA methylation was conducted. (C) The GO was performed for differential genes with differential DNA methylation. (D) PPI analysis was performed on differential genes with differential DNA methylation

Full size image

Methylation inhibits EBF1 expression and EBF1 inhibits preadipocyte the differentiation

Sequencing analysis revealed that EBF1 methylation was negatively correlated with its expression level. Following treatment of the cells with 5-Aza-dc, demethylation of the modification site was observed through Bisulfite treatment and Sanger sequencing (Fig. 5A). After the cells were treated with 5-Aza-dc and betaine, the qPCR results revealed that EBF1 expression was negatively correlated with the degree of methylation modification, and EBF1 expression decreased as the degree of methylation modification increased (Fig. 5B and C). Furthermore, the qPCR results revealed that EBF1 was differentially expressed in the HF and LF groups (Fig. 5D). EBF1 was enriched in PPARA activated genes. To further investigate the role of EBF1 in fat deposition, sodium oleate was used to induce ICP-1 differentiation, and EBF1 expression of mRNA and protein decreased following differentiation (Fig. 5E and F). Oil red staining revealed that EBF1 inhibited preadipocyte differentiation (Fig. 5G-I). In addition, qPCR results showed that the expression of differentiation-related genes PPARγ, C/EBPα, C/EBPβ, ADIPOR1, and LPL decreased after overexpression of EBF1 (Fig. 5J). At the protein level, EBF1 inhibited PPARγ expression, suggesting that EBF1 inhibited adipose differentiation through the PPARγ axis (Fig. 5K).

Fig. 5
figure 5

The DNA methylation of EBF1 affects adipogenesis differentiation. (A) DNA methylation levels of cells treated with 5-Aza-dc and DMSO. (B, C) Cells were treated with 5-Aza-dc and betaine to detect the relative expression of EBF1. (D) Relative expression of EBF1 in High-fat chicken and Low-fat chicken. (E, F) The expression of mRNA and protein in EBF1 before and after the differentiation of preadipocytes was investigated. (G) Detection of transfection efficiency of EBF1. (H, I) Oil red staining showed the influence of EBF1 on grease drops. (J) Relative mRNA expression of PPARγ, C/ EBPα, C/ EBPβ, ADIPOR1, and LPL in ICP-1 cells. (K) Western blot for PPARγ protein in ICP-1 cells after EBF1 transfection, full-length blots are presented in Supplementary file 2 A. Data presented as mean ± SEM, *P < 0.05; **P < 0.01; ns: no significance

Full size image

Discussion

Abdominal fat deposits have been linked to several disorders, and research has indicated that DNA methylation plays a significant role in the metabolism of adipose tissue and lipids in a range of animals, including chickens [25, 30]. In this study, WGBS sequencing revealed differential DNA methylation modifications, of EBF1. A combined analysis using previously studied RNA-seq data revealed that EBF1 had a higher level of DNA methylation in the low-fat group but lower mRNA expression. Based on sequencing data, we validated the cell function of EBF1 and found that DNA methylation affects its expression. 5-Aza-dc has been demonstrated to inhibit DNA methyltransferase and promotes EBF1 expression, whereas betaine inhibits EBF1 expression. Moreover, our findings indicate that EBF1 plays a regulatory role in the differentiation of precursor adipocytes through the PPAR-β signaling pathway.

To explore the relationship between DNA methylation and abdominal fat deposition in chickens, we compared whole-gene methylation in abdominal adipose tissues from the HF and LF groups. Our finding indicate that HF chickens exhibited lower overall methylation levels than LF chickens. This result contradicts Gong’s findings [35]. It has been demonstrated that the levels of DNA methylation of specific genes fluctuate with age [36, 37]. In the study conducted by Gong et al., chickens at seven weeks of age were utilized, whereas in the present experiment, chickens at 100 days of age were employed. It can therefore be hypothesized that the alterations in DNA methylation levels of specific genes resulting from the age discrepancy between the chickens may be the underlying cause of the observed overall difference. The DNA methylation pattern observed in this study was predominantly GC, which is in accordance with the findings of Luo et al. [38]. Additionally, the majority of the differential DMRs were identified within introns, a result that aligns with the conclusions of Lu et al. [39]. The study by Wang et al. demonstrated that DMRs are predominantly located at the 5’ end in liver lipid metabolism. In contrast, this study revealed that they are primarily situated within introns [40]. Given that DNA methylation is susceptible to environmental influences, Wang et al. employed a high-fat diet and a low-fat diet to construct their model. In contrast, this experiment employed a normal feeding regimen, which may be a contributing factor to the observed discrepancy in the predominant DMR locations.

EBF1 plays a pivotal role in regulating lipid metabolism [41,42,43]. Although previous studies have demonstrated that abnormal DNA methylation of EBF1 is associated with various diseases [43,44,45]. To date, no study has focused on its role in fat deposition. This study demonstrated for the first time that DNA methylation of EBF1 is associated with fat deposition. Abnormal DNA methylation of EBF1 was identified in the WGBS of abdominal adipose tissue in both high and low-fat groups, with a lower level of DNA methylation in the low-fat group. Treatment of ICP cells with 5-Aza-dc was observed to promote EBF1 expression, whereas treatment with betaine was seen to inhibit EBF1 expression. The experiments conducted by Napat Armartmuntree and colleagues demonstrated a negative correlation between DNA methylation of EBF1 and mRNA expression, which was consistent with the sequencing results of this experiment [46]. Studies have shown that EBF1 expression is linked to lipid metabolism and is enriched in the PPARγ pathway [47], Therefore, we investigated their biological roles. Using qPCR, oil red staining, and protein analysis, we found that EBF1 inhibited adipocyte differentiation via the PPARγ pathway. Yasmina et al. showed that miR-365-5p and miR-574-5p were highly expressed in obese adults and that miR-365-5p and miR-574-5p could target EBF1 and reduce its expression, which was consistent with our reduced expression of EBF1 in the high-fat chicken group [48]. Masaaki et al. showed that miR-33b affects the differentiation and development of porcine precursor adipocytes by decreasing the expression of EBF1, C/EBPα, and PPARγ lipid genes, which is contrary to our findings that EBF1 inhibits adipocyte differentiation via through the PPARγ pathway [49]. This could be attributed to species differences or to DNA methylation. Further investigation is required to determine the specific reasons for these differences.

Conclusion

In summary, our findings indicate that EBF1 expression and the level of DNA methylation differ in the low-fat group. In addition, cytological experiments revealed that DNA methylation of EBF1 affects EBF1 expression. EBF1 also regulates ICP-1 differentiation via the PPARγ pathway. The findings of this study provide a theoretical framework for investigating the effects of DNA methylation on adipogenesis.

Materials and methods

Animal experiment and ethics statement

The animals used in this study were supported by Wens. 158 Sanhuang broilers were put in a cage after birth and were fed ad libitum. When they reach the age of 100 days, they were euthanized by cervical dislocation. Weight and abdominal fat weight were measured. The abdominal fat tissues were collected and stored in a refrigerator at -80 °C. According to the abdominal fat rate (abdominal fat rate = abdominal fat weight/live weight*100%), Twelve individuals were selected and divided into high-fat group (HF) and low-fat group (LF), with 6 in each group. The measurement data of 12 individuals are shown in Table 3. The animal experiments conducted in this study were approved (license ID: SCAU#2017015; September 13, 2017) by the Animal Care Committee of South China Agricultural University.

Table 3 Abdominal fat rate of 12 experimental chickens
Full size table

Whole-genome bisulfite sequence (WGBS)

Twelve 100-day-old ad libitum Sanhuang broilers were euthanized, and their abdominal fat tissues were collected. DNA extracted from the 12 abdominal fat tissues, were sent to Annoroad Gene Technology (Beijing) Co., Ltd. for WGBS.

DNA extraction and total RNA extraction

The DNA was extracted using DNA extraction kit (TAKARA, Japan) according to the manufacturer’s instructions. MagZol Reagent (Magen, Guangdong, China) was used for extracting total RNA from abdominal fat tissues and cells according to the manufacturer’s instructions.

Bisulfite treatment and Sanger sequencing

Primers are designed by Methyl Primer Express v1.0. The DNA was extracted and amplified. Following the recovery of the PCR amplification products by cutting the glue, they were connected to the T vector with the use of a TA cloning kit. The connected products were then transformed into coated plates, after which the monoclonal plaque was sent to the sequencing department for sequencing. A total of 10 monoclones were selected from each plate for sequencing.

Reverse transcription and Real-time quantitative PCR (RT-qPCR)

To validate the gene expression changes, total RNA was reverse transcribed using PrimescriptTM RT Reagent Kit with gDNA Eraser (Takara, Japan), and qRT-PCR was performed with ChamQ Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China). qRT-PCR was performed using ABI QuantStudio 5 instrument (Thermo Fisher, NY, USA). Relative expression was calculated using GAPDH as a reference gene and comparative cycle threshold method (2−ΔΔCt). The primers used in qPCR were designed by Premier Primer 5.0 software and the primer sequences are listed in Supplementary file 1: Table S5.

Data analysis of differential methylation genes

The software and parameters used in the align of the WGBS data refer to the paper published by Krueger and Andrews [50]. GCF_016700215.2 is used as the reference genome (downloaded from https://download.cncb.ac.cn/assembly/ncbi/Animals/GCF/016/700/Gallus_gallus_chicken_bGalGal1.pat.whiteleghornlayer.GRCg7w_WZ_GCF_016700215.2/GCF_016700215.2_bGalGal1.pat.whiteleghornlayer.GRCg7w_genomic.fna.gz, and the matching annotation file is downloaded from https://download.cncb.ac.cn/assembly/ncbi/Animals/GCF/016/700/Gallus_gallus_chicken_bGalGal1.pat.whiteleghornlayer.GRCg7w_WZ_GCF_016700215.2/GCF_016700215.2_bGalGal1.pat.whiteleghornlayer.GRCg7w_genomic.gff.gz). Methylation level difference more than 40% and q value ≤ 0.05 were considered as the cut off differentially methylated C sites (DMC). DMR analysis was performed by R package methylKit.V.0.9.2 [51] and eDMR.V.0.5.1 [52], and the standard is that the coverage depth of a single site is at least 10 on the same genomic location in multiple samples, contains at least 4 CpGs and 1 DMC, and one area with an average differential methylation level greater than 25%. We define the DMGs as the genes that overlap with the location of DMR.

GO/KEGG enrichment analysis and PPI network analysis

We performed Gene Ontology (GO) analysis and KEGG enrichment analysis for DMGs. PPI analysis is carried out in STRING (https://string-db.org/).

Cell culture and cell transfection

ICP-1 was cultured in a mixture of DMEM and DMEM/ F12 (Gibco, CA, United States) mixed with 15% fetal bovine serum (Gibco, CA, United States) and 1% streptomycin/penicillin. And then put it in an incubator 37℃ and 5% CO2 (Invitrogen, CA, United States, United States). The plasmids transfection was performed according to Lipofectamine 3000 (Invitrogen, CA, United States) instructions. Transfection doses for 12-well plate was 1 µg/ well.

Plasmids construction

The complete CDS sequence of EBF1 (NM_001397656.1) was subcloned into the Nhe I and Kpn I sites of the pcDNA3.1 vector (Promega, WI, United States) by TSINGKE (Beijing, China), and then named pcDNA3.1-EBF1.

Oil red O staining

A medium with 15% fetal bovine serum and 0.2% oleic acid (Sigma, CA, United States) was used to induce preadipocyte differentiation after 6 h of transfection. 300 µL of 4% paraformaldehyde was used to fix the cells at room temperature for 30 min after induction for 48 h. Then, the Oil red staining test was carried out according to the Oil Red O Staining Solution (SolarBio, Beijing, China) instructions.

Western blotting

After 48 h of transfection, proteins were extracted from cells. Extract proteins from cells. Protein samples were isolated on 10% SDS-PAGE gel (EpiZyme, Shanghai, China) and transferred to a polyvinylidene fluoride (PVDF) membrane (Bio-Rad, CA, United States). The membrane was placed in a bag with anti-PPARγ (1: 1,000; bs-0530R, BIOSS), anti-EBF1 (1: 1,000; bs-12116R, BIOSS) and anti-GAPDH antibodies (1: 2,000; bsm-33033 M, BIOSS) at 4℃ for 12 h. The film can be exposed by inserting it into an X-ray clamp. Finally, ImageJ software was used to measure the gray value (image density) of the strip.

Other data analysis

The data represents mean ± SEM. The sample size of each experiment is at least three. Student’s t-test and ANOVA analysis were performed to determine the statistical significance of differences observed between groups. *P < 0.05; **P < 0.01; ns: no significance.

Data availability

Sequencing data submitted in the NCBI database of SRA. Accession link: http://www.ncbi.nlm.nih.gov/bioproject/942033.

Abbreviations

WGBS:

Genome-wide bisulfite sequencing

DMR:

Differentially methylated region

5-Aza:

5-azacytidine

ICP-1:

Immortalized chicken preadipocyte 1

EBF1:

EBF Transcription Factor 1

HF:

High-fat

LF:

Low-fat

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

DMGs:

Differentially methylated genes

SLC45A3:

Solute Carrier Family 45 Member 3

PLA2G15:

Phospholipase A2 Group XV

ACAD9:

Acyl-CoA Dehydrogenase Family Member 9

LPIN1:

Phosphatidate Phosphatase LPIN1

MOGAT2:

Monoacylglycerol O-Acyltransferase 2

DGAT2:

Diacylglycerol O-Acyltransferase 2

ABHD5:

Abhydrolase Domain Containing 5, Lysophosphatidic Acid Acyltransferase

ACBD5:

Acyl-CoA Binding Domain Containing 5

FABP1:

Fatty Acid Binding Protein 1

C/EBPα:

CCAAT Enhancer Binding Protein Alpha

PPARγ:

Peroxisome Proliferator-Activated Receptor Gamma

References

  1. Chandler M, Cunningham S, Lund EM, Khanna C, Naramore R, Patel A, Day MJ. Obesity and associated comorbidities in people and companion animals: A one health perspective. J Comp Pathol. 2017;156(4):296–309.

    Article  CAS  PubMed  Google Scholar 

  2. Bristow C, Meurer C, Simmonds J, Snell T. Anti-obesity public health messages and risk factors for disordered eating: a systematic review. Health Promot Int. 2020;35(6):1551–69.

    Article  PubMed  Google Scholar 

  3. Roberto CA, Swinburn B, Hawkes C, Huang TT, Costa SA, Ashe M, Zwicker L, Cawley JH, Brownell KD. Patchy progress on obesity prevention: emerging examples, entrenched barriers, and new thinking. Lancet. 2015;385(9985):2400–9.

    Article  PubMed  Google Scholar 

  4. Collaborators GBDO, Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, Lee A, Marczak L, Mokdad AH, Moradi-Lakeh M, et al. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017;377(1):13–27.

    Article  Google Scholar 

  5. Zerga AA, Bezabih AM, Adhanu AK, Tadesse SE. Obesity indices for identifying metabolic syndrome among type two diabetes patients attending their Follow-Up in Dessie referral hospital, North East Ethiopia. Diabetes Metab Syndr Obes. 2020;13:1297–304.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Qi W, Clark JM, Timme-Laragy AR, Park Y. Per- and polyfluoroalkyl substances and obesity, type 2 diabetes and Non-alcoholic fatty liver disease: A review of epidemiologic findings. Toxicol Environ Chem. 2020;102(1–4):1–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Singh GM, Danaei G, Farzadfar F, Stevens GA, Woodward M, Wormser D, Kaptoge S, Whitlock G, Qiao Q, Lewington S, et al. The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: a pooled analysis. PLoS ONE. 2013;8(7):e65174.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Emerging Risk Factors C, Wormser D, Kaptoge S, Di Angelantonio E, Wood AM, Pennells L, Thompson A, Sarwar N, Kizer JR, Lawlor DA, et al. Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies. Lancet. 2011;377(9771):1085–95.

    Article  Google Scholar 

  9. Pleis JR, Ward BW, Lucas JW. Summary health statistics for U.S. Adults: National health interview survey. Vital Health Stat. 2009;10 2010(249):1–207.

    Google Scholar 

  10. Jiang L, Tian W, Wang Y, Rong J, Bao C, Liu Y, Zhao Y, Wang C. Body mass index and susceptibility to knee osteoarthritis: a systematic review and meta-analysis. Joint Bone Spine. 2012;79(3):291–7.

    Article  PubMed  Google Scholar 

  11. Jiang L, Rong J, Wang Y, Hu F, Bao C, Li X, Zhao Y. The relationship between body mass index and hip osteoarthritis: a systematic review and meta-analysis. Joint Bone Spine. 2011;78(2):150–5.

    Article  PubMed  Google Scholar 

  12. Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K. International agency for research on cancer handbook working G: body fatness and cancer–Viewpoint of the IARC working group. N Engl J Med. 2016;375(8):794–8.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Avgerinos KI, Spyrou N, Mantzoros CS, Dalamaga M. Obesity and cancer risk: emerging biological mechanisms and perspectives. Metabolism. 2019;92:121–35.

    Article  CAS  PubMed  Google Scholar 

  14. Bugianesi E. Fatty liver disease: putting the spotlight on a silent menace for young adults. Lancet Gastroenterol Hepatol. 2020;5(3):236–8.

    Article  PubMed  Google Scholar 

  15. Sahu B, Bal NC. Adipokines from white adipose tissue in regulation of whole body energy homeostasis. Biochimie. 2023;204:92–107.

    Article  CAS  PubMed  Google Scholar 

  16. Giralt M, Cereijo R, Villarroya F. Adipokines and the endocrine role of adipose tissues. Handb Exp Pharmacol. 2016;233:265–82.

    Article  CAS  PubMed  Google Scholar 

  17. Chao X, Guo L, Ye C, Liu A, Wang X, Ye M, Fan Z, Luan K, Chen J, Zhang C, et al. ALKBH5 regulates chicken adipogenesis by mediating LCAT mRNA stability depending on m(6)A modification. BMC Genomics. 2024;25(1):634.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Gong X, Yu Q, Duan K, Tong Y, Zhang X, Mei Q, Lu L, Yu X, Li S. Histone acetyltransferase Gcn5 regulates gene expression by promoting the transcription of histone methyltransferase SET1. Biochim Biophys Acta Gene Regul Mech. 2020;1863(9):194603.

    Article  CAS  PubMed  Google Scholar 

  19. Chen X, Gong W, Shao X, Shi T, Zhang L, Dong J, Shi Y, Shen S, Qin J, Jiang Q, et al. METTL3-mediated m(6)A modification of ATG7 regulates autophagy-GATA4 axis to promote cellular senescence and osteoarthritis progression. Ann Rheum Dis. 2022;81(1):87–99.

    Article  CAS  PubMed  Google Scholar 

  20. Campion J, Milagro F, Martinez JA. Epigenetics and obesity. Prog Mol Biol Transl Sci. 2010;94:291–347.

    Article  CAS  PubMed  Google Scholar 

  21. Wan QL, Meng X, Wang C, Dai W, Luo Z, Yin Z, Ju Z, Fu X, Yang J, Ye Q, et al. Histone H3K4me3 modification is a transgenerational epigenetic signal for lipid metabolism in caenorhabditis elegans. Nat Commun. 2022;13(1):768.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Sun YN, Qiao YGSP, Wang SZ, Duan K, Wang YX, Li H, Wang N. Epigenetic DNA methylation in the promoters of Peroxisome Proliferator-Activated Receptor γ in chicken lines divergently selected for fatness. 2013.

  23. Borengasser SJ, Zhong Y, Kang P, Lindsey F, Ronis MJ, Badger TM, Gomez-Acevedo H, Shankar K. Maternal obesity enhances white adipose tissue differentiation and alters genome-scale DNA methylation in male rat offspring. Endocrinology. 2013;154(11):4113–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Li J, Li R, Wang Y, Hu X, Zhao Y, Li L, Feng C, Gu X, Liang F, Lamont SJ, et al. Genome-wide DNA methylome variation in two genetically distinct chicken lines using MethylC-seq. BMC Genomics. 2015;16:851.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zhang S, Shen L, Xia Y, Yang Q, Li X, Tang G, Jiang Y, Wang J, Li M, Zhu L. DNA methylation landscape of fat deposits and fatty acid composition in obese and lean pigs. Sci Rep. 2016;6:35063.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Sun Y, Li R, Zhai G, Zhang X, Wang Y. DNA methylation of the PLIN1 promoter downregulates expression in chicken lines. Arch Anim Breed. 2019;62(2):375–82.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Zhang Z, Nie C, Chen Y, Dong Y, Lin T. DNA methylation of CpG sites in the chicken KLF7 promoter and exon 2 in association with mRNA expression in abdominal adipose tissue and blood metabolic indicators. BMC Genet. 2020;21(1):120.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Cui TT, Huang JX, Ning BL, Mu F, Chen HY, Xing TY, Li H, Wang N. DNA methylation promotes the expression of PPARgamma transcript 1 at least in part by preventing NRF1 binding to the promoter P1 of chicken PPARgamma gene. Poult Sci. 2024;103(5):103559.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Huang HY, Liu RR, Zhao GP, Li QH, Zheng MQ, Zhang JJ, Li SF, Liang Z, Wen J. Integrated analysis of MicroRNA and mRNA expression profiles in abdominal adipose tissues in chickens. Sci Rep. 2015;5:16132.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Zhang M, Li D, Zhai Y, Wang Z, Ma X, Zhang D, Li G, Han R, Jiang R, Li Z, et al. The landscape of DNA methylation associated with the transcriptomic network of intramuscular adipocytes generates insight into intramuscular fat deposition in chicken. Front Cell Dev Biol. 2020;8:206.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Ng AP, Coughlan HD, Hediyeh-Zadeh S, Behrens K, Johanson TM, Low MSY, Bell CC, Gilan O, Chan YC, Kueh AJ, et al. An Erg-driven transcriptional program controls B cell lymphopoiesis. Nat Commun. 2020;11(1):3013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Griffin MJ, Zhou Y, Kang S, Zhang X, Mikkelsen TS, Rosen ED. Early B-cell factor-1 (EBF1) is a key regulator of metabolic and inflammatory signaling pathways in mature adipocytes. J Biol Chem. 2013;288(50):35925–39.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Angueira AR, Shapira SN, Ishibashi J, Sampat S, Sostre-Colon J, Emmett MJ, Titchenell PM, Lazar MA, Lim HW, Seale P. Early B cell factor activity controls developmental and adaptive thermogenic gene programming in adipocytes. Cell Rep. 2020;30(9):2869–e28782864.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Chao X, Guo L, Wang Q, Huang W, Liu M, Luan K, Jiang J, Lin S, Nie Q, Luo W, et al. miR-429-3p/LPIN1 axis promotes chicken abdominal fat deposition via PPARgamma pathway. Front Cell Dev Biol. 2020;8:595637.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Gong P, Jing Y, Liu Y, Wang L, Wu C, Du Z, Li H. Whole-genome bisulfite sequencing of abdominal adipose reveals DNA methylation pattern variations in broiler lines divergently selected for fatness. J Anim Sci. 2021;99(1).

  36. Han Y, Han D, Yan Z, Boyd-Kirkup JD, Green CD, Khaitovich P, Han JD. Stress-associated H3K4 methylation accumulates during postnatal development and aging of rhesus macaque brain. Aging Cell. 2012;11(6):1055–64.

    Article  CAS  PubMed  Google Scholar 

  37. Kim DY, Lim B, Lim D, Park W, Lee KT, Cho ES, Lim KS, Cheon SN, Choi BH, Park JE et al. Integrative methylome and transcriptome analysis of Porcine abdominal fat indicates changes in fat metabolism and immune responses during different development. J Anim Sci. 2022;100(12).

  38. Luo R, Dai X, Zhang L, Li G, Zheng Z. Genome-Wide DNA methylation patterns of muscle and Tail-Fat in dairymeade sheep and Mongolian sheep. Anim (Basel). 2022;12(11).

  39. Lu Y, Zhou J, Li F, Cao H, Zhang X, Yu D, He Z, Ji H, Lv K, Wu G et al. The integration of Genome-Wide DNA methylation and transcriptomics identifies the potential genes that regulate the development of skeletal muscles in ducks. Int J Mol Sci. 2023;24(20).

  40. Wang S, Zha L, Cui X, Yeh YT, Liu R, Jing J, Shi H, Chen W, Hanover J, Yin J, et al. Epigenetic regulation of hepatic lipid metabolism by DNA methylation. Adv Sci (Weinh). 2023;10(20):e2206068.

    Article  PubMed  Google Scholar 

  41. Gao H, Mejhert N, Fretz JA, Arner E, Lorente-Cebrian S, Ehrlund A, Dahlman-Wright K, Gong X, Stromblad S, Douagi I, et al. Early B cell factor 1 regulates adipocyte morphology and lipolysis in white adipose tissue. Cell Metab. 2014;19(6):981–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Dang TN, Taylor JL, Kilroy G, Yu Y, Burk DH, Floyd ZE. SIAH2 is expressed in adipocyte precursor cells and interacts with EBF1 and ZFP521 to promote adipogenesis. Obesity. 2020;29(1):98–107.

    Article  PubMed  Google Scholar 

  43. Petrus P, Mejhert N, Gao H, Backdahl J, Arner E, Arner P, Ryden M. Low early B-cell factor 1 (EBF1) activity in human subcutaneous adipose tissue is linked to a pernicious metabolic profile. Diabetes Metab. 2015;41(6):509–12.

    Article  CAS  PubMed  Google Scholar 

  44. Brochet P, Ianni BM, Laugier L, Frade AF, Silva Nunes JP, Teixeira PC, Mady C, Ferreira LRP, Ferre Q, Santos RHB, et al. Epigenetic regulation of transcription factor binding motifs promotes Th1 response in Chagas disease cardiomyopathy. Front Immunol. 2022;13:958200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Chu AY, Tin A, Schlosser P, Ko YA, Qiu C, Yao C, Joehanes R, Grams ME, Liang L, Gluck CA, et al. Epigenome-wide association studies identify DNA methylation associated with kidney function. Nat Commun. 2017;8(1):1286.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Armartmuntree N, Jusakul A, Sakonsinsiri C, Loilome W, Pinlaor S, Ungarreevittaya P, Yong CH, Techasen A, Imtawil K, Kraiklang R, et al. Promoter hypermethylation of early B cell factor 1 (EBF1) is associated with cholangiocarcinoma progression. J Cancer. 2021;12(9):2673–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Akerblad P, Mansson R, Lagergren A, Westerlund S, Basta B, Lind U, Thelin A, Gisler R, Liberg D, Nelander S, et al. Gene expression analysis suggests that EBF-1 and PPARgamma2 induce adipogenesis of NIH-3T3 cells with similar efficiency and kinetics. Physiol Genomics. 2005;23(2):206–16.

    Article  PubMed  Google Scholar 

  48. Belarbi Y, Mejhert N, Gao H, Arner P, Ryden M, Kulyte A. MicroRNAs-361-5p and miR-574-5p associate with human adipose morphology and regulate EBF1 expression in white adipose tissue. Mol Cell Endocrinol. 2018;472:50–6.

    Article  CAS  PubMed  Google Scholar 

  49. Taniguchi M, Nakajima I, Chikuni K, Kojima M, Awata T, Mikawa S. MicroRNA-33b downregulates the differentiation and development of Porcine preadipocytes. Mol Biol Rep. 2014;41(2):1081–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Krueger F, Andrews SR. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics. 2011;27(11):1571–2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Akalin A, Kormaksson M, Li S, Garrett-Bakelman FE, Figueroa ME, Melnick A, Mason CE. MethylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 2012;13(10):R87.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Li S, Garrett-Bakelman FE, Akalin A, Zumbo P, Levine R, To BL, Lewis ID, Brown AL, D’Andrea RJ, Melnick A, et al. An optimized algorithm for detecting and annotating regional differential methylation. BMC Bioinformatics. 2013;14(Suppl 5):S10.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank ICP-1 was provided by Li Hui laboratory of Northeast Agricultural University (Heilongjiang, China).

Funding

This work was supported by the Key-Area Research and Development Program of Guangdong Province, No.2022B0202100002, Science and Technology Program of Guangzhou City No. 2024B03J1353, and Science and Technology Program of Chaozhou City No.202101ZD07.

Author information

Authors and Affiliations

  1. College of Animal Science, South China Agricultural University, Guangzhou, China

    Lijin Guo, Mao Ye, Zhexia Fan, Kang Luan, Manqing Liu, Xiquan Zhang, Zhenhui Li & Qingbin Luo

  2. State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou, China

    Xiaohuan Chao, Lijin Guo, Manqing Liu, Xiquan Zhang, Zhenhui Li & Qingbin Luo

  3. Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, China

    Xiaohuan Chao, Lijin Guo, Meiling Hu, Manqing Liu, Xiquan Zhang, Zhenhui Li & Qingbin Luo

  4. College of Animal Science and Technology, Nanjing Agricultural University, Nanjing, China

    Xiaohuan Chao, Jiahao Chen, Chunlei Zhang & Bo Zhou

Authors
  1. Xiaohuan Chao
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Lijin Guo
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Meiling Hu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Mao Ye
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Zhexia Fan
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Kang Luan
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Jiahao Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Chunlei Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Manqing Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Bo Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Xiquan Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. Zhenhui Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  13. Qingbin Luo
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

XH C, LJ G and ML H was involved in research design, data analysis and manuscript writing. K L, ZX F and M Y participated in the experiment and data analysis; ZH L and B Z reviewed the revised manuscript; JH C, CL Z, MQ L, and XQ Z were responsible for samples collection; QB L designed the entire study and supervised its progress.

Corresponding authors

Correspondence to Zhenhui Li or Qingbin Luo.

Ethics declarations

Ethics approval and consent to participate

All methods were carried out in accordance with relevant guidelines and regulations. This study was carried out in compliance with the ARRIVE guidelines 2.0 (https://arriveguidelines.org/). All animals are euthanized in accordance with animal welfare. The animal experiment program was approved by the Animal Care Committee of South China Agricultura (SCAU#2017015; September 13, 2017).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

12864_2025_11464_MOESM1_ESM.xlsx

Supplementary Material 1: Table S1: Genome sequencing coverage statistics table. Table S2: Effective genomic C coverage of abdominal fat. Table S3: The information pertaining to DMRs. Table S4: KEGG analysis related to fat. Table S5: Primers sequence information.

Supplementary Material 2: A: Original Figure of Figure 5F, B: Original Figure of Figure 5K.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chao, X., Guo, L., Hu, M. et al. Abnormal DNA methylation of EBF1 regulates adipogenesis in chicken. BMC Genomics 26, 275 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12864-025-11464-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12864-025-11464-6

Keywords

BMC Genomics

ISSN: 1471-2164

Contact us