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. 2024 Nov 1;72(45):25403–25413. doi: 10.1021/acs.jafc.4c06867

Deciphering Mechanisms of Adipocyte Differentiation in Abdominal Fat of Broilers

Xi Sun 1, Xiaoying Liu 1, Chaohui Wang 1, Zhouzheng Ren 1, Xiaojun Yang 1, Yanli Liu 1,*
PMCID: PMC11565640  PMID: 39483088

Abstract

graphic file with name jf4c06867_0008.jpg

The excessive deposition of abdominal fat tissue (AFT) in broilers has emerged as a major concern in the poultry industry. Despite some progress in recent years, the molecular mechanisms underlying AFT development remain ambiguous. The current study combined RNA-seq with transposase-accessible chromatin sequencing (ATAC-seq) to map the dynamic profiling of chromatin accessibility and transcriptional reprogramming in AFT adipocyte differentiation in broilers at day 3 (D3) and D14. Our results found that the levels of CDK1 and PCNA were down-regulated at D14, D28, and D42 compared to D3, while the levels of C/EBPα and FABP4 were up-regulated at D14 and D42 compared to D3. Meanwhile, PPARγ was significantly up-regulated at D28 and D42. RNA-seq of AFT identified 1705 up-regulated and 1112 down-regulated differential expression genes (DEGs) between D3 and D14. Pathways based on up-regulated DEGs mainly enriched some pathways related to adipocyte differentiation, while down-regulated DEGs pointed to DNA replication, cell cycle, and gap junction. Gene set enrichment analysis (GSEA) revealed that DNA replication and the cell cycle were down-regulated at D14, while the insulin signaling pathway was up-regulated. In the OA-induced immortalized chicken preadipocyte (ICP2) model, protein dynamic changes were consistent with AFT from D3 to D14. Same pathways were enriched in ICP2. In addition, based on overlapped DEGs from AFT and ICP2, enriched pathways related to adipocyte differentiation or proliferation mentioned above were all involved. A total of 1600 gain and 16727 loss differential peaks (DPs) were identified in ICP2 by ATAC-seq. Predicted genes from DPs at the promoter regions were enriched in glycerophospholipid metabolism, TGF-β signaling, FoxO signaling, and ubiquitin-mediated proteolysis. DNA motifs predicted 159 transcription factors (TFs) based on gain and loss peaks from the promoter regions, where 1 and 10 TFs were overlapped with up or down TFs from DEGs. Overall, this study presents a framework for the comprehension of the epigenetic regulatory mechanisms of adipocyte differentiation and identifies candidate genes and potential TFs involved in AFT adipocyte differentiation in broilers.

Keywords: abdominal fat, adipocyte differentiation, ICP2 cell, ATAC-seq, RNA-seq

Introduction

As an efficient animal production system, broilers provide economical and nutritious animal protein for human consumption.1 However, genetic selection for commercial purposes has led to an unprecedented increase in the growth rate of broilers,2 which concomitantly leads to excessive abdominal fat tissue (AFT),3 resulting in significant economic losses for the broiler industry. Studies have shown that AFT traits have high heritability,4 which further contributes to AFT accumulation. Excessive AFT deposition decreases feed efficiency, impairs animal health,5 and negatively impacts the slaughtering process and the environment.6 Excessive AFT deposition has become an ongoing and perplexing problem puzzling the breeding and broiler industry, making it a major challenge to reduce AFT deposition without slowing down the growth rate.2 Consequently, addressing the issue of excessive AFT accumulation is of great significance to the breeding and broiler industry.

Reducing AFT content through traditional breeding presents a great challenge due to the strong positive correlation between AFT mass and body weight.7 Therefore, new strategies are required to reveal the mechanisms underlying AFT development without affecting the production performance. The exponential growth of multiomics data and the rapid development of high-throughput sequencing technology have presented unprecedented opportunities for potential mechanisms mentioned above. Numerous studies have attempted to elucidate the mechanisms and regulatory targets of AFT formation through proteomics,1 16s,2 LC/MS-based lipidomics,8 and so on. Chromatin accessibility provides valuable information for identifying regulatory elements and mechanisms, which is used to determine various cis-regulatory elements and predict transcription factor (TF) binding sites.9 The integration of transcriptome and ATAC-seq technologies has become a valuable strategy for identifying the underlying mechanisms of complex traits in farm animals.9,10 For instance, studies have utilized a combination of ATAC-seq and RNA-seq to map chromatin accessibility and developmental transcriptomes at different stages of skeletal muscle development in pigs,11 as well as epigenetic mechanisms for differences in fertility between Meishan and Duroc pigs.12 The regulation of adipogenesis involves a complex interplay of various factors, and several genes have been identified to participate in AFT deposition. For example, the expression of KLF15 and APOC2 genes has been linked to the regulation of fat formation,13,14 and the roles of PPARγ and C/EBPα in adipocyte differentiation have been extensively studied.15 Our previous study found that the abdominal fat weight increases with age in broilers and reaches a significant inflection point at D14, which may be the key physiological stage of abdominal fat cell hypertrophy.16 In the current work, we integrated RNA-seq and ATAC-seq to investigate the developmental patterns of AFT in broilers, aiming to map high-resolution chromatin accessibility profiling and identify potential priority regulatory targets involved in AFT adipocyte proliferation and differentiation in broilers. Our results systematically describe the epigenetic mechanism of AFT deposition and identify the potential TFs driving the process of AFT deposition. These findings broaden the knowledge of AFT development and regulation, providing new insights into the transcriptional regulation of abdominal adipocyte differentiation in broilers.

Materials and Methods

All experimental protocols (DK2022007) involving broilers in this study were approved by the Animal Care and Use Committee of Northwest A&F University (protocol number NWAFAC1008).

Animals and Sample Collection

A total of 120 1-day-old Arbor Acres broilers were obtained from Xi’an Dacheng Poultry Industry Co., Ltd. The broilers were given free access to a commercial diet and water. At each of the age of 3, 14, 28, and 42 days, 12 birds were randomly selected without considering gender, euthanized by neck dislocation, and dissected. The AFT was immediately dissected and frozen with liquid nitrogen.

Western Blotting

Total protein was extracted as previously described.17 Three samples were used for each time point, and the protein concentration was standardized using the BCA commercial reagent kit (AccuRef Scientific Co., Ltd., Xi’an, China). After denaturation, an equal amount of protein (25 μg) was separated by SDS polyacrylamide gel for electrophoresis to detect the expression of the target protein. The primary antibodies were used at a dilution of 1:1000, and the secondary antibody was diluted at a ratio of 1:2000 (DIYIBio, Shanghai, China). Detailed information about the antibodies used is provided in Table 1. Images were captured using the iBright FL1500 system (Thermo Fisher Scientific, USA) and quantified by ImageJ (National Institutes of Health, USA) with GAPDH or β-actin as an internal control.

Table 1. Antibody Information Used for Western Blotting.

protein company molecular size (kDa) dilution ratio
GAPDH Abways (AB0037) 37 1:1000
CDK1 Abways (CY5176) 34 1:1000
PCNA Abways (AB0051) 29 1:1000
C/EBPα Abways (CY5723) 43 1:1000
FABP4 Abways (CY6768) 14 1:1000
PPARγ Abways (CY6675) 57 1:1000
β-actin PTMbio (PTM-5028) 42 1:1000
CPT-1A Proteintech (BC000185) 88 1:1000
SREBP-1c Wanleibio (WL02093) 68; 125 1:1000
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Transcriptome Profiling

Total RNA was isolated from AFT using the TRIZOL kit protocol (AG21101, Agbio, China). RNA-seq libraries were constructed by applying the Illumina TruSeq Kit (Illumina, San Diego, USA) and sequenced by Shanghai Personal Biotechnology Co., Ltd. The raw data were filtered, quality-controlled, and aligned to the chicken genome (GRCg7b, https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_016699485.2/) using HISAT2. DESeq2 was further applied to identify differential expression genes (DEGs) based on a threshold of log2 FoldChange > 1 and P-value < 0.05. Subsequently, DEGs were annotated to the Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp/) database and subjected to enrichment analysis using Clusterprofiler. Gene set enrichment analysis (GSEA) was conducted through https://www.gsea-msigdb.org/gsea/index.jsp. The detailed procedures for RNA-seq were referred to the previous report.18

Cell Culture

The immortalized chicken preadipocytes (ICP2) were purchased from the Key Laboratory of Chicken Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Northeast Agricultural University (Harbin, China). Briefly, the ICP2 cells were cultured in DMEM/F12 medium (Gibco, US) supplemented with 10% fetal bovine serum (BI, Germany), 100 units/mL penicillin, and 100 μg/mL streptomycin at 37 °C in a 5% CO2 incubator. Upon reaching approximately 80% confluence, the cells were washed and changed with treatment culture medium. To induce differentiation, ICP2 cells were treated with control (Con) or 200 μM oleic acid (OA) for 3 days. This dosage was based on previous research19,20 and our preliminary experiments.

Oil Red O Staining and Quantification

The ICP2 cells were fixed with 4% paraformaldehyde for 30 min. After being washed with PBS three times, the filtered Oil Red O working solution was added to the fixed cells and maintained for 2 h. Subsequently, the stained cells were washed with PBS and observed under an optical microscope. To quantify the lipid content, 500 μL of isopropanol was added to each well to extract the Oil Red O stain from the cells. After 20 min, isopropanol was added to a 96-well plate and the OD value was further measured at 510 nm.

ATAC-seq Analysis

ATAC-seq was conducted by Wuhan Frasergen Bioinformatics Co., Ltd., following the protocol previously described.21 The preprocessing steps mainly included nuclei extraction, Tn5 transposase cleavage, adapter introduction, and AMpure bead purification. After sequencing on the Illumina platform and filtering the raw data using SOAPnuke, the Burrows–Wheeler Alignment algorithm was applied to align the reads to the reference chicken genome (GRCg7b). For the peak calling, MACS2 (v2.1.1) was used to identify the landscape of the open chromatin regions across the genome. The annotatePeak function of ChIPseeker was used to annotate the peaks, with the promoter region defined as < 3 kb from the transcription start site. DESeq2 was employed to assess the differential peaks (DPs) under the condition that log2 fold change > 1 and P-value < 0.05. Accessibility analysis using DiffBind was performed with the following parameters: |log2 FoldChange| > 1, with a P-value < 0.05. Pathway analysis was conducted through the KOBAS Web site. DNA motif enrichment analysis of DPs was performed using the MEME Suite function in the JASPAR database. The detailed process of DNA motif analysis was conducted according to our previous studies.22,23

Statistical Analysis

All data were shown as the mean ± standard error of the mean (SEM). Comparisons between different groups were calculated using the unpaired Student’s t-test with SPSS 20.0 (Chicago, USA). Figures were drawn by GraphPad Prism 8 (Boston, USA). A P-value < 0.05 was considered as statistically significant. Specific statistical analyses for RNA-seq and ATAC-seq were described and defined in their respective sections.

Results and Discussion

Dynamic Changes of Adipocyte Proliferation and Differentiation in AFT of Broilers

Excessive AFT is a common phenomenon in broilers, which impairs the health of broilers and causes huge economic losses.1,7 To reveal the critical growth developmental period of AFT, some proteins associated with adipocyte proliferation and differentiation were analyzed, and they are shown in Figure 1. Compared with D3, the cyclin-dependent kinase 1 (CDK1) protein level decreased gradually in AFT from D14 to D42. Similarly, proliferating cell nuclear antigen (PCNA) protein expression was lower at D14, D28, and D42 than that at D3. On the contrary, the proteins related to adipocyte differentiation, CCAAT enhancer binding protein α (C/EBPα), and fatty acid binding protein 4 (FABP4) were higher from D14 to D42 than at D3. In addition, peroxisome proliferator-activated receptor γ (PPARγ), a vital factor in adipocyte differentiation and fatty acid metabolism,24 was significantly up-regulated at D28 and D42. These results are consistent with our previous findings,16 indicating that D14 is a pivotal node for the abdominal adipocyte differentiation of broilers.

Figure 1.

Figure 1

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Dynamic changes of adipocyte proliferation and differentiation in AFT of broilers. (A) Western blotting analysis of GAPDH, CDK1, PCNA, C/EBPα, FABP4, and PPARγ. (B) Relative blotting band density of proteins mentioned above (standardized to GAPDH, n = 3). Data are expressed as the mean ± SEM, and the different letters indicate significant differences and the same letter indicates no significant differences.

Transcriptional Comparison of AFT between D3 and D14 in Broilers

Adipocyte proliferation and differentiation are essential processes for adipogenesis, and understanding the regulatory mechanisms of adipocyte proliferation and differentiation is crucial to revealing its transcriptional changes. Considering that D14 might be a pivotal physiological node for adipocyte differentiation in AFT, RNA-seq was utilized to illustrate the transcriptional changes. As displayed in Figure 2A,B, principal component analysis (PCA) and heatmap analysis showed clear separation between the two physiological stages, with intragroup samples clustered together. A total of 2817 DEGs were identified, including 1705 up-regulated and 1112 down-regulated genes at D14 compared to those at D3 (Figure 2C), indicating a significant separation of gene transcription between the two physiological nodes. Gene lists are shown in Additional File 1. Previous studies have demonstrated that steroid biosynthesis in AFT affects adipocyte function,25 and insufficient biosynthesis of sphingolipid can lead to lipodystrophy and insulin resistance.26 Further, sphingolipids are essential for effective loss of triacylglycerol in adipocytes, especially in response to conjugated linoleic acid treatment.27 These studies collectively emphasize the importance of PPAR signaling, sphingolipid and steroid biosynthesis, and fatty acid metabolism in the AFT formation process. In the present study, some pathways related to adipocyte differentiation were enriched based on up DEGs such as steroid biosynthesis, sphingolipid metabolism, fatty acid biosynthesis, and PPAR signaling (Figure 2D). Further, KEGG analysis of down DEGs (Figure 2E) pointed to DNA replication, cell cycle, gap junction, and p53 signaling pathways, which were associated with adipocyte proliferation. These pathways were also enriched based on all DEGs (Figure 2F). The information on pathways is shown in Additional File 2. In addition, GSEA analysis based on the expression abundance of whole genes revealed down-regulation of DNA replication and cell cycle at D14, while the insulin signaling pathway was up-regulated (Figure 2G–I), indicating that cell proliferation was weakened from D3 to D14, but lipid formation was progressively up-regulated. Consequently, great changes at the transcriptional level emphasized that D14 is the key physiological point of adipocyte differentiation in the abdominal tissue. The timing of adipocyte proliferation and differentiation in broilers provides a reference for precise nutritional regulation in broilers, which may address the puzzle to reduce AFT deposition without slowing down the growth rate.2

Figure 2.

Figure 2

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Transcriptional comparison of AFT between D3 and D14 in broilers. (A) Principal component analysis plots based on identified DEGs. (B) Heatmap of DEGs between D3 and D14. (C) The number of up- and down-regulated genes at D14 when compared with D3. (D–F) KEGG pathway enriched from up, down, and all DEGs, respectively. (G–I) GSEA analysis of DNA replication, cell cycle, and insulin signaling pathway in the AFT between D3 and D14. Statistical significance is denoted by *P < 0.05 and **P < 0.01.

Model Construction and Analysis of Chicken Adipocyte Differentiation

To explore the mechanism of adipocyte differentiation in broilers, an in vitro model was constructed in chicken adipocytes ICP2. Studies have shown that 0.16 mM OA can induce differentiation of chicken preadipocytes,28 adipogenesis of adipocytes increased after OA treatment, and adipocytes matured after exposure to OA for 3 days.29 In the current study, as shown in Figure 3A, ICP2 cells displayed a significant increase in lipid accumulation after OA induction, as evidenced by Oil Red O staining and quantitative analysis. In accord with the morphological changes, Western Blotting analysis (Figure 3B,C) showed that adipogenic marker protein levels, such as C/EBPα, FABP4, and sterol regulatory element-binding protein 1c (SREBP-1c), were significantly increased compared to the control, while the PCNA and CDK1 proteins, markers associated with cell proliferation, were notably down-regulated. Carnitine O-palmitoyltransferase 1 (CPT1), a limited enzyme for fatty acid oxidation, was also decreased in the OA group. No significant difference was observed in the PPARγ protein abundance. Collectively, these findings suggest that the differentiation model of ICP2 cells induced by OA was successfully constructed, which was consistent with the phenomenon observed in abdominal fat from D3 to D14.

Figure 3.

Figure 3

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Model construction and analysis of differentiated chicken adipocytes ICP2. (A) Oil Red O staining and quantification of Oil Red O staining in OA-treated ICP2 cells (magnification: 10 × 10). (B) Protein bands of β-actin, CDK-1, PCNA, C/EBPα, FABP4, PPARγ, CPT-1A, and SREBP-1c. (C) Quantification of the blot bands using β-actin as an internal control (n = 3). Statistical significance is denoted by *P < 0.05 and **P < 0.01.

Analysis of Transcriptional Changes in Differentiated ICP2 Cells

To further understand the molecular mechanisms underlying chicken adipocyte differentiation, we performed RNA-seq analysis of differentiated ICP2 cells and overlapped it with the results in the ATF of broilers. As presented in Figure 4A,B, obvious separation was found between the Con and OA groups, with intragroup samples clustered together. When compared with the Con group, 3324 up-regulated and 2941 down-regulated genes were detected (Figure 4C). Similar to the results from AFT, the same metabolic pathways were significantly enriched by DEGs in ICP2, such as steroid biosynthesis, sphingolipid metabolism, and PPAR signaling (Figure 4D). Down DEGs in ICP2 were also involved in pathways such as DNA replication, cell cycle, and the gap junction (Figure 4E). These pathways about adipocyte proliferation and differentiation were also enriched based on all DEGs in differentiated ICP2 cells (Figure 4F). Additionally, GSEA analysis also revealed that DNA replication and cell cycle were down-regulated, while the insulin signaling pathway was up-regulated in the OA group compared to the Con group (Figure 4G–I), further supporting the high consistency between the ICP2 model and the transcriptional changes identified from D3 to D14 in AFT. In addition, a total of 816 DEGs overlapped with those from AFT, including 479 up-regulated and 337 down-regulated DEGs (Figure 5A,B). All gene lists are shown in Additional File 1. We also performed KEGG pathway analysis based on the up, down, and all overlapped DEGs between ICP2 and AFT. As displayed in Figure 5C–E, adipocyte differentiation- or proliferation-related pathways mentioned above were all involved, and the regulation trend of these pathways was consistent with those from ATF in vivo or ICP2 in vitro. The information on pathways is shown in Additional File 2. Therefore, we then evaluated the changes in chromatin accessibility of ICP2 cells to reveal the regulation mechanism of abdominal fat development.

Figure 4.

Figure 4

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Transcriptional changes in differentiated ICP2 cells. (A) PCA analysis based on DEGs of differentiated ICP2. (B) Heatmap of the identified DEGs of differentiated ICP2. Higher expression genes were shown in red color, while lower expression genes were presented in blue color. (C) Up- and down-regulated gene numbers of differentiated ICP2. (D–F) KEGG enriched from up, down, and total DEGs, respectively. (G–I) GSEA assays of pathways in the ICP2 cells, including DNA replication, cell cycle, and insulin signaling pathway. Data are expressed as the mean ± SEM (n = 6). Statistical significance is denoted by *P < 0.05 and **P < 0.01.

Figure 5.

Figure 5

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Integration analysis of AFT and ICP2 transcriptome. (A, B) Venn diagram of overlapping genes obtained from AFT and ICP2 co-up-regulating or co-down-regulating DEGs. (C–E) KEGG pathways enriched from up, down, and all overlapped genes, respectively. Data are expressed as the mean ± SEM, and statistical significance is denoted by *P < 0.05 and **P < 0.01.

Chromatin Accessibility Analysis in Differentiated ICP2 Cells

Chromatin modification maintains preadipocyte status and induces adipocyte differentiation by regulating the master lipogenesis regulatory genes,30 highlighting the importance of epigenetic profiling in adipose-specific gene expression. To interrogate the relationship between chromatin accessibility and transcriptional changes, ATAC-seq was applied to map the changes of chromatin openness in differentiated ICP2 cells. As displayed in Figure 6A, an obvious separation was observed between samples from the Con and OA groups based on PCA analysis. A total of 1600 gain and 16,727 loss DPs were identified, which were predicted to be associated with 1336 and 9254 genes, respectively (Figure 6B,C). When focused on the promoter regions, 180 gain and 6448 loss peaks were obtained, which were mapped to 171 and 5601 genes, respectively (Figure 6D,E). All gene lists are shown in Additional File 1. The targeted genes from all gain (Figure 6F) or loss peaks (Figure 6G) were related to metabolic pathways such as glycosphingolipid biosynthesis, tight junction, cell cycle, adipocytokine signaling, inflammatory mediator regulation, and autophagy. Moreover, KEGG enrichment analysis based on the mapped genes from gain or loss DPs (Figure 6H,I) includes apoptosis, Th17 cell differentiation, glycerophospholipid metabolism, TGF-β signaling, FoxO signaling, and ubiquitin-mediated proteolysis, which were consistent with those enriched from DEGs to some extent (Additional File 2). Studies have shown that lipid metabolism plays a significant role in the differentiation and function of Th17 cells, particularly through de novo lipogenesis.31 TGF-β signaling has also been reported to be associated with lipid metabolism,32 while SREBP-1 activation can promote the expression of TGFB1.33 Our results were consistent with previous findings that the FoxO signaling pathway is associated with fat deposition.34 CDK1 can bind to phosphorylate ACSL4 at S447, followed by recruitment of E3 ubiquitin ligase UBR5 and ubiquitination and degradation of ACSL4, thereby inhibiting lipid peroxidation and ferroptosis.35 Hepatocyte-specific E3 ubiquitin ligase ring finger protein 5 knockout significantly exacerbated hepatic steatosis.36 Consequently, these results suggest that transcriptional reprogramming occurs during adipocyte differentiation, and the pathways mentioned above warrant further attention.

Figure 6.

Figure 6

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Chromatin accessibility analysis in differentiated ICP2 cells. (A) PCA analysis of ATAC-seq in differentiated ICP2 cells. (B, C) The number of differential peaks and their target genes in the whole genome. (D, E) The number of differential peaks in the promoter regions and targeted gene numbers of the differential peaks in the promoters. (F, G) KEGG pathway enrichment analysis based on targeted genes from gain or loss peaks in the whole genome. (H, I) KEGG enrichment analysis based on mapped gene from gain or loss DPs in the promoter regions. Data are expressed as the mean ± SEM (n = 3). Statistical significance is denoted by *P < 0.05 and **P < 0.01.

DNA Motif Prediction

It was reported that chromatin accessibility can help to identify the regulation regions of gene expressions associated with AFT deposition and provide insights into gene regulatory targets.37 A recent study has investigated the mechanism of sex differentiation and infertility in sex reversal chicken.38 To further validate the theory that chromatin accessibility is involved in the regulation of transcriptional reprogramming, we overlapped up or down DEGs with predicted genes from the corresponding gain or loss DPs. As shown in Figure 7A,B, 234 up-regulated and 1325 down-regulated genes overlapped with gain and loss genes, including FABP3, PCNA, CDK1/2, and CPT2, which were associated with adipocyte proliferation and differentiation. Both PCNA and CDK1/2 are cell proliferative markers,39 and CPT2 was reported to be a fatty acid oxidation-associated gene.40 FABP3 has effects on stearic abundance41 and influences membrane lipid composition via lipid remodeling in muscle tissues.42 All gene lists are shown in Additional File 1, and these were enriched in DNA replication, cell cycle, p53 signaling, gap junction, and steroid hormone biosynthesis pathways. Collectively, these results indicated that the transcriptional reprogramming shifted toward abdominal adipocyte differentiation and lipid deposition, and the identified candidate genes might be regulated by nearby open chromatin regions, which may act as TF-binding sites.

Figure 7.

Figure 7

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DNA motif prediction based on ATAC-seq and RNA-seq. (A, B) Venn diagram of up or down DEGs with predicted genes from corresponding gain or loss DPs. (C) KEGG enrichment analysis based on overlapped genes mentioned in panels (A) and (B). (D, E) Venn map where the gain or loss motifs predicted from the promoter regions overlapped with the up- or down-regulated TFs predicted from DEGs.

Gene expression is precisely regulated by enhancers that can recruit TFs and cofactors to activate transcription from target promoters,43 and TFs can activate gene expression by promoting the formation of enhancer–promoter loop.44 A motif is a specific base sequence with high affinities for TFs.12 Considering that TFs were closely related to gene transcriptional regulation, DNA motif analysis was carried out to seek the potential TFs based on the promoter regions of gain and loss peaks. A total of 159 TFs were predicted, including 7 gain and 152 loss factors. Furthermore, we overlapped these factors with the up or down TFs from DEGs (Figure 7D,E). Only KLF4 was identified between gain motifs and the up TFs. KLF4 has been shown to be a pioneer TF that plays a crucial role in reprogramming,45 which directly binds to the SREBP-1 promoter region, and promotes the production of lipogenesis regulators.46 Among the loss motifs, 10 down TFs were detected, including TCFL5, ELK3, ETV5, E2F1, CREM, RFX5, MLXIPL, E2F2, MYCN, and POU3F4. Some of these TFs have been reported to be associated with lipid metabolism. For example, TCFL5, as a Notch target gene, can induce SOX2 and reduce KLF4 gene levels, which may have the function of lipid regulation.47,48 ELK3 inhibits mitochondrial fission, thereby suppressing lipid transport and mediating the reprogramming process of lipid metabolism.49 ETV5 regulates macrophage activation in adipose tissue and may be a target for obesity-related chronic inflammation.50 E2F1 regulates lipid synthesis and glycolysis by binding to the promotors of adipogenic genes such as FASN51 and regulates preadipocyte differentiation.52 E2F2 is regulated by PPARγ,53 and E2F1 interacts with E2F2 to mediate CPT2 inhibition, providing a lipid-rich environment.54 Consistently, down-regulation of CPT2 was detected in this study from both RNA-seq and ATAC-seq. Enhanced binding of CREM to the glutathione peroxidase 4 promoter site increases the abundance of lipid-reactive oxygen species.55 MLXIPL, which encodes the carbohydrate-responsive element-binding protein, has been shown to be related to plasma triglycerides, apolipoprotein-B, VLDL, and HDL-c.56 MYCN amplification leads to glycerolipid accumulation by promoting fatty acid uptake and biosynthesis57 and mediates lipid peroxidation and sensitivity to ferroptosis.58 However, no evidence has shown a clear relationship between RFX5 and lipid metabolism, but it may serve as a cell cycle regulator. The expression of RFX5 increased with NASH progression,59 and the RFX5-KDM4A (lysine-specific demethylase 4A) pathway promotes the cell cycle through regulation of p53.60 The DNA motif analysis revealed a group of promising TFs that may bind to DNA regions associated with the cell cycle and adipogenesis-related genes, thereby regulating abdominal adipocyte differentiation and lipid metabolism. Taken together, this study proposed the timing of adipocyte proliferation and differentiation in broilers, mapped chromatin openness during abdominal fat cell hypertrophy, and observed a strong association between chromatin openness and gene transcriptional activity, which supports the crucial role of chromatin signatures in transcriptional regulation of the proliferation and differentiation of abdominal adipocytes. The core genes and TFs can be used as targets for screening nutritional regulatory strategies to achieve weight loss and lipid reduction, but more information should be clarified for further verification.

Acknowledgments

Xi Sun: writing of the original draft, review and editing, and software. Xiaoying Liu: review, editing, and conceptualization. Chaohui Wang: data curation and formal analysis. Zhouzheng Ren: project administration and supervision. Xiaojun Yang: supervision and resources. Yanli Liu: supervision, methodology, project administration, and review and editing.

Glossary

Abbreviations

AFT

abdominal fat tissue

C/EBPα

CCAAT enhancer binding protein α

Con

control

CPT1

carnitine O-palmitoyltransferase 1

DEGs

differential expression genes

DPs

differential peaks

FABP4

fatty acid binding protein 4

GSEA

gene set enrichment analysis

ICP2

immortalized chicken preadipocytes

KEGG

Kyoto Encyclopedia of Genes and Genomes

OA

oleic acid

PCA

principal component analysis

PCNA

proliferating cell nuclear antigen

PPARγ

peroxisome proliferator-activated receptor γ

SREBP-1c

sterol regulatory element-binding protein 1c

TFs

transcription factors

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jafc.4c06867.

  • Additional File 1: gene lists for DEGs and the overlapped DEGs of AFT and ICP2, target genes of DPs from ATAC-seq, overlapped genes of ICP2 between RNA-seq and ATAC, and motif predicted from DPs at the promoter regions and the overlapped TFs between RNA-seq and ATAC (XLSX)

  • Additional File 2: detailed information on pathways enriched in KEGG analysis (XLS)

This work was funded by the National Key Research & Development Program of China (2023YFD1301400 and 2022YFF1001000), the Program for Shaanxi Science & Technology (2022GD-TSLD-46-0302, 2023KXJ-243, 2023GXJS-02-01 and L2022-QCYZX-NY-004) and Innovation and Entrepreneurship Training Program for College Students (202410712240 and X202410712257). We also express our sincerely thanks to HPC of NWAFU for data analysis.

The authors declare no competing financial interest.

Supplementary Material

jf4c06867_si_001.xlsx (5.8MB, xlsx)
jf4c06867_si_002.xls (55KB, xls)

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