Dynamic m⁶A methylation during bovine preadipocyte differentiation and functional implication of the m⁶A writer METTL14.

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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Genomics . 2026 Apr 20;27:511. doi: 10.1186/s12864-026-12858-w Dynamic m⁶A methylation during bovine preadipocyte differentiation and functional implication of the m⁶A writer METTL14 Dianqi Zhang 1, # , Jiawei Du 1, # , Ni Gao 1 , Hehan Qiu 1 , Huaxuan Li 1 , Chugang Mei 2, 3, ✉ , Linsen Zan 1, 2, ✉ Author information Article notes Copyright and License information 1 College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi 712100 P. R. China 2 National Beef Cattle Improvement Center, Northwest A&F University, Yangling, Shaanxi 712100 P. R. China 3 College of Grassland Agriculture, Northwest A&F University, Yangling, Shaanxi 712100 PR China ✉ Corresponding author. # Contributed equally. Received 2025 Nov 19; Accepted 2026 Apr 9; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/ . PMC Copyright notice PMCID: PMC13224485  PMID: 42010454 Abstract As a fundamental mechanism of post-transcriptional epigenetic regulation, RNA modifications have attracted increasing research interest in recent years. N6-methyladenosine (m 6 A), the most abundant internal RNA modification, plays a crucial role in regulating multiple stages of mRNA metabolism including processing, transport, translation, and degradation. The differentiation of bovine preadipocytes into mature adipocytes is regulated at multiple levels including transcriptional, post-transcription, translation, and post-translation levels. However, the precise regulatory mechanism of m 6 A modification during bovine adipogenesis remains poorly understood. In this study, we conducted MeRIP-seq and RNA-seq analyses across three stages of bovine preadipocyte differentiation—pre-differentiation (day 0, D0), early differentiation (day 2, D2), and late differentiation (day 8, D8)—to characterize the dynamic landscape of m⁶A methylation during adipogenesis. The analysis identified 400 genes exhibiting significant changes in both m⁶A modification levels and mRNA expression during the differentiation phase (D0–D2), while 592 genes exhibited such changes during the lipid accumulation phase (D2–D8). These results indicate that m⁶A modification likely contributes to adipogenesis by modulating the expression of these genes. To further investigate the functional relevance of the m⁶A machinery, we knocked down the m⁶A writer METTL14, a core component of the methyltransferase complex, and performed RNA-seq analysis. Integrative analysis of the differentially expressed genes (DEGs) with the stage-specific expression profile revealed a set of candidate genes potentially regulated by METTL14 that participate in early preadipocyte differentiation and late-stage lipid accumulation. These findings provide new evidence for the molecular mechanisms underlying m⁶A modification during bovine adipogenesis and highlight a functional contribution of the m⁶A writer METTL14. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-026-12858-w. Keywords: Bovine, METTL14 , MeRIP-seq, Adipogenesis Introduction Beef is valued by consumers for its high-quality protein content and essential fatty acids. Intramuscular fat (IMF) content critically influences key beef quality attributes, including flavor, tenderness, and juiciness [ 1 , 2 ]. High-IMF beef is generally considered a premium product and frequently experiences market shortages. The deposition of IMF is a complex biological process regulated at multiple levels, including transcriptional, post-transcriptional, and translational control [ 3 ]. Current molecular breeding research in beef cattle mainly concentrates on identifying and functionally characterizing key genes, whereas the role of RNA modifications on trait improvement remains largely unexplored. Structurally, RNA exhibits diverse chemical modifications, with over 170 distinct types identified to date [ 4 ]. Among these, m 6 A is the most prevalent internal modification in mRNA and has emerged as one of the most extensively studied RNA epigenetic marks. It plays a critical role in cellular development and functional maintenance by regulating processes such as pre-mRNA alternative splicing, nuclear export, translation efficiency, stability, and degradation [ 5 – 8 ]. The discovery that FTO (Fat mass and obesity-associated protein) can demethylate m 6 A revealed that, similar to DNA methylation, m⁶A is dynamically reversible, establishing it as a major focus in RNA epigenetics. The deposition of m 6 A is catalyzed by a methyltransferase complex (MTC) with METTL3 (Methyltransferase 3) and METTL14 (Methyltransferase 14) serving as its core enzymatic subunits [ 9 , 10 ]. In addition to its core subunits, the methyltransferase complex comprises auxiliary components such as WTAP (Wilms Tumor 1-Associating Protein), RBM15/15B (RNA Binding Motif Protein 15/15B), ZC3H13 (Zinc Finger CCCH-Type Containing 13), and VIRMA (Vir-Like m 6 A Methyltransferase Associated), which collectively regulate m 6 A deposition and its specific localization [ 11 – 15 ]. The currently known m 6 A demethylases, which belong to the Fe(II)/α-ketoglutarate-dependent dioxygenase superfamily of the AlkB family, include FTO and ALKBH5 (AlkB Homolog 5) [ 16 – 19 ]. These two demethylases exhibit distinct functional mechanisms due to their structural differences [ 20 ]. Proteins that directly recognize m 6 A and mediate its functional consequences are referred to as “readers.” The YTH domain family, comprising YTHDF1 (YTH N6-Methyladenosine RNA Binding Protein F1), YTHDF2 (YTH N6-Methyladenosine RNA Binding Protein F2), YTHDF3 (YTH N6-Methyladenosine RNA Binding Protein F3), YTHDC1 (YTH N6-Methyladenosine RNA Binding Protein C1), and YTHDC2 (YTH N6-Methyladenosine RNA Binding Protein C2), represents the most thoroughly characterized group of m 6 A readers. YTHDC1 is predominantly nuclear and regulates RNA splicing, whereas the remaining members exhibit high cytoplasmic expression and are involved in mRNA stability, translation, and degradation [ 21 , 22 ]. Beyond the YTH family, several other m⁶A reader proteins have also been identified and characterized. For example, the IGF2BP family (IGF2BP1/2/3, Insulin-like Growth Factor 2 mRNA Binding Protein 1/2/3) recognizes m⁶A modifications to enhance the stability and translation efficiency of their target mRNAs, consequently affecting cellular processes such as metabolism and tumorigenesis. Members of the HNRNP family, including HNRNPA2B1 (Heterogeneous Nuclear Ribonucleoprotein A2/B1), HNRNPC (Heterogeneous Nuclear Ribonucleoprotein C), and HNRNPG (Heterogeneous Nuclear Ribonucleoprotein G), primarily regulate pre-mRNA splicing and alternative transcription. Additionally, the eIF3 complex promotes cap-independent translation initiation through its interaction with m⁶A modified sites [ 23 ]. Research has demonstrated that m⁶A modification is extensively involved in diverse physiological and pathological processes, such as glucose metabolism [ 24 – 26 ], type 2 diabetes [ 27 – 29 ], tumorigenesis and progression [ 30 , 31 ], muscle cell differentiation, and cardiac function regulation [ 32 , 33 ]. Recent studies have established that m⁶A modification plays an important regulatory role in adipogenesis. MeRIP-seq analyses have revealed how m⁶A-modifying proteins influence adipocyte differentiation by modulating m⁶A levels on key adipogenic genes [ 34 ]. Genome-wide association studies (GWAS) indicate that variations in the FTO gene are significantly correlated with obesity [ 35 , 36 ]. FTO was later characterized as an RNA demethylase responsible for catalyzing the removal of m⁶A. The knockdown of FTO significantly increased m⁶A levels in mRNA [ 16 ]. During adipogenesis, PPARG (Peroxisome Proliferator-Activated Receptor Gamma) acts as a master regulator of adipocyte differentiation [ 37 ]. FTO promotes adipogenesis by enhancing PPARG expression through the reduction of m⁶A modification on PPARG transcripts [ 38 ]. METTL3 has also been implicated in adipogenesis, as its knockdown enhances JAK1 (Janus Kinase 1) mRNA stability by reducing m⁶A modification, which subsequently upregulates C/EBPβ (CCAAT Enhancer Binding Protein Beta) expression and promotes adipocyte differentiation [ 39 ]. Beyond mammalian adipogenesis, m⁶A modification is increasingly recognized for its association with economically important traits in livestock. Dynamic m⁶A changes have been observed during follicular and testicular development in pigs [ 40 ]. Moreover, MeRIP-seq analyses in chicken muscle suggest that m⁶A modification is associated with the regulation of fat deposition [ 41 , 42 ]. In cattle, m⁶A regulates muscle cell differentiation by modulating MEF2C [ 43 ]. However, transcriptome-wide mapping of m⁶A during bovine preadipocyte differentiation remains limited, leaving an important research gap unaddressed. This study characterizes m⁶A modification patterns during bovine preadipocyte differentiation and investigates the potential regulatory relevance of m⁶A dynamics. Furthermore, we functionally examine the contribution of the m⁶A writer METTL14 and identify candidate genes potentially regulated by METTL14 to advance our understanding of m⁶A-associated regulation in bovine adipogenesis. Materials and methods Ethics statement All animal experiments were conducted in strict accordance with institutional, national, and international guidelines for the care and use of animals in research. In particular, all procedures adhered to Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes. The experimental cattle were obtained from the breeding farm of Northwest A&F University. All procedures involving animals were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) of Northwest A&F University (Ethics Committee Name: Institutional Animal Care and Use Committee of Northwest A&F University; Approval Number: DK2021042). Prior to tissue collection, the animals were gently restrained and rendered unconscious by electrical stunning. Loss of consciousness was confirmed by the presence of tonic spasm, fixed eyeballs, absence of the corneal reflex, and lack of struggling. The animals were then immediately exsanguinated by severing the carotid arteries and jugular veins, and tissues were collected promptly thereafter. No pharmacological anaesthetic was used in this study in order to avoid potential interference of anaesthetic residues with primary cell isolation, cell viability, and subsequent experimental results. Isolation and culture of bovine preadipocytes Primary bovine preadipocytes were isolated from the perirenal adipose tissue of three 1-day-old Qinchuan calves raised under identical feeding and management conditions, following a previously established method from our laboratory [ 44 ]. Briefly, adipose tissue was collected under sterile conditions, washed three times with PBS containing antibiotics, and carefully cleared of connective tissue and blood vessels. The tissue was minced into approximately 1 mm³ fragments and digested with type I collagenase at 37 °C for 90 min with gentle agitation. The digestion was terminated by adding an equal volume of complete culture medium, and the cell suspension was filtered through 200 μm and 70 μm cell strainers. After centrifugation (1000 rpm, 10 min), erythrocytes were removed using red blood cell lysis buffer. The cells were washed twice with serum-free medium, resuspended in basal medium, and seeded into T75 culture flasks (NEST Biotechnology, China). Cells were maintained at 37 °C in a humidified incubator with 5% CO₂, and the medium was replaced after 24 h to remove non-adherent cells. Upon reaching approximately 90% confluence, the medium was replaced with Induction Medium I. After two days, the medium was changed to Induction Medium II to promote adipocyte differentiation. The basal medium contained 90% DMEM/F12, 10% fetal bovine serum (NEST Biotechnology, China), and 1% penicillin-streptomycin solution. Induction Medium I was prepared by supplementing the basal medium with 0.5 mM 3-isobutyl-1-methylxanthine (IBMX, Beijing Solarbio, China), 5 µg/mL insulin, 1 µM dexamethasone, and 1 µM rosiglitazone. Induction Medium II was formulated by adding only 5 µg/mL insulin to the basal medium. Cells were passaged using 0.25% trypsin (NEST Biotechnology, China) digestion. Cell identification Preadipocytes at 90% confluence were subjected to Pref-1 immunofluorescence staining (Supplementary Fig. 1A) using the protocol previously established in our laboratory [ 45 ]. Quantification of m⁶A content by LC-MS/MS Total RNA was isolated from bovine preadipocytes at various time points during differentiation. mRNA was purified with the BeyoMag™ Magnetic Beads mRNA Purification Kit (Beyotime Biotechnology, China). A 20 µL digestion reaction containing 100 ng of purified mRNA, 2 U nuclease P1, 25 mM NaCl, and 1.5 mM ZnCl₂ was incubated at 37 °C for 2 h. The reaction mixture was then supplemented with 3 µL of 1 M NH₄HCO₃ and 1 U alkaline phosphatase (New England Biolabs, China), followed by a further 2-hour incubation at 37 °C. After the reaction, the mixture was diluted five-fold with deionized water and centrifuged at 10,000 rpm and 4 °C for 5 min. The supernatant was collected for further analysis. The resulting samples were analyzed on an Agilent 1290 Infinity II–6470 LC–MS/MS system to quantify m⁶A and adenosine (A) levels and calculate the m⁶A/A ratio. Quantification of m⁶A RNA methylation (colorimetric method) The m⁶A content was measured with the EpiQuik™ m⁶A RNA Methylation Quantification Kit (Colorimetric) from Epigentek (USA). Total RNA was extracted from bovine preadipocytes collected at different days of differentiation, and 300 ng of this RNA was used for each m⁶A assay. The RNA was first immobilized on the well surfaces with a high-efficiency binding solution to ensure stable attachment while preserving its native conformation. m⁶A sites were then specifically captured by a bound antibody. A detection antibody was introduced, forming an immunocomplex with the capture antibody. After adding a signal enhancement solution to amplify the detection signal, a color development solution was applied. This immunocomplex catalyzed a colorimetric reaction where the intensity of the resulting blue color was directly proportional to the m⁶A content. The reaction was stopped with a stop solution once the positive control wells developed a moderate blue color, changing the solution from blue to yellow. The absorbance at 450 nm (OD value) was finally recorded using a microplate reader. The absolute m⁶A content or relative percentage in the samples was determined from the linear relationship between the OD values and a standard curve, allowing for the quantification of RNA m⁶A modification levels. Dot blot The purified mRNA was diluted to 100 ng/µL, denatured at 95 °C for 3 min, and immediately chilled on ice. A 1 µL aliquot of the sample was applied to a Hybond-N⁺ nylon membrane (GE Healthcare, USA) and air-dried at room temperature followed by UV cross-linking for 20 min. After that, the membrane was blocked with rapid blocking buffer (Beyotime Biotechnology, China) for 30 min at room temperature, and incubated overnight at 4 °C with an m⁶A-specific primary antibody (Synaptic Systems GmbH, Germany). The next day, the membrane was incubated with an HRP-conjugated secondary antibody for 2 h at room temperature. After incubation, a chemiluminescent substrate (UElandy, Suzhou, China) was applied for signal detection, and signals were detected and imaged using a protein imaging system (BIO-RAD, USA). After that, the membrane was stained for 30 min with 0.02% methylene blue solution prepared in 0.3 M sodium acetate, rinsed with deionized water, and imaged under white light to confirm uniform RNA loading. RNA isolation and sequencing library construction Total RNA was isolated from bovine preadipocytes at differentiation D0, D2 and D8 using the TRIzol reagent (Accurate Biotechnology, China). The RNA samples were subsequently sent to Novogene Co., Ltd. (China) for both MeRIP-seq and RNA-seq analyses. RNA integrity and concentration were assessed using an Agilent 2100 Bioanalyzer (Agilent) and a SimpliNano spectrophotometer (GE Healthcare). Fragmented mRNA (~ 100 nt) was immunoprecipitated with an m 6 A antibody (Synaptic Systems) at 4 °C for 2 h. Library construction was then performed using the NEBNext ® Ultra™ II RNA Library Prep Kit for Illumina ® (New England Biolabs). The resulting libraries were sequenced on an Illumina platform using a paired-end 150 bp (PE150) strategy. Three biological replicates were included for each experimental group. RNA-seq data analysis Raw sequencing reads were subjected to quality control using fastp (v0.19.11), which removed adapter sequences, low-quality reads, and reads with excessive N bases to yield high-quality clean reads. The Q20, Q30, and GC content of these clean reads were then calculated to assess data quality. All downstream analyses were performed using these high-quality reads. The reference genome (ARS-UCD1.2) was indexed using HISAT2 (v2.0.5), and the clean reads were aligned to it. Read counts for each gene were quantified using Subread (v1.5.0-p3). The FPKM (Fragments Per Kilobase of transcript per Million mapped reads) for each gene was calculated from its length and the corresponding read counts. Differential expression analysis between experimental groups was performed with the DESeq2 R package (v1.14.1). The DEGs were defined by the thresholds |log₂FC| ≥ 1 and an adjusted P -value (padj) < 0.05. MeRIP-Seq data analysis We identified transcriptome-wide m⁶A-enriched regions (peaks) using the R package exomePeak (v2.16.0), applying a significance threshold of FDR < 0.05. Input RNA-seq data were used as the background control to improve the accuracy of peak detection. The detected peaks were annotated according to genomic features, and genes containing peaks located in exonic regions were designated as m⁶A peak-associated genes. To characterize the distribution of m⁶A modifications across transcripts, each peak was assigned to one of five mRNA regions: transcription start site (TSS), 5′UTR, coding sequence (CDS), stop codon region (STOP), or 3′UTR. We then visualized the distribution and regional enrichment of these peaks using the R package Guitar, which highlights positional trends of m⁶A along transcripts. Using HOMER software (v4.9.1), we performed motif analysis on sequences from significantly enriched m⁶A peaks to identify consensus motifs. Differential m⁶A peak analysis between comparison groups was performed using exomePeak (parameters: P ≤ 0.05 and |fold change| ≥ 1.5), and the identified differential peaks were subsequently annotated. cDNA synthesis and qRT-PCR The cDNA library was constructed with reagent Evo M-MLVRT Mix Kit with gDNA Clean for qPCR Ver.2 (Accurate Biotechnology, China). Quantitative real-time polymerase chain reaction (qRT-PCR) was then performed using PerfectStart ® Green qPCR SuperMix (TransGen Biotech, China). Relative gene expression levels were calculated using the 2^-ΔΔCt method. ACTB was used as the internal reference gene. Primer sequences are listed in Supplementary Table 1. m 6 A immunoprecipitation quantitative real-time PCR (m 6 A-IP-qPCR) Total RNA was isolated from bovine preadipocytes on differentiation D0, D2, and D8. Primers targeting approximately 300 bp regions flanking the m 6 A modification sites predicted by SRAMP were designed [ 46 , 47 ], with sequences listed in Supplementary Table 1. RNA immunoprecipitation was performed using the CUT&RUN m 6 A RNA Enrichment (MeRIP) Kit (Epigentek, New York, USA). Magnetic beads conjugated with RNA were incubated with an anti-m 6 A antibody, followed by digestion of unbound RNA fragments using an RNA cleavage enzyme. The immunoprecipitated RNA was eluted from the beads and reverse transcribed. The qRT-PCR was then conducted using PerfectStart ® Green qPCR SuperMix. Relative quantification was performed using the 2^-ΔΔCt method, with the input RNA sample as the control. H&E staining Longissimus dorsi muscle samples were collected from Qinchuan beef Cattle (QC) and Wagyu. Following paraformaldehyde fixation, the specimens were embedded in paraffin, sectioned, and stained with hematoxylin and eosin. Images were captured using an Olympus microscope (Olympus Corporation, Tokyo, Japan). Oil Red O Staining Adipocytes differentiated for 8 days were washed twice with PBS and fixed in paraformaldehyde. After 30 min, the cells were stained with Oil Red O staining kit (Beijing Solarbio Science & Technology Co., Ltd., China). Excess dye was removed by washing, and images were captured using an Olympus microscope (Olympus Corporation, Tokyo, Japan). BODIPY staining Adipocytes differentiated for 8 days were fixed and then incubated with a 10µM BODIPY staining solution. Following a PBS wash, the cells were stained with DAPI for 10 min. After removing excess dye with further washes, images were acquired randomly using an EVOS™ Auto 2 Imaging System (Thermo Fisher Scientific, USA). Triglyceride (TG) quantification Adipocytes subjected to differentiation for D0, D2 and D8 were collected, and their TG content was quantified according to established methods [ 48 ]. Statistical analysis and data visualization All data are reported as the mean ± SD from three independent biological replicates. Statistical analyses and graph generation were performed with GraphPad Prism 8.0.2 (GraphPad Software, USA), and data were visualized using RStudio (version 4.3.2). Differences among three or more groups were assessed by one-way analysis of variance (ANOVA), whereas comparisons between two groups were performed using an independent samples t-test. Statistical significance was defined as P < 0.05, and a threshold of P < 0.01 indicated high statistical significance. Result m⁶A modification is associated with IMF deposition and adipocyte differentiation To investigate the potential role of m 6 A modification in adipogenesis, we first quantified the IMF content in QC and Wagyu cattle. Wagyu exhibited a significantly higher IMF content than QC (Fig. 1 A-B). However, dot blot analysis revealed that the overall m 6 A modification level in QC Longissimus dorsi muscle tissue was significantly higher than in Wagyu. This observation was further supported by subsequent LC–MS/MS and colorimetric assays which consistently showed higher m⁶A levels in QC samples. (Fig. 1 C). Furthermore, lipid droplet accumulation and TG content were examined at three time points during the ex vivo differentiation of preadipocytes: pre-differentiation (D0), early differentiation (D2), and late differentiation (D8). Both lipid droplet accumulation and TG content increased significantly during adipocyte differentiation. (Fig. 1 D-E). Simultaneously, m 6 A levels decreased significantly during the differentiation process (Fig. 1 F). These findings suggest that m⁶A modification is associated with adipogenesis and may contribute to the differences in IMF deposition observed between QC and Wagyu. Fig. 1. Open in a new tab m⁶A modification is associated with IMF deposition and preadipocyte differentiation. A H&E staining of QC and Wagyu (10×, scale bar: 500 μm). B IMF content in the bovine Longissimus dorsi muscle. C (Left) Dot blot analysis of QC and Wagyu; (Middle and Right) m 6 A levels detected by colorimetric assay and LC-MS/MS, respectively. D Results of Bodipy staining (200×, scale bar: 275 μm). E Statistical data for cellular TG. F (Left) Dot blot analysis of bovine preadipocytes at D0, D2 and D8 of differentiation; (Middle and Right) m 6 A levels detected by colorimetric assay and LC-MS/MS, respectively. Data are presented as mean ± SD ( n = 3). Statistical significance between two groups was determined using an independent-samples t-test, while comparisons among three or more groups were analyzed by one-way ANOVA. ** indicates P < 0.01. Different lowercase letters indicate P < 0.05 The m⁶A writer METTL14 positively promotes adipogenesis METTL14, a core component of the m 6 A methyltransferase complex, plays an important role in regulating lipid deposition and adipogenesis. Comparison of METTL14 expression in Longissimus dorsi muscle tissue revealed significantly lower levels in QC than in Wagyu. (Fig. 2 A). We designed and transfected siRNA targeting METTL14 into bovine preadipocytes, resulting in an approximately 80% reduction in METTL14 expression eight days post-transfection. This knockdown markedly inhibited lipid droplet accumulation and significantly reduced the expression of adipogenesis-related genes (Fig. 2 B-D). To further investigate the regulatory role of METTL14 in adipogenesis, we performed RNA-seq on the METTL14 -knockdown adipocytes. The biological replicates showed high Pearson correlation coefficients, and principal component analysis (PCA) confirmed clear and consistent clustering (Fig. 2 E-F). Differential expression analysis identified 352 DEGs, with 133 upregulated and 219 downregulated (Fig. 2 G). Gene Ontology (GO) enrichment analysis revealed that the DEGs were significantly enriched in multiple adipogenesis-related biological processes, including negative regulation of fat cell differentiation, fatty acid oxidation, and regulation of triglyceride biosynthesis (Fig. 2 H). Gene Set Enrichment Analysis (GSEA) further revealed that METTL14 knockdown markedly suppressed key signaling pathways involved in lipid metabolism, such as the PPAR signaling pathway and fatty acid metabolism (Fig. 2 I). Fig. 2. Open in a new tab METTL14 positively promotes adipogenesis. A Relative mRNA expression levels of METTL14 in the Longissimus dorsi muscle of QC and Wagyu. B Relative mRNA expression levels following METTL14 knockdown in bovine adipocytes. C Quantification of intracellular TG content after METTL14 knockdown. D Oil Red O staining showing lipid accumulation after METTL14 knockdown (magnification ×200; scale bar = 50 μm). E PCA plot of RNA-seq samples. F Heatmap of Pearson correlation coefficients among RNA-seq samples. G Volcano plot of DEGs following METTL14 knockdown. H GO enrichment analysis of DEGs. I GSEA of pathways affected by METTL14 knockdown. Data are presented as mean ± SD ( n = 3). Statistical significance between two groups was determined using an independent-samples t-test. * P < 0.05, ** P < 0.01 Overview of m 6 A modification levels during bovine preadipocyte differentiation To investigate m 6 A modification patterns during adipogenesis, RNA was extracted from adipocytes at D0, D2, and D8 for MeRIP-seq analysis. PCA was initially performed to assess the overall distribution of the samples. The three biological replicates from each time point clustered together, demonstrating the stability of the sequencing data (Fig. 3 A). Pearson correlation analysis further confirmed strong correlations among samples within the same group, supporting their suitability for subsequent experimental analyses (Fig. 3 B). Over 95% of the clean reads from sequencing were successfully aligned to the bovine reference genome, with uniquely mapped reads also exceeding 95%, confirming the high quality and reliability of high-throughput sequencing data (Table 1 ). To characterize the transcriptome-wide distribution of m⁶A modifications, we analyzed the chromosomal localization of m⁶A peaks. These peaks were broadly distributed across all chromosomes without obvious chromosomal bias (Supplementary Fig. 1B). Mapping of m⁶A peaks to mRNA structural regions indicated that m⁶A modifications were predominantly enriched in CDS and 3’UTRs, with a notable concentration near the stop codon. Conversely, only minimal enrichment was observed in 5’UTRs and around the start codon (Fig. 3 C-D). The enriched m⁶A peaks contained the canonical DRACH (D = A/G/U, R = A/G, H = A/C/U) consensus motif (Fig. 3 E). During adipogenic differentiation of bovine preadipocytes, each transcript contained an average of approximately 0.57–0.64 m⁶A peaks. Among the m⁶A-modified transcripts, each exhibited approximately 1.83–1.92 peaks on average (Table 2 ). Further quantification revealed that over 75% of m⁶A-modified mRNAs contained 1–2 m⁶A peaks, whereas nearly 20% possessed at least 3 m⁶A peaks (Fig. 3 F). Fig. 3. Open in a new tab Overview of m 6 A modification levels during bovine preadipocyte differentiation. A PCA plot of MeRIP-seq sample. B Heatmap showing Pearson correlation coefficients among biological replicates. C Transcript-wide distribution pattern of m⁶A peaks across mRNA regions. D Proportion of m 6 A peaks distributed across mRNA regions, different lowercase letters indicate significant differences, with P < 0.05. E Sequence logo representation of enriched m⁶A consensus motifs identified from MeRIP-seq peaks at D0, D2, and D8. F Percentage distribution of transcripts containing different numbers of m⁶A peaks Table 1. Summary of MeRIP-seq data and read-alignment statistics in bovine preadipocytes Sample Clean reads Mapped reads Unique Mapped reads D0_1_Input 48,636,748 46,769,864(97.03%) 46,758,484(97.01%) D0_2_Input 43,910,582 41,881,692(97.14%) 41,870,274(97.11%) D0_3_Input 44,414,600 42,805,912(97.18%) 42,795,040(97.15%) D2_1_Input 46,089,750 44,310,424(97.06%) 44,299,066(97.03%) D2_2_Input 54,185,810 52,131,713(97.03%) 52,119,305(97.01%) D2_3_Input 48,230,390 46,345,045(97.06%) 46,333,879(97.03%) D8_1_Input 47,466,226 45,397,178(96.89%) 45,384,413(96.86%) D8_2_Input 53,042,180 50,586,200(97.14%) 50,571,604(97.11%) D8_3_Input 59,248,858 56,593,108(97.14%) 56,578,205(97.11%) D0_1_IP 47,298,158 44,935,052(95.78%) 44,917,036(95.75%) D0_2_IP 41,958,522 39,842,828(95.82%) 39,825,035(95.78%) D0_3_IP 43,287,094 41,163,491(95.77%) 41,144,993(95.72%) D2_1_IP 45,263,494 42,928,017(95.62%) 42,909,328(95.58%) D2_2_IP 40,856,514 38,826,142(95.77%) 38,808,554(95.73%) D2_3_IP 39,592,792 37,611,511(95.76%) 37,596,384(95.72%) D8_1_IP 41,012,922 39,000,311(95.77%) 38,982,276(95.73%) D8_2_IP 43,860,594 41,758,693(96.09%) 41,739,555(96.05%) D8_3_IP 41,315,590 39,219,562(95.72%) 39,203,837(95.68%) Open in a new tab Table 2. Number of m 6 A peaks during bovine preadipocyte differentiation Samples Total transcripts Total m 6 A transcripts Total m 6 A peaks m 6 A peaks per m 6 A transcripts m 6 A peaks per transcripts D0_1_IP 27,607 8881 16,833 1.90 0.61 D0_2_IP 27,607 8785 16,381 1.86 0.59 D0_3_IP 27,607 8528 15,679 1.83 0.57 D2_1_IP 27,607 8788 16,489 1.87 0.60 D2_2_IP 27,607 8895 16,918 1.90 0.61 D2_3_IP 27,607 8741 16,366 1.87 0.59 D8_1_IP 27,607 8711 16,076 1.85 0.58 D8_2_IP 27,607 8849 16,831 1.90 0.61 D8_3_IP 27,607 9197 17,666 1.92 0.64 Open in a new tab Analysis of DEGs across distinct stages of bovine preadipocyte differentiation During adipocyte differentiation, significant changes occur at the transcriptional level. Initially, DEGs in the early differentiation stage of adipocytes were analyzed, identifying a total of 1,245 significantly upregulated genes and 1,961 significantly downregulated genes (adjusted P < 0.05, |log₂FC| ≥ 1) (Fig. 4 A). GO enrichment analysis revealed that these DEGs were primarily involved in biological processes such as regulation of cell shape, long-chain fatty-acyl-CoA biosynthetic process, and response to insulin. Furthermore, KEGG pathway enrichment analysis showed that the DEGs were significantly enriched in key signaling pathways, including the MAPK signaling pathway, PI3K-Akt signaling pathway, and PPAR signaling pathway (Fig. 4 B). These results indicate the successful establishment of an adipocyte differentiation model. During the early differentiation phase, DEGs promote the initiation of adipocyte differentiation by regulating processes such as cell morphology, fatty acid synthesis, and insulin response, thereby activating multiple signaling pathways closely associated with adipogenesis. Fig. 4. Open in a new tab Analysis of DEGs across distinct stages of bovine preadipocyte differentiation. A Number of DEGs at D2 of adipocyte differentiation compared to D0. B GO and KEGG enrichment analysis of DEGs (D2 vs. D0). C Number of DEGs at D8 of adipocyte differentiation compared to D2. D GO and KEGG enrichment analysis of DEGs (D8 vs. D2) During the lipid deposition stage, the gene expression profile of adipocytes continued to undergo dynamic changes, with 1,583 genes being significantly upregulated and 977 genes significantly downregulated (adjusted P < 0.05, |log₂FC| ≥ 1) (Fig. 4 C). GO enrichment analysis demonstrated that the DEGs at this stage were mainly involved in metabolism-related processes, including fatty acid beta-oxidation, glucose homeostasis, and response to fatty acid. KEGG analysis indicated significant enrichment in signaling pathways such as the PI3K-Akt signaling pathway, PPAR signaling pathway, and Adipocytokine signaling pathway (Fig. 4 D). These findings indicate that during the lipid accumulation stage, adipocytes predominantly regulate the molecular regulatory network responsible for lipid droplet formation and maturation through modulation of pathways related to fatty acid metabolism and energy homeostasis. Collectively, the DEGs across distinct differentiation stages exhibited stage-specific functional characteristics: the early phase was primarily associated with the initiation of differentiation signals and cell fate determination, whereas the later phase focused on the precise regulation of lipid synthesis and energy metabolism. Analysis of DMGs at different stages of bovine preadipocyte differentiation To investigate the dynamic changes and functional roles of m 6 A modification during adipocyte differentiation, we first compared m 6 A modification profiles between the early differentiation stage (D2) and the undifferentiated state (D0). This analysis identified 4,767 differential m 6 A peaks, including 1,129 statistically significant peaks (FC > 1.5, P < 0.05) corresponding to 862 differentially methylated genes (DMGs). Among these DMGs, 624 genes exhibited significantly increased m 6 A modification levels, while 246 showed significantly decreased levels. Notably, 8 genes simultaneously contained both upregulated and downregulated modification sites (Fig. 5 A), suggesting the presence of multiple m 6 A sites with independent regulatory functions. GO enrichment analysis revealed that these DMGs were primarily involved in biological processes including multicellular organism development, negative regulation of cell growth, and regulation of cell shape (Fig. 5 C). KEGG pathway analysis indicated significant enrichment in the Hippo signaling pathway, FoxO signaling pathway, and PI3K-Akt signaling pathway (Fig. 5 D). These findings suggest that m 6 A modification plays an important role during early preadipocyte differentiation by regulating pathways related to cell morphology, proliferation, and development. Fig. 5. Open in a new tab Analysis of DMGs at different stages of bovine preadipocyte differentiation. A Overlapping genes between DMGs and DEGs at day D2 versus day D0. B The number of genes displaying both significant differential m 6 A modification and significant differential mRNA expression levels at day D2 relative to day D0. C GO enrichment analysis of DMGs (D2 vs. D0). D KEGG enrichment analysis of DMGs (D2 vs. D0). E Pearson correlation analysis between the fold change in m⁶A methylation and the fold change in mRNA expression (D2 vs. D0) During the lipid deposition stage (D8 vs. D2), we identified 6,774 differential m 6 A peaks, including 1,956 statistically significant peaks (FC > 1.5, P < 0.05) corresponding to 1,045 DMGs. Among these, 417 genes showed significantly increased methylation levels while 634 exhibited decreased levels, with 6 genes containing both upregulated and downregulated modification sites simultaneously (Fig. 6 A). GO enrichment analysis showed that these DMGs were mainly involved in intracellular signal transduction, positive regulation of glycolytic process, and positive regulation of fat cell differentiation (Fig. 6 C). KEGG analysis revealed significant enrichment in pathways including the MAPK signaling pathway, Cushing syndrome, and autophagy in animals (Fig. 6 D). These results revealed that m 6 A modification contributes to adipogenesis and the maintenance of energy homeostasis during lipid accumulation by regulating pathways involved in signal transduction, glucose metabolism, and autophagy. Fig. 6. Open in a new tab Analysis of DMGs at different stages of bovine preadipocyte differentiation. A Overlapping genes between DMGs and DEGs at day D8 versus day D2. B The number of genes displaying both significant differential m 6 A modification and significant differential mRNA expression levels at day D8 relative to day D2. C GO enrichment analysis of DMGs (D8 vs. D2). D KEGG enrichment analysis of DMGs (D8 vs. D2). E Pearson correlation analysis between the fold change in m⁶A methylation and the fold change in mRNA expression (D8 vs. D2) To explore the relationship between m 6 A modification and gene expression, we integrated the data on m 6 A modification levels and mRNA expression changes. Between D2 and D0, 400 genes showed significant changes at both m 6 A and mRNA levels, suggesting their potential involvement in early preadipocyte differentiation via m 6 A modification. Quadrant analysis identified 13 Hyper_up (hypermethylation and upregulated expression), 331 Hyper_down (hypermethylated and downregulated expression), 35 Hypo_up (hypomethylated and upregulated expression), and 21 Hypo_down genes (hypomethylated and downregulated expression) (Fig. 5 B). Between D8 and D2, we identified 592 genes with simultaneous significant changes, comprising 2 Hyper_up, 191 Hyper_down, 398 Hypo_up, and 1 Hypo_down genes (Fig. 6 B). These results suggest that these genes may contribute to regulating bovine preadipocyte differentiation through m 6 A modification. Finally, Pearson correlation analysis was performed to evaluate the relationship between m 6 A modification levels and mRNA expression changes. The results indicated a moderate positive correlation (|r| > 0.5) between D2 and D0 (Fig. 5 E). This correlation strengthened further between D8 and D2 (|r| > 0.7) (Fig. 6 E), suggesting that the association of m 6 A modification on gene expression becomes more pronounced during differentiation. To identify key functional target genes, we intersected DEGs from METTL14 interference experiments with candidate gene sets from the quadrant analyses of both differentiation stages. In differentiating preadipocytes, 19 candidate genes were screened (Supplementary Table 2), including PPARGC1B (PPARG Coactivator 1 Beta) and NKD1 (NKD Inhibitor Of Wnt Signaling Pathway 1), which are closely associated with preadipocyte differentiation. At the adipogenesis stage, 43 genes were identified (Supplementary Table 3), including PLPP3 (Phospholipid Phosphatase 3) and ADIPOQ (Adiponectin), which directly participate in lipid deposition. The m 6 A methylation peaks of these genes were then visualized in the IGV browser (Fig. 7 A-B), and their methylation sites were predicted using an online platform ( http://www.cuilab.cn/sramp ) (Fig. 7 C-D). The m 6 A-IP-qPCR assays validated the m 6 A methylation levels of the relevant genes (Fig. 7 E). These genes not only reflect the dynamic regulatory characteristics of m 6 A modification but also play crucial roles in adipogenesis, potentially representing candidate functional targets associated with the METTL14–m 6 A regulatory axis. Fig. 7. Open in a new tab Prediction and Validation of m⁶A Methylation Sites for Selected Genes. A IGV visualization shows the m⁶A methylation site distribution in the PPARGC1B gene. B An online database predicts potential m⁶A sites in PPARGC1B . C The distribution of m⁶A methylation sites in the ADIPOQ gene is visualized using IGV. D Potential m⁶A sites in ADIPOQ were predicted with an online database. E m⁶A-IP-qPCR validated the enrichment of m⁶A modifications in PPARGC1B and ADIPOQ Discussion Qinchuan cattle rank among China’s five principal indigenous yellow cattle breeds, valued for their meat quality and robust adaptability [ 49 ]. However, key economic traits—especially IMF content—remain lower than those of elite foreign breeds such as Wagyu [ 50 ]. Our findings confirm that IMF content in QC is significantly lower than in Wagyu. Therefore, this study aims to explore potential epigenetic mechanisms associated with IMF deposition in QC, with the goal of providing insights for meat quality improvement. As the first identified internal mRNA modification [ 51 ], m 6 A plays a critical role in regulating mRNA stability and function [ 52 ]. Recent evidence indicates that m 6 A modification has been implicated in the regulation of various economic traits in domestic animals [ 53 , 54 ]. Comparison of m⁶A modification levels in IMF from QC and Wagyu revealed significantly lower global m⁶A levels in the high-fat-content Wagyu compared with QC. Interestingly, although METTL14 expression was significantly higher in Wagyu compared with QC, the global m⁶A level was lower in Wagyu. At first glance, this observation appears inconsistent with the established role of METTL14 as a core component of the m⁶A methyltransferase complex. However, global m⁶A abundance represents the integrated outcome of multiple regulatory factors, including methyltransferases, demethylases, transcriptome composition, and transcript-specific methylation dynamics. This apparent discrepancy likely reflects differences between complex tissue composition and the in vitro adipocyte differentiation system. Intramuscular tissue represents a heterogeneous environment dominated by myofibers, whereas adipocytes account for only a small fraction of total RNA. Therefore, global m⁶A levels measured in muscle largely reflect composite transcriptomic contributions from multiple cell types rather than adipocytes alone. In contrast, the in vitro differentiation model captures intrinsic m⁶A dynamics within a relatively homogeneous adipocyte population. These findings suggest that global m⁶A abundance may not serve as a direct linear indicator of fat deposition capacity, and that adipogenesis may be more strongly influenced by transcript-specific m⁶A remodeling rather than overall methylation quantity. Therefore, changes in the expression of a single writer component do not necessarily lead to proportional alterations in total m⁶A levels. Moreover, biological effects related to IMF deposition are more likely mediated by methylation changes at specific functional transcripts rather than by overall m⁶A abundance. Studies in pigs and chickens have also reported divergent or species-specific trends. A negative correlation was observed between m 6 A modification levels and fat deposition capacity in the intramuscular adipose tissue of Jinhua pigs (fat-type breed) and Landrace pigs (lean-type breed) [ 55 ]. Conversely, chicken leg muscle, which possesses higher IMF content, exhibited increased m 6 A modification levels [ 41 ], a phenomenon potentially linked to the generally low IMF in chickens [ 56 ]. Furthermore, we measured TG content and m 6 A modification at three key time points during adipocyte differentiation. The m 6 A levels decreased significantly as lipid droplets progressively accumulated during differentiation. However, studies in 3T3-L1 cells reported the opposite trend [ 57 ]. This discrepancy may arise from species-specific differences in adipocyte differentiation regulatory networks and the expression patterns of m⁶A-related enzymes, suggesting that m⁶A exerts a complex, context-dependent role in fat deposition. This context dependency may arise from multiple factors. First, the expression levels and relative activities of m⁶A writers, erasers, and readers vary across species and tissues, potentially leading to distinct regulatory outcomes. Second, species-specific differences in m⁶A motif distribution and sequence architecture may influence m⁶A deposition efficiency and reader recognition. For example, previous studies reported GGACA as the predominant motif in chicken intramuscular fat, whereas GGACU was enriched in porcine muscle. In our bovine data, we observed broader enrichment of (G/U/A)GACU motifs. Although these motifs all conform to the canonical DRACH consensus, subtle variations in sequence context may modulate methyltransferase recruitment or reader-binding affinity, thereby contributing to species-specific regulatory effects. METTL14 , a core component of the m⁶A methyltransferase complex, forms a heterodimer with METTL3 to co-catalyze mRNA adenosine methylation at the N6 position [ 58 ]. It primarily recognizes and binds RNA substrates, thereby enhancing the specificity and stability of the methylation process. METTL14 contributes to the regulation of global m⁶A levels and influences gene expression post-transcriptionally by modulating mRNA splicing, stability, nuclear export, and translation efficiency [ 59 , 60 ]. Previous studies suggested that METTL14 participates in the regulation of preadipocyte fate through m⁶A-dependent mechanisms during adipogenesis, identifying it as an important epigenetic regulator of adipogenesis. The METTL3–METTL14–WTAP complex promotes preadipocyte proliferation and early differentiation by regulating mitotic clonal expansion in 3T3-L1 cells, thereby establishing a foundation for lipid droplet formation [ 61 ]. Reduced METTL14 expression, in turn, inhibits adipogenesis by affecting genes such as JAK2 (Janus Kinase 2) and STAT3 (Signal Transducer And Activator of Transcription 3) [ 62 ]. Further in vivo studies demonstrated that adipose tissue-specific METTL14 knockout significantly inhibits high-fat diet-induced obesity and adipose tissue expansion, whereas METTL14 overexpression curtails lipolysis-related pathways via m⁶A modification and promotes fat deposition [ 63 ]. Our results similarly show that METTL14 knockdown significantly inhibits adipogenesis and decreases the expression of key adipogenic genes. RNA-seq analysis, however, revealed that METTL14 interference affected only a limited number of DEGs. To gain further insight, we therefore performed GSEA, which indicated that METTL14 interference markedly suppresses adipogenesis and activity of associated signaling pathways. These findings suggest that METTL14 may regulate adipocyte differentiation not primarily through large-scale transcriptional reprogramming, but via m⁶A-mediated post-transcriptional control of specific gene sets. The decrease in m⁶A modification caused by METTL14 deficiency likely disrupts the stability and translation efficiency of adipogenesis-related mRNAs, ultimately impairing adipocyte differentiation and lipid droplet formation. The development of MeRIP-seq has provided a powerful tool for mapping RNA m 6 A modifications and their functions [ 64 ]. However, the role of m 6 A in bovine adipogenesis remains largely unexplored, and the m 6 A modification landscape in these cells has not yet been characterized. To investigate the potential functions of m 6 A during bovine adipogenesis, we conducted transcriptome-wide m 6 A sequencing of bovine preadipocytes at differentiation D0, D2 and D8. Biological analysis indicated that DEGs were significantly enriched in biological processes associated with early preadipocyte differentiation and late lipid synthesis, supporting the biological relevance of our sequencing data. We found that 30.89–33.31% of transcripts contained m 6 A methylation during bovine preadipocyte differentiation. A comparable proportion (~ 30.77%) of m 6 A-modified transcripts was reported in human adipocytes using MeRIP-seq data [ 65 ]. Similarly, approximately 24.3–33.9% of transcripts were m 6 A-methylated in bovine muscle cells [ 15 ], aligning with our observations. Furthermore, over 75% of m 6 A-modified transcripts contained at least 1–2 peaks. Analysis of m 6 A peak distribution on mRNAs revealed enrichment within the coding sequence (CDS) and 3′ untranslated region (3′UTR). These modifications primarily occurred in the (G/U/A)GACU motif, which matches the mammalian DRACH consensus and is consistent with established distribution patterns [ 34 , 66 , 67 ]. The functional consequences of m 6 A modification are known to depend on its location. Modifications within the 3′UTR and CDS that are recognized by YTHDF1 enhance translation by increasing ribosome occupancy [ 68 , 69 ], while those bound by YTHDF2 promote mRNA degradation via recruitment of the CCR4–NOT deadenylase complex [ 70 ]. YTHDF3 is proposed to play a dual role, cooperating with YTHDF1 to facilitate translation and with YTHDF2 to accelerate decay, thereby acting as a bridge in determining mRNA fate [ 21 , 71 ]. IGF2BPs primarily bind to the CDS and 3′UTR; when m 6 A modifications are recognized by IGF2BP1/2/3, they recruit RNA-stabilizing complexes that protect mRNAs from degradation [ 72 , 73 ]. Integrated analysis of MeRIP-seq and RNA-seq data revealed a significant moderate correlation between changes in gene m⁶A levels and gene expression during preadipocyte differentiation. A strong correlation between m⁶A level changes and gene expression changes emerged during adipogenesis, consistent with observations in goose embryonic muscle [ 74 ]. Interestingly, during bovine muscle differentiation, changes in mRNA expression levels showed no significant correlation with alterations in m⁶A modification [ 15 ]. During porcine embryonic muscle development, WGCNA identified two modules in which mRNA expression changes showed significant positive and negative correlations, respectively, with m⁶A level changes. These findings collectively indicate that the relationship between m⁶A modification and gene expression is highly species- and tissue-specific [ 75 , 76 ]. To identify candidate differentially m⁶A-methylated genes potentially regulated by METTL14, we intersected genes from the differential expression analysis following METTL14 knockdown with those derived from four-quadrant plots analysis of early and late differentiation stages. During the early differentiation stage, this approach highlighted genes including SPP1 (Secreted Phosphoprotein 1), DKK2 (Dickkopf Wnt Signaling Pathway Inhibitor 2), WNT4 (Wnt Family Member 4), and PPARGC1B (PPARG Coactivator 1 Beta). Notably, previous studies in porcine mesenchymal stem cell adipogenic differentiation models have shown that SPP1 overexpression inhibits the expression of adipogenic marker genes [ 77 ]. While WNT4 represents a key WNT signaling pathway component, DKK2 functions as its inhibitor [ 78 ], suggesting the involvement of the WNT pathway in early preadipocyte differentiation. WNT pathway activation is known to strongly suppress preadipocyte differentiation [ 79 ]. PPARGC1B has also been identified as associated with fat deposition [ 80 ]. During adipogenesis, we identified candidate genes associated with adipogenesis and lipid droplet accumulation, including LPIN1 (Lipin 1), LIPE (Lipase E), ADIPOQ , and PLIN4 (Perilipin 4). LPIN1 plays an important role in TG synthesis and adipose tissue development, with its deficiency resulting in impaired adipose development [ 81 ]. Although LPIN1 functions as a phosphatidic acid phosphatase involved in triglyceride synthesis, it also contributes to adipocyte differentiation and lipid droplet homeostasis, where alterations in its activity or expression affect lipid accumulation in preadipocytes [ 82 ]. ADIPOQ , an adipocyte-secreted factor, serves not only as a mature adipocyte marker but also enhances adipogenic gene expression and increases lipid accumulation through autocrine/paracrine mechanisms [ 83 ]. Interestingly, one study found ADIPOQ promotes adipogenesis in an m⁶A-dependent manner during porcine intramuscular adipogenesis [ 84 ]. PLIN4 is a member of the perilipin family of lipid droplet-associated proteins. Experimental evidence indicates that it is upregulated during mid-to-late stages of adipocyte differentiation, where it plays a key role in lipid droplet formation and the maintenance of cellular lipid homeostasis [ 85 ]. Finally, m⁶A modification levels of PPARGC1B and ADIPOQ were validated using m⁶A-IP-qPCR. Further investigation is required to determine how these genes regulate adipocyte differentiation and fat deposition through m⁶A modification. Based on our findings and observations across species, we propose that m⁶A modification may play distinct regulatory roles in fat deposition across different species. Conclusion This study characterizes the m⁶A modification landscape across key stages of bovine preadipocyte differentiation. The m⁶A writer METTL14 was found to play an important role in adipocyte lipid deposition. Furthermore, we identified candidate genes potentially influenced by METTL14-mediated m⁶A modification, such as PPARGC1B and ADIPOQ , which showed significant changes in both m⁶A levels and mRNA expression during differentiation. These findings provide new insights into the epigenetic regulation of adipogenesis in beef cattle, further supporting the involvement of m⁶A modification in lipid deposition. Furthermore, as aberrant lipid accumulation is a hallmark of obesity, our results suggest that m⁶A-mediated regulation of adipogenesis, particularly via METTL14, may offer insights into the epigenetic regulation of adipogenesis in broader metabolic contexts. Supplementary Information Supplementary Material 1. (16.6KB, docx) Supplementary Material 2. (15.3KB, docx) Supplementary Material 3. (15.4KB, docx) Acknowledgements Sample collection in this study was strongly supported by Jianqi Zan, Director of the Beef Cattle Breeding Farm at the National Beef Cattle Improvement Center, Northwest A&F University, for which we express our sincere gratitude. Abbreviations bp Base Pair CDS Coding Sequence cDNA Complementary DNA DEGs Differentially Expressed Genes DMGs Differentially Methylated Genes FC Fold Change FPKM Fragments Per Kilobase of Transcript per Million Mapped Reads GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes GSEA Gene Set Enrichment Analysis GWAS Genome-Wide Association Studies H&E Hematoxylin and Eosin HRP Horseradish Peroxidase IBMX 3-isobutyl-1-methylxanthine IMF Intramuscular fat IP Immunoprecipitation LC-MS/MS Liquid chromatography–tandem mass spectrometry m⁶A N6-methyladenosine MeRIP-seq methylated RNA immunoprecipitation sequencing nt Nucleotide OD Optical density PCA Principal component analysis QC Qinchuan beef cattle rpm Revolutions per minute TSS Transcription start site UTR Untranslated region Authors’ contributions Linsen Zan conceived and designed the experiments and acquired funding. Dianqi Zhang and Jiawei Du performed the experiments, conducted data analysis. Dianqi Zhang and Huaxuan Li wrote the original draft. Ni Gao and Hehan Qiu contributed to experimental validation and manuscript revision. Chugang Mei provided resources and supervised the project. All authors read and approved the final manuscript. Funding This work was supported by the National Key Research and Development Program of China (2023YFD1300103, 2023YFD1300101), the National Natural Science Foundation of China (32472877), the Special Fund for Science and Technology of the Ministry of Agriculture and Rural Affairs (22250059), the Shaanxi Provincial Agricultural Core Technology Research Program (K3031224096, 2024NYGG005), the Shaanxi Provincial Double-Chain Integration Special Project (2022GD-TSLD-46-0102), and the China Agriculture Research System of Beef and Yak (CARS-37). Data availability The raw sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA1364174. Additional data are available from the corresponding author upon reasonable request. Declarations Ethics approval and consent to participate All animal procedures were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) of Northwest A&F University (Approval No. DK2021042). All animal experiments were conducted in accordance with institutional, national, and international guidelines for the care and use of animals in research, and complied with Directive 2010/63/EU of the European Parliament on the protection of animals used for scientific purposes. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Dianqi Zhang and Jiawei Du contributed equally to this work. Contributor Information Chugang Mei, Email: meichugang@163.com. Linsen Zan, Email: Zanlinsen@163.com. References 1. Hoa VB, Kim DG, Song DH, Ko JH, Kim HW, Bae IS, Kim YS, Cho SH. Quality Properties and Flavor-Related Components of Beef Longissimus Lumborum Muscle from Four Korean Native Cattle Breeds. Food Sci Anim Resour. 2024;44(4):832–48. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. Hirai S, Kawai A, Mizuno Y, Sasaki S, Iida F. Effect of intramuscular fat content on the sensory characteristics and dynamic flavor attributes of Japanese black cattle beef. Anim Sci J. 2023;94(1):e13841. [ DOI ] [ PubMed ] [ Google Scholar ] 3. Tan Z, Jiang H. Molecular and cellular mechanisms of intramuscular fat development and growth in cattle. Int J Mol Sci. 2024;25(5):2520. [ DOI ] [ PMC free article ] [ PubMed ] 4. Liu WW, Zheng SQ, Li T, Fei YF, Wang C, Zhang S, Wang F, Jiang GM, Wang H. RNA modifications in cellular metabolism: implications for metabolism-targeted therapy and immunotherapy. Signal Transduct Target Ther. 2024;9(1):70. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Qin Y, Li L, Luo E, Hou J, Yan G, Wang D, Qiao Y, Tang C. Role of m6A RNA methylation in cardiovascular disease (Review). Int J Mol Med. 2020;46(6):1958–72. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 6. Wei G. RNA m6A modification, signals for degradation or stabilisation? Biochem Soc Trans. 2024;52(2):707–17. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 7. He L, Li H, Wu A, Peng Y, Shu G, Yin G. Functions of N6-methyladenosine and its role in cancer. Mol Cancer. 2019;18(1):176. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. Zhao BS, Roundtree IA, He C. Post-transcriptional gene regulation by mRNA modifications. Nat Rev Mol Cell Biol. 2017;18(1):31–42. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Wang P, Doxtader KA, Nam Y. Structural Basis for Cooperative Function of Mettl3 and Mettl14 Methyltransferases. Mol Cell. 2016;63(2):306–17. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Huang J, Dong X, Gong Z, Qin LY, Yang S, Zhu YL, Wang X, Zhang D, Zou T, Yin P, et al. Solution structure of the RNA recognition domain of METTL3-METTL14 N(6)-methyladenosine methyltransferase. Protein Cell. 2019;10(4):272–84. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 11. Ping XL, Sun BF, Wang L, Xiao W, Yang X, Wang WJ, Adhikari S, Shi Y, Lv Y, Chen YS, et al. Mammalian WTAP is a regulatory subunit of the RNA N6-methyladenosine methyltransferase. Cell Res. 2014;24(2):177–89. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 12. Patil DP, Chen CK, Pickering BF, Chow A, Jackson C, Guttman M, Jaffrey SR. m(6)A RNA methylation promotes XIST-mediated transcriptional repression. Nature. 2016;537(7620):369–73. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Wen J, Lv R, Ma H, Shen H, He C, Wang J, Jiao F, Liu H, Yang P, Tan L, et al. Zc3h13 Regulates Nuclear RNA m(6)A Methylation and Mouse Embryonic Stem Cell Self-Renewal. Mol Cell. 2018;69(6):1028–38. e1026. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 14. Li N, Zhu Z, Deng Y, Tang R, Hui H, Kang Y, Rana TM. KIAA1429/VIRMA promotes breast cancer progression by m(6) A-dependent cytosolic HAS2 stabilization. EMBO Rep. 2023;24(10):e55506. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Yang X, Wang J, Ma X, Du J, Mei C, Zan L. Transcriptome-wide N (6)-Methyladenosine Methylome Profiling Reveals m(6)A Regulation of Skeletal Myoblast Differentiation in Cattle (Bos taurus). Front Cell Dev Biol. 2021;9:785380. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Jia G, Fu Y, Zhao X, Dai Q, Zheng G, Yang Y, Yi C, Lindahl T, Pan T, Yang YG, et al. N6-methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO. Nat Chem Biol. 2011;7(12):885–7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Azzam SK, Alsafar H, Sajini AA. FTO m6A demethylase in obesity and cancer: implications and underlying molecular mechanisms. Int J Mol Sci. 2022;23(7):3800. [ DOI ] [ PMC free article ] [ PubMed ] 18. Qu J, Yan H, Hou Y, Cao W, Liu Y, Zhang E, He J, Cai Z. RNA demethylase ALKBH5 in cancer: from mechanisms to therapeutic potential. J Hematol Oncol. 2022;15(1):8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Wang J, Wang J, Gu Q, Ma Y, Yang Y, Zhu J, Zhang Q. The biological function of m6A demethylase ALKBH5 and its role in human disease. Cancer Cell Int. 2020;20:347. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 20. Gao Z, Zha X, Li M, Xia X, Wang S. Insights into the m(6)A demethylases FTO and ALKBH5: structural, biological function, and inhibitor development. Cell Biosci. 2024;14(1):108. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 21. Zou Z, Sepich-Poore C, Zhou X, Wei J, He C. The mechanism underlying redundant functions of the YTHDF proteins. Genome Biol. 2023;24(1):17. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Zhou B, Feng X, Han C, Wu Y, Qiao J, Tong W, Xu W. YTHDF3 regulates IL32 mRNA stability to promote osteogenic differentiation of bone mesenchymal stem cells in ankylosing spondylitis. J Transl Med. 2025;23(1):604. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 23. Petri BJ, Klinge CM. m6A readers, writers, erasers, and the m6A epitranscriptome in breast cancer. J Mol Endocrinol. 2023;70(2):e220110. [ DOI ] [ PMC free article ] [ PubMed ] 24. Li Z, Peng Y, Li J, Chen Z, Chen F, Tu J, Lin S, Wang H. N(6)-methyladenosine regulates glycolysis of cancer cells through PDK4. Nat Commun. 2020;11(1):2578. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Zheng Y, Wang Y, Liu Y, Xie L, Ge J, Yu G, Zhao G. N6-Methyladenosine Modification of PTTG3P Contributes to Colorectal Cancer Proliferation via YAP1. Front Oncol. 2021;11:669731. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Li F, He C, Yao H, Zhao Y, Ye X, Zhou S, Zou J, Li Y, Li J, Chen S, et al. Glutamate from nerve cells promotes perineural invasion in pancreatic cancer by regulating tumor glycolysis through HK2 mRNA-m6A modification. Pharmacol Res. 2023;187:106555. [ DOI ] [ PubMed ] [ Google Scholar ] 27. Khoshi A, Bajestani MK, Shakeri H, Goodarzi G, Azizi F. Association of Omentin rs2274907 and FTO rs9939609 gene polymorphisms with insulin resistance in Iranian individuals with newly diagnosed type 2 diabetes. Lipids Health Dis. 2019;18(1):142. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 28. Hu F, Tong J, Deng B, Zheng J, Lu C. MiR-495 regulates macrophage M1/M2 polarization and insulin resistance in high-fat diet-fed mice via targeting FTO. Pflugers Arch. 2019;471(11–12):1529–37. [ DOI ] [ PubMed ] [ Google Scholar ] 29. Sun J, Liu G, Chen R, Zhou J, Chen T, Cheng Y, Lou Q, Wang H. PARP1 Is Upregulated by Hyperglycemia Via N6-methyladenosine Modification and Promotes Diabetic Retinopathy. Discov Med. 2022;34(172):115–29. [ PubMed ] [ Google Scholar ] 30. Zhang Q, Zhang Y, Chen H, Sun LN, Zhang B, Yue DS, Wang CL, Zhang ZF. METTL3-induced DLGAP1-AS2 promotes non-small cell lung cancer tumorigenesis through m(6)A/c-Myc-dependent aerobic glycolysis. Cell Cycle. 2022;21(24):2602–14. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 31. Xu W, Lai Y, Pan Y, Tan M, Ma Y, Sheng H, Wang J. m6A RNA methylation-mediated NDUFA4 promotes cell proliferation and metabolism in gastric cancer. Cell Death Dis. 2022;13(8):715. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 32. Kudou K, Komatsu T, Nogami J, Maehara K, Harada A, Saeki H, Oki E, Maehara Y, Ohkawa Y. The requirement of Mettl3-promoted MyoD mRNA maintenance in proliferative myoblasts for skeletal muscle differentiation. Open Biol. 2017;7(9):170119. [ DOI ] [ PMC free article ] [ PubMed ] 33. Dorn LE, Lasman L, Chen J, Xu X, Hund TJ, Medvedovic M, Hanna JH, van Berlo JH, Accornero F. The N(6)-Methyladenosine mRNA Methylase METTL3 Controls Cardiac Homeostasis and Hypertrophy. Circulation. 2019;139(4):533–45. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 34. Tao X, Chen J, Jiang Y, Wei Y, Chen Y, Xu H, Zhu L, Tang G, Li M, Jiang A, et al. Transcriptome-wide N (6) -methyladenosine methylome profiling of porcine muscle and adipose tissues reveals a potential mechanism for transcriptional regulation and differential methylation pattern. BMC Genomics. 2017;18(1):336. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 35. Huang C, Chen W, Wang X. Studies on the fat mass and obesity-associated (FTO) gene and its impact on obesity-associated diseases. Genes Dis. 2023;10(6):2351–65. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 36. Yin D, Li Y, Liao X, Tian D, Xu Y, Zhou C, Liu J, Li S, Zhou J, Nie Y, et al. FTO: a critical role in obesity and obesity-related diseases. Br J Nutr. 2023;130(10):1657–64. [ DOI ] [ PubMed ] [ Google Scholar ] 37. Rosen ED, Spiegelman BM. What we talk about when we talk about fat. Cell. 2014;156(1–2):20–44. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Shen GS, Zhou HB, Zhang H, Chen B, Liu ZP, Yuan Y, Zhou XZ, Xu YJ. The GDF11-FTO-PPARgamma axis controls the shift of osteoporotic MSC fate to adipocyte and inhibits bone formation during osteoporosis. Biochim Biophys Acta Mol Basis Dis. 2018;1864(12):3644–54. [ DOI ] [ PubMed ] [ Google Scholar ] 39. Yao Y, Bi Z, Wu R, Zhao Y, Liu Y, Liu Q, Wang Y, Wang X. METTL3 inhibits BMSC adipogenic differentiation by targeting the JAK1/STAT5/C/EBPbeta pathway via an m(6)A-YTHDF2-dependent manner. FASEB J. 2019;33(6):7529–44. [ DOI ] [ PubMed ] [ Google Scholar ] 40. Cao Z, Zhang D, Wang Y, Tong X, Avalos LFC, Khan IM, Gao D, Xu T, Zhang L. Identification and functional annotation of m6A methylation modification in granulosa cells during antral follicle development in pigs. Anim Reprod Sci. 2020;219:106510. [ DOI ] [ PubMed ] [ Google Scholar ] 41. Zhang T, Yu B, Cai Z, Jiang Q, Fu X, Zhao W, Wang H, Gu Y, Zhang J. Regulatory role of N(6)-methyladenosine in intramuscular fat deposition in chicken. Poult Sci. 2023;102(10):102972. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 42. Wang Z, Ju X, Li K, Cai D, Zhou Z, Nie Q. MeRIP sequencing reveals the regulation of N6-methyladenosine in muscle development between hypertrophic and leaner broilers. Poult Sci. 2024;103(6):103708. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 43. Yang X, Ning Y, Abbas Raza SH, Mei C, Zan L. MEF2C Expression Is Regulated by the Post-transcriptional Activation of the METTL3-m(6)A-YTHDF1 Axis in Myoblast Differentiation. Front Vet Sci. 2022;9:900924. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 44. Ma Z, Chen Y, Qiu J, Guo R, Cai K, Zheng Y, Zhang Y, Li X, Zan L, Li A. CircBTBD7 inhibits adipogenesis via the miR-183/SMAD4 axis. Int J Biol Macromol. 2023;253(Pt 2):126740. [ DOI ] [ PubMed ] [ Google Scholar ] 45. Ma X, Zhang D, Yang Z, Sun M, Gao N, Mei C, Zan L. bta-miR-484 Inhibits Bovine Intramuscular Adipogenesis by Regulating Mitotic Clonal Expansion via the MAP3K9/JNK/CCND1 Axis. J Agric Food Chem. 2025;73(2):1062–74. [ DOI ] [ PubMed ] [ Google Scholar ] 46. Zhou Y, Zeng P, Li YH, Zhang Z, Cui Q. SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features. Nucleic Acids Res. 2016;44(10):e91. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 47. Fan R, Cui C, Kang B, Chang Z, Wang G, Cui Q. A combined deep learning framework for mammalian m6A site prediction. Cell Genom. 2024;4(12):100697. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 48. Zhang D, Ma X, Li H, Li X, Wang J, Zan L. SERPINE1AS2 regulates intramuscular adipogenesis by inhibiting PAI1 protein expression. Int J Biol Macromol. 2024;275(Pt 2):133592. [ DOI ] [ PubMed ] [ Google Scholar ] 49. Yu H, Wang J, Zhang K, Cheng G, Mei C, Zan L. Integrated multi-omics analysis reveals variation in intramuscular fat among muscle locations of Qinchuan cattle. BMC Genomics. 2023;24(1):367. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 50. Yu H, Yu S, Guo J, Wang J, Mei C, Abbas Raza SH, Cheng G, Zan L. Comprehensive Analysis of Transcriptome and Metabolome Reveals Regulatory Mechanism of Intramuscular Fat Content in Beef Cattle. J Agric Food Chem. 2024;72(6):2911–24. [ DOI ] [ PubMed ] [ Google Scholar ] 51. Desrosiers R, Friderici K, Rottman F. Identification of methylated nucleosides in messenger RNA from Novikoff hepatoma cells. Proc Natl Acad Sci U S A. 1974;71(10):3971–5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 52. Corovic M, Hoch-Kraft P, Zhou Y, Hallstein S, Konig J, Zarnack K. m(6)A in the coding sequence: linking deposition, translation, and decay. Trends Genet. 2025;41(11):963–73. [ DOI ] [ PubMed ] 53. Yang X, Mei C, Raza SHA, Ma X, Wang J, Du J, Zan L. Interactive regulation of DNA demethylase gene TET1 and m(6)A methyltransferase gene METTL3 in myoblast differentiation. Int J Biol Macromol. 2022;223(Pt A):916–30. [ DOI ] [ PubMed ] [ Google Scholar ] 54. Mao C, You W, Yang Y, Cheng H, Hu X, Lan X, Song E. Comprehensive characterization of lncRNA N(6)-methyladenosine modification dynamics throughout bovine skeletal muscle development. J Anim Sci Biotechnol. 2025;16(1):36. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 55. Jiang Q, Sun B, Liu Q, Cai M, Wu R, Wang F, Yao Y, Wang Y, Wang X. MTCH2 promotes adipogenesis in intramuscular preadipocytes via an m(6)A-YTHDF1-dependent mechanism. FASEB J. 2019;33(2):2971–81. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 56. Hocquette JF, Gondret F, Baeza E, Medale F, Jurie C, Pethick DW. Intramuscular fat content in meat-producing animals: development, genetic and nutritional control, and identification of putative markers. Animal. 2010;4(2):303–19. [ DOI ] [ PubMed ] [ Google Scholar ] 57. Zhao X, Yang Y, Sun BF, Shi Y, Yang X, Xiao W, Hao YJ, Ping XL, Chen YS, Wang WJ, et al. FTO-dependent demethylation of N6-methyladenosine regulates mRNA splicing and is required for adipogenesis. Cell Res. 2014;24(12):1403–19. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 58. Xu Y, Zhang Y, Luo Y, Qiu G, Lu J, He M, Wang Y. Novel insights into the METTL3-METTL14 complex in musculoskeletal diseases. Cell Death Discov. 2023;9(1):170. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 59. Cun Y, Guo W, Ma B, Okuno Y, Wang J. Decoding the specificity of m(6)A RNA methylation and its implication in cancer therapy. Mol Ther. 2024;32(8):2461–9. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 60. Deng T, Ma J. Structures and mechanisms of the RNA m (6)A writer. Acta Biochim Biophys Sin (Shanghai). 2024;57(1):59–72. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 61. Kobayashi M, Ohsugi M, Sasako T, Awazawa M, Umehara T, Iwane A, Kobayashi N, Okazaki Y, Kubota N, Suzuki R et al. The RNA methyltransferase complex of WTAP, METTL3, and METTL14 regulates mitotic clonal expansion in adipogenesis. Mol Cell Biol. 2018;38(16):e00116–18. [ DOI ] [ PMC free article ] [ PubMed ] 62. Liu J, Ma C, Cheng Y, Wang M, Zhao G, Huang L, Song R, Wang X, Li H. METTL14 and WTAP play a crucial role in the regulation of bovine preadipocyte differentiation. Anim Biotechnol. 2025;36(1):2476531. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 63. Kang Q, Zhu X, Ren D, Ky A, MacDougald OA, O’Rourke RW, Rui L. Adipose METTL14-Elicited N(6) -Methyladenosine Promotes Obesity, Insulin Resistance, and NAFLD Through Suppressing beta Adrenergic Signaling and Lipolysis. Adv Sci (Weinh). 2023;10(28):e2301645. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 64. Dominissini D, Moshitch-Moshkovitz S, Salmon-Divon M, Amariglio N, Rechavi G. Transcriptome-wide mapping of N(6)-methyladenosine by m(6)A-seq based on immunocapturing and massively parallel sequencing. Nat Protoc. 2013;8(1):176–89. [ DOI ] [ PubMed ] [ Google Scholar ] 65. Xiao L, De Jesus DF, Ju CW, Wei JB, Hu J, DiStefano-Forti A, Gonzales VS, Tsuji T, Wei S, Bluher M, et al. Divergent roles of m(6)A in orchestrating brown and white adipocyte transcriptomes and systemic metabolism. Nat Commun. 2025;16(1):533. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 66. Jiang X, Liu B, Nie Z, Duan L, Xiong Q, Jin Z, Yang C, Chen Y. The role of m6A modification in the biological functions and diseases. Signal Transduct Target Ther. 2021;6(1):74. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 67. Dominissini D, Moshitch-Moshkovitz S, Schwartz S, Salmon-Divon M, Ungar L, Osenberg S, Cesarkas K, Jacob-Hirsch J, Amariglio N, Kupiec M, et al. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature. 2012;485(7397):201–6. [ DOI ] [ PubMed ] [ Google Scholar ] 68. Wang X, Zhao BS, Roundtree IA, Lu Z, Han D, Ma H, Weng X, Chen K, Shi H, He C. N(6)-methyladenosine Modulates Messenger RNA Translation Efficiency. Cell. 2015;161(6):1388–99. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 69. Spitale RC, Flynn RA, Zhang QC, Crisalli P, Lee B, Jung JW, Kuchelmeister HY, Batista PJ, Torre EA, Kool ET, et al. Structural imprints in vivo decode RNA regulatory mechanisms. Nature. 2015;519(7544):486–90. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 70. Xiao S, Duan S, Caligiuri MA, Ma S, Yu J. YTHDF2: a key RNA reader and antitumor target. Trends Immunol. 2025;46(6):485–98. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 71. Li A, Chen YS, Ping XL, Yang X, Xiao W, Yang Y, Sun HY, Zhu Q, Baidya P, Wang X, et al. Cytoplasmic m(6)A reader YTHDF3 promotes mRNA translation. Cell Res. 2017;27(3):444–7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 72. Duan M, Liu H, Xu S, Yang Z, Zhang F, Wang G, Wang Y, Zhao S, Jiang X. IGF2BPs as novel m(6)A readers: Diverse roles in regulating cancer cell biological functions, hypoxia adaptation, metabolism, and immunosuppressive tumor microenvironment. Genes Dis. 2024;11(2):890–920. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 73. Ma S, Qin Y, Ren W. Insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) in hematological diseases. Mol Med. 2024;30(1):165. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 74. Xu T, Xu Z, Lu L, Zeng T, Gu L, Huang Y, Zhang S, Yang P, Wen Y, Lin D, et al. Transcriptome-wide study revealed m6A regulation of embryonic muscle development in Dingan goose (Anser cygnoides orientalis). BMC Genomics. 2021;22(1):270. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 75. Shachar R, Dierks D, Garcia-Campos MA, Uzonyi A, Toth U, Rossmanith W, Schwartz S. Dissecting the sequence and structural determinants guiding m6A deposition and evolution via inter- and intra-species hybrids. Genome Biol. 2024;25(1):48. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 76. Liu J, Li K, Cai J, Zhang M, Zhang X, Xiong X, Meng H, Xu X, Huang Z, Peng J, et al. Landscape and Regulation of m(6)A and m(6)Am Methylome across Human and Mouse Tissues. Mol Cell. 2020;77(2):426–40. e426. [ DOI ] [ PubMed ] [ Google Scholar ] 77. Xiao L, Xu Q, Liu X, Chan S, Luo Y, He S, Fang M. The novel-miR-659/SPP1 interaction regulates fat deposition in castrated male pigs. Anim (Basel). 2022;12(8):944. [ DOI ] [ PMC free article ] [ PubMed ] 78. Pina JO, Raju R, Roth DM, Winchester EW, Padilla C, Iben J, Faucz FR, Cotney JL, D’Souza RN. Spatial Multi-omics Reveals the Role of the Wnt Modulator, Dkk2, in Palatogenesis’. J Dent Res. 2024;103(13):1412–20. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 79. Bagchi DP, MacDougald OA. Wnt Signaling: From Mesenchymal Cell Fate to Lipogenesis and Other Mature Adipocyte Functions. Diabetes. 2021;70(7):1419–30. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 80. Xing K, Wang K, Ao H, Chen S, Tan Z, Wang Y, Xitong Z, Yang T, Zhang F, Liu Y, et al. Comparative adipose transcriptome analysis digs out genes related to fat deposition in two pig breeds. Sci Rep. 2019;9(1):12925. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 81. Mitra MS, Chen Z, Ren H, Harris TE, Chambers KT, Hall AM, Nadra K, Klein S, Chrast R, Su X, et al. Mice with an adipocyte-specific lipin 1 separation-of-function allele reveal unexpected roles for phosphatidic acid in metabolic regulation. Proc Natl Acad Sci U S A. 2013;110(2):642–7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 82. Shen WJ, Yu Z, Patel S, Jue D, Liu LF, Kraemer FB. Hormone-sensitive lipase modulates adipose metabolism through PPARgamma. Biochim Biophys Acta. 2011;1811(1):9–16. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 83. Gao Y, Li F, Zhang Y, Dai L, Jiang H, Liu H, Zhang S, Chen C, Zhang J. Silencing of ADIPOQ efficiently suppresses preadipocyte differentiation in porcine. Cell Physiol Biochem. 2013;31(2–3):452–61. [ DOI ] [ PubMed ] [ Google Scholar ] 84. Gong H, Gong T, Liu Y, Wang Y, Wang X. Profiling of N6-methyladenosine methylation in porcine longissimus dorsi muscle and unravelling the hub gene ADIPOQ promotes adipogenesis in an m(6)A-YTHDF1-dependent manner. J Anim Sci Biotechnol. 2023;14(1):50. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 85. Griseti E, Bello AA, Bieth E, Sabbagh B, Iacovoni JS, Bigay J, Laurell H, Copic A. Molecular mechanisms of perilipin protein function in lipid droplet metabolism. FEBS Lett. 2024;598(10):1170–98. [ DOI ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary Material 1. (16.6KB, docx) Supplementary Material 2. (15.3KB, docx) Supplementary Material 3. (15.4KB, docx) Data Availability Statement The raw sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) under the BioProject accession number PRJNA1364174. Additional data are available from the corresponding author upon reasonable request. 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