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. 2026 Jul 27;105(11):107508. doi: 10.1016/j.psj.2026.107508

Identification of central regulators associated with serum triglyceride metabolism during growth and development in chickens

Wei Wei a, Najun Huang a, Chuchu Zhang a, Jingran Jiao a, Zihan Chen a, Hao Wang a, Chaomu Li b, Xing Guo a, Runshen Jiang a,⁎
PMCID: PMC13589130  PMID: 42727348

Abstract

Liver lipid metabolism is critical for chicken abdominal fat (AF) deposition, and serum triglyceride (TG) serves as an important indicator of lipid delivery from the liver to adipose tissue. However, the hepatic regulatory networks underlying serum TG metabolism during the growth and development of chickens remain poorly understood. In the present study, serum TG levels were measured in Wannan chickens at 9 W, 20 W, and 52 W. Liver transcriptome data from 24 Wannan chickens (n = 8 per stage) were analyzed using differential expression gene analysis, weighted gene co-expression network analysis (WGCNA), and Mfuzz clustering. Pearson correlation analysis revealed a strong positive association between TG content and abdominal fat percentage (AFP) (r = 0.777, p = 8.05 × 10⁻⁶). A total of 1048, 1407, and 234 differentially expressed genes (DEGs) were identified in the 9 W vs. 20 W, 9 W vs. 52 W, and 20 W vs. 52 W comparisons, respectively. WGCNA identified 58 co-expression modules, among which the plum2 module showed significant correlations with TG and AFP. Furthermore, several genes, including GPAM (glycerol-3-phosphate acyltransferase, mitochondrial), AGPAT2 (1-acyl-sn-glycerol-3-phosphate acyltransferase 2), MBOAT2 (membrane-bound O-acyltransferase domain containing 2), DHCR7 (7-dehydrocholesterol reductase), SQLE (squalene epoxidase), and SCD (stearyl CoA desaturase), involved in glycerolipid metabolism, steroid biosynthesis, and fatty acid metabolism, were identified as central regulators that contribute to the regulation of TG. These findings provide new insights into the hepatic regulatory mechanisms of lipid metabolism, which may inform breeding programs aiming to optimize AF in chickens.

Keywords: Liver, Chicken, Abdominal fat percentage, Serum triglyceride content, Weighted gene co-expression network analysis

Introduction

As a dynamic organ, adipose tissue undergoes stage-dependent remodeling during growth and development (Berry et al., 2013; Choe et al., 2016). Consequently, variations in the timing and extent of fat accumulation across these stages reflect coordinated metabolic regulation rather than a static phenotypic trait (Xing et al., 2021). Abdominal fat (AF), as the main fat depot in chickens, is essential for energy storage, but excessive deposition negatively impacts feed efficiency and carcass quality, leading to substantial economic losses in poultry production (Zhou et al., 2022). Therefore, it is imperative to elucidate the genetic basis underlying the dynamic changes in AF deposition to implement efficacious genetic improvement strategies.

Liver lipid metabolism is crucial for chicken fat deposition (Zhang et al., 2025). Since adipose tissue has a limited capacity for fatty acid synthesis, approximately 90% of lipid biosynthesis in chickens occurs in the liver, where lipids are primarily synthesized as triacylglycerols (TG) (Nematbakhsh et al., 2021; Sandhofer, 1994). These hepatic-synthesized TG are then packaged into very low-density lipoproteins (VLDL), secreted into the bloodstream, and ultimately processed by peripheral adipose tissue for deposition in adipocyte central vacuoles (Claire D'Andre et al., 2013). This physiological characteristic highlights the close relationship between hepatic TG metabolism and AF deposition. Accordingly, serum TG content serves as an important indicator of the efficiency of lipid delivery from the liver to AF, making it a key trait for exploring hepatic contributions to AF deposition.

Over the past decade, numerous studies have investigated the regulation of lipid metabolism in the liver. For example, transcriptomic analysis identified MFGE8, HHLA1, CKAP2, and ACSBG2 as hub genes in the liver of Jingxing-Huang chickens, showing significant correlations with AF weight (Xing et al., 2021). Based on integrative multi-omics analyses, FOXF1, ACSS2, USP10, and SEC16B were found to be related to AF deposition in 42-day-old Arbor Acres broilers (Ma et al., 2025). Moreover, CLOCK and NEK2 have been identified as playing a crucial role in maintaining the lipid metabolism homeostasis of the liver in chicken embryos (Wang et al., 2024). Despite many efforts, the hepatic regulatory networks underlying serum TG metabolism across developmental stages remain unclear and need further elucidation.

This study integrated hepatic transcriptome profiles and serum TG phenotypic data, applying WGCNA combined with Mfuzz time-series clustering to explore the central regulators regulating serum TG during the growth and development of Wannan chickens. These findings provide new insights into the hepatic regulatory mechanisms of lipid metabolism and may inform genetic improvement strategies for AF deposition in Wannan chickens.

Materials and methods

Ethics statement

All the experimental procedures were conducted by the Laboratory Animal Guidelines for the Ethical Review of Animal Welfare and were approved by the Animal Care and Use Committee of Anhui Agricultural University (SYXK(WAN) 2021–009).

Animals and sample collection

The Wannan chickens used in this study were sourced from the Muzi Agricultural Development Co. Ltd., Anhui, China. All birds were raised in the same environment and had free access to feed and water. The temperature was maintained at 33 to 35°C in the first week and decreased by 2-3°C per week until it reached 26°C. The birds were fed age-appropriate diets with the following nutrient levels: 0–3 W, CP 17.5% and ME 11.59 MJ/kg; 4–52 W, CP 15.5% and ME 10.98 MJ/kg. At 9 W, 20 W, and 52 W, 8 healthy female Wannan chickens were randomly selected at each time point. After a 12-hour fast, blood samples were collected from the wing vein. All birds were then euthanized by electrical stunning followed by exsanguination. The liver tissues were immediately placed in cryotubes, snap-frozen in liquid nitrogen, and stored at -80°C until RNA extraction.

Assays of serum biochemical indicator

Five milliliters of non-anticoagulated blood were drawn from the wing vein and centrifuged at 2500 g for ten minutes after clotting. Serum biochemical parameters were quantified using an automated biochemical analyzer (TBA-120FR, Toshiba Corporation, Tokyo, Japan). TG concentrations in the serum were measured using analysis kits (Beijing XinChuangYuan Biotechnology Co., Ltd., Beijing, China) according to the manufacturer’s instructions.

RNA extraction, cDNA library construction, and sequencing

Total RNA was extracted from 24 liver samples using the Eastep™ Super Total RNA Extraction Kit (LS1040, Shanghai Promega, Shanghai, China) according to the manufacturer’s outlined protocol. RNA concentration and purity were assessed using the NanoDrop 2000 (Thermo Fisher Scientific, USA). The integrity of the RNA was evaluated using the RNA Nano 6000 assay kit on the Agilent Bioanalyzer 2100 system (Agilent Technologies, USA). Transcriptome libraries were constructed using the MGIEasy Fast RNA Library Prep Set Kit (MGI Tech, Shenzhen, China) following the instructions. The libraries were sequenced using the DNBSEQ-T7 sequencing platform and generated 150 bp paired-end reads. The transcriptome sequencing data are available through the Genome Sequence Archive (GSA: https://bigd.big.ac.cn/gsa/) under the accession number CRA038706.

Differentially expressed gene and functional enrichment analysis

Raw reads were filtered using trim_galore with the parameters -q 20 –phred33 –stringency 3 –paired to produce clean reads, which were then aligned to the reference genome (GRCg7b) using HISAT2 (Kim et al., 2019). The BAM files were sorted and indexed using Samtools (Li et al., 2009). Gene expression levels were quantified using the HTSeq-count script in Python (Anders et al., 2015). Differentially expressed genes (DEGs) were identified using the DESeq2 (v4.2.2) (Love et al., 2014) in R, with the criteria of |log2(FC)| > 1.0 and adjusted p-value < 0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted using the online tool g:Profiler (Reimand et al., 2016). Statistical significance was set at an adjusted p-value < 0.05.

Weighted gene Co-expression network and temporal expression clustering analysis

A total of 24 transcriptomic sequencing datasets were employed for WGCNA. We applied the varianceStabilizingTransformation function from DESeq2 (v4.2.2) (Love et al., 2014) for gene expression normalization. An unsigned weighted gene co-expression network was constructed using the WGCNA package (v1.71) in R (Langfelder and Horvath, 2008). The pickSoftThreshold function was used to determine the optimal soft threshold. The hierarchical cluster tree was cut into modules using the dynamic tree cut algorithm with minModuleSize = 30 and mergeCutHeight = 0.25. Pearson correlation analysis was performed between phenotype (TG and AFP) and module eigengenes (ME) to identify modules significantly associated with the phenotypes. Hub genes were screened based on gene significance (GS) > 0.5 and module membership (MM) > 0.8. Temporal expression clustering analysis of all DEGs in the liver was performed using the fuzzy C-means clustering algorithm in the Mfuzz package (v2.60.0) (Kumar and M, 2007). A protein-protein interaction (PPI) analysis of the hub genes was performed using the STRING database (Szklarczyk et al., 2023) with a minimum interaction confidence score of 0.4. Network visualization was conducted in Cytoscape (v3.10.2) (Shannon et al., 2003).

Real‑time fluorescence quantitative PCR analysis

Fifteen liver tissue samples selected from the 24 samples used for RNA-seq analysis (n = 5 per age group: 9 W, 20 W, and 52 W) were used for real-time quantitative PCR (qRT-PCR) validation. The primers for the 6 central regulators are listed in Table S1. qRT-PCR was carried out on an ABI 7500 Real-Time PCR System (Thermo Fisher Scientific, Waltham, USA), with GAPDH used as the internal control. Each sample was analyzed in triplicate, and relative mRNA expression levels were calculated using the 2−ΔΔCt method.

Statistical analysis

Pearson correlation coefficient analysis was conducted between TG and AFP. The threshold for significance was set at p < 0.05.

Results

Triglyceride content and abdominal fat percentage across developmental stages

The TG content and AFP of Wannan chickens exhibited stage-specific dynamic trends across three developmental stages (Fig. 1A, B). From 9 to 20 W, both TG and AFP increased sharply, whereas from 20 to 52 W, TG continued to rise while AFP showed a slight decline. Pearson correlation analysis using all 24 samples across the three developmental stages revealed a strong positive association between serum TG content and AFP (r = 0.777, p = 8.05 × 10⁻⁶), indicating that TG metabolism is closely associated with AF deposition during development.

Fig. 1.

Fig. 1

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Dynamic trends of triglyceride content (A) and abdominal fat percentage (B) in Wannan chickens at 9 W, 20 W, and 52 W.

Transcriptome profiles

To identify genes involved in hepatic regulation of TG content across different developmental stages in Wannan chickens, we performed transcriptome sequencing on 24 liver tissue samples. After stringent filtering, clean reads ranging from 19725299 to 26605766 were generated per sample. The sequencing data were of high quality, with Q20 and Q30 scores above 99.35% and 97.72%, respectively, and a GC content ranging from 45.66% to 47.86%. Clean reads from each sample were mapped to the chicken reference genome with mapping rates ranging from 96.22% to 97.48% (Table S2).

Identification of DEGs in liver tissue

To investigate changes in gene expression in the livers of Wannan chickens at 9 W, 20 W, and 52 W, differential expression gene analysis was performed. A total of 1048 DEGs (774 up-regulated and 274 down-regulated) were identified in the 9 W vs. 20 W comparison (Fig. 2A, D; Table S3), 1407 DEGs (940 up-regulated and 467 down-regulated) were identified in the 9 W vs. 52 W comparison (Fig. 2B, E; Table S4), and 234 DEGs (169 up-regulated and 65 down-regulated) were identified in the 20 W vs. 52 W comparison (Fig. 2C, F; Table S5). Functional enrichment analysis revealed that DEGs from the 9 W vs. 20 W and 9 W vs. 52 W comparisons were predominantly associated with lipid-related processes, including lipid metabolic process (GO:0006629) and lipid biosynthetic process (GO:0008610) (Fig. 2G–H;Table S6). However, DEGs from the 20 W vs. 52 W comparison were significantly enriched in eight cellular components, including extracellular space (GO:0005615), plasma lipoprotein particle (GO:0034358), and protein-lipid complex (GO:0032994) (Fig. 2I; Table S6). These findings indicate a shift in hepatic function from lipid synthesis to lipid transport during late development.

Fig. 2.

Fig. 2

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Differential expression gene analysis of the liver in Wannan chickens at 9 W, 20 W, and 52 W. (A) Volcano plot of differentially expressed genes (DEGs) in 9 W vs. 20 W. (B) Volcano plot of DEGs in 9 W vs. 52 W. (C) Volcano plot of DEGs in 20 W vs. 52 W. (D) Heat map of DEGs in 9 W vs. 20 W. (E) Heat map of DEGs in 9 W vs. 52 W. (F) Heat map of DEGs in 20 W vs. 52 W. (G) GO enrichment analysis of DEGs in 9 W vs. 20 W. (H) GO enrichment analysis of DEGs in 9 W vs. 52 W. (I) GO enrichment analysis of DEGs in 20 W vs. 52 W.

Weighted gene Co-expression network construction and module detection

We performed WGCNA to identify hepatic gene modules associated with TG content and AFP using the expression profiles of 21,889 genes. A soft-thresholding power of 3 was selected to ensure a scale-free network topology (R² > 0.90), and the genes were subsequently clustered into 58 co-expression modules (Fig. 3A, B; Table S7). Among these modules, the darkslateblue and plum2 modules were significantly and positively correlated with TG (r = 0.54, p = 0.006; r = 0.84, p = 4 × 10⁻⁷) and AFP (r = 0.56, p = 0.005; r = 0.76, p = 1 × 10⁻⁵) (Fig. 3C). Subsequent analyses focused on modules that were significantly associated with TG and AFP and had absolute correlation values greater than 0.7. These results suggest that the plum2 module likely contains hub genes that mediate the dynamic regulation of TG content and subsequent AF deposition.

Fig. 3.

Fig. 3

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Weighted gene co-expression network analysis (WGCNA) of genes related to triglyceride (TG) content and abdominal fat percentage (AFP). (A) Selection of a suitable soft threshold (power = 3) and scale-free topology fit index (R2 = 0.9). (B) Gene hierarchy tree clustering diagram. (C) Module−trait relationships. Each cell contains two values. The unparenthesized value is the correlation coefficient, and the parenthesized value indicates statistical significance. Positive and negative correlations are represented by red and green colors, respectively.

Identification of hub genes in Co-expression modules

To identify hub genes potentially regulating TG content and contributing to AF deposition, we focused on the plum2 module. A total of 456 hub genes were identified using MM > 0.8 and GS > 0.5, with identical genes for TG and AFP (Table S8). Functional enrichment analysis revealed significant enrichment of these genes in 98 GO terms, including lipid metabolic process, lipid biosynthetic process, fatty acid metabolic process, and lipid storage (GO:0019915) (Table S9). Seven KEGG pathways were significantly enriched, such as steroid biosynthesis (gga00100), fatty acid metabolism (gga01212), and PPAR signaling pathway (gga03320) (Table S9).

Gene expression trend analysis in liver tissues

To explore dynamic hepatic transcriptomic changes regulating TG content and contributing to AF deposition, 1792 non-redundant DEGs identified across the three pairwise comparisons (9 W vs. 20 W, 9 W vs. 52 W, and 20 W vs. 52 W) were classified into 12 expression clusters using Mfuzz analysis (Figs. 4A and S1; Table S10). Gene expression levels in clusters 1, 4, and 7 were lowest at 9 W and increased rapidly from 9 to 20 W, which was consistent with the sharp rise of TG and AFP during this stage. However, gene expression levels peaked at 52 W in clusters 1 and 4, while cluster 7 showed a decreasing trend at this stage, which corresponded to the divergent trends of TG (continuous rise) and AFP (slight decline) from 20 W to 52 W. The 33 hub genes distributed in clusters 1, 4, and 7 were selected as central regulators for subsequent analyses. GO and KEGG enrichment analyses revealed significant enrichment of these genes in 22 categories and 6 pathways (Fig. 4B; Table S11). A protein–protein interaction (PPI) network for the 33 hub genes in the plum2 module was constructed using the STRING database. After removing isolated nodes, the network retained six genes, namely GPAM (glycerol-3-phosphate acyltransferase, mitochondrial), AGPAT2 (1-acyl-sn-glycerol-3-phosphate acyltransferase 2), MBOAT2 (membrane-bound O-acyltransferase domain containing 2), DHCR7 (7-dehydrocholesterol reductase), SQLE (squalene epoxidase), and SCD (stearyl-CoA desaturase) (Fig. 4C). These genes were enriched in glycerolipid metabolism (gga00561), steroid biosynthesis (gga00100), and fatty acid metabolism (gga01212), suggesting their potential roles in TG metabolism and abdominal fat deposition during development. To verify the reliability of the transcriptome data, we selected GPAM, AGPAT2, MBOAT2, DHCR7, SQLE, and SCD for qRT-PCR validation. The qRT-PCR results showed that the expression trends of the genes were consistent with the RNA-seq results (Fig. S2).

Fig. 4.

Fig. 4

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Screening of central regulators associated with triglyceride (TG) content and abdominal fat percentage (AFP) in Wannan chickens. (A) Mfuzz analysis of DEGs. (B) Functional enrichment analysis of central regulators. (C) PPI network of the central regulators. The thickness of the line connecting two nodes reflects the strength of the protein interaction.

Discussion

The liver is the primary site of lipid synthesis and metabolism in chickens. Its regulation involves multiple complex physiological processes (Tan et al., 2025). Therefore, identifying the dynamic regulatory network of hepatic genes involved in TG metabolism and associated with AF deposition is essential for advancing our understanding of lipid metabolic regulation in chickens. In this study, both TG and AFP increased rapidly between weeks 9 and 20, reflecting active lipid accumulation during the rapid growth phase. From 20 W to 52 W, TG continued to rise while AFP slightly decreased, suggesting that fat deposition stabilizes as chickens reach maturity. Differential expression gene analysis revealed that DEGs (9 W vs. 20 W and 9 W vs. 52 W) were primarily associated with lipid metabolism-related biological processes. In comparison, DEGs (20 W vs. 52 W) were significantly enriched in cellular components involved in lipid transport, secretion, and distribution.

In this study, we employed WGCNA to identify gene modules primarily associated with serum TG content and assess their linkage to AF accumulation. This allowed us to excavate core regulatory nodes that mediate lipid metabolism. The plum2 module exhibited significant positive correlations with both TG and AFP. This indicates that genes within this module may contribute to AF deposition regulation by acting as core regulators of hepatic TG metabolism. A total of 456 hub genes were identified within the plum2 module. GO enrichment analysis further revealed that several of these genes, including FASN (fatty acid synthase), CDS2 (CDP-diacylglycerol synthase 2), FA2H (fatty acid 2-hydroxylase), GNPAT (glyceronephosphate O-acyltransferase), APOB (apolipoprotein B), LPL (lipoprotein lipase), and C3 (complement component 3), are significantly associated with lipid metabolic processes, lipid biosynthesis, fatty acid metabolism, triglyceride metabolism, and lipid storage. FASN belongs to the fatty acid synthase gene family and acts as a crucial lipogenic enzyme that catalyzes palmitate synthesis during de novo fatty acid synthesis (Pitel et al., 1998). The CDS2 gene belongs to the CDP-diacylglycerol synthase gene family. It encodes a critical enzyme that catalyzes the conversion of diacylglycerol to CDP-diacylglycerol (Arnoldus et al., 2025). CDP-diacylglycerol synthases have been reported to regulate lipid droplet growth and adipocyte development (Chan et al., 2025). FA2H, a member of the fatty acid hydroxylase gene family, catalyzes the 2-hydroxylation of fatty acids, contributing to sphingolipid synthesis (Li et al., 2024a). FATH-1 (fatty acid 2-hydroxylase 1) is the only C. elegans homolog of the mammalian enzyme FA2H (Li et al., 2018b). In intestinal cells, inactivated FATH-1 impairs lipid droplet formation (Li et al., 2018b). GNPAT belongs to the acyltransferase gene family and encodes dihydroxyacetone phosphate acyltransferase (Ofman et al., 2001). This enzyme catalyzes the initial, rate-limiting step in glycerolipid synthesis and participates in glycerophospholipid metabolism (Zhu et al., 2019). Researchers have found that GNPAT is associated with adipogenesis in rainbow trout (Blay et al., 2021). Additionally, GNPAT has been associated with fat deposition within a QTL region on chicken chromosome 3 (Moreira et al., 2015). APOB belongs to the apolipoprotein gene family and is a major protein component of very low-density and low-density lipoproteins in plasma (Li et al., 2025). It plays a key role in exporting cholesterol and triglycerides from the liver (Hu et al., 2012). APOB gene expression has been reported to be related to fat traits in local chicken breeds in Yunnan (Li et al., 2018a). LPL, a member of the lipase gene family, catalyzes the hydrolysis of plasma lipoproteins—a rate-limiting step for lipid uptake into peripheral tissues—and plays a key role in liver fatty acid metabolism (Gu et al., 2022; Sato et al., 1999). C3 is primarily produced by the liver but also in adipocytes (Copenhaver et al., 2019). Both C3 and its proteolytic product, acylation-stimulating protein (ASP), have been associated with fat storage and obesity (Araujo et al., 2022). In mice, C3 deficiency results in adipocyte hypertrophy and increased fat accumulation. Conversely, ASP elevation results in increased adipocyte LPL (Araujo et al., 2022; Cianflone et al., 2003).

To characterize the temporal expression patterns of DEGs across physiological stages, Mfuzz time-series clustering analysis was performed. A total of 1792 DEGs were classified into 12 distinct expression pattern clusters. We found that the gene expression in clusters 1 and 4 followed a similar trend to the changes in TG content at 9 W, 20 W, and 52 W. In contrast, cluster 7 showed a trend similar to changes in AFP. Among these three clusters, 33 hub genes from the plum2 module were identified as central regulators. KEGG enrichment analysis revealed that these central regulators were involved in glycerolipid metabolism, steroid biosynthesis, and fatty acid metabolism. Among them, GPAM, AGPAT2, and MBOAT2 were significantly enriched in the glycerolipid metabolism. Glycerolipids are essential for energy storage and fat deposition, and their dysregulation is associated with obesity and insulin resistance (Su et al., 2024). GPAM, which belongs to the glycerol-3-phosphate acyltransferase gene family, promotes fatty acid synthesis and accumulation (Hong et al., 2024). GPAM catalyzes the initial, rate-limiting step in glycerolipid biosynthesis, helping channel acyl-CoA into TG synthesis (Yu et al., 2021). Studies have shown that GPAM knockdown significantly reduces the expression of genes associated with triglyceride synthesis and lipid metabolism in bovine embryonic fibroblast cells (Yu et al., 2017). AGPAT2 belongs to the acylglycerophosphate acyltransferases family and plays a crucial role in TG synthesis by regulating the conversion of lysophosphatidic acid to phosphatidic acid (Bradley and Duncan, 2018). Researchers have found that knocking out the AGPAT2 gene in mice impairs lipid droplet biogenesis and growth (Mak et al., 2021). MBOAT2 regulates TG anabolism (Lee et al., 2019), and its expression is related to fat metabolism and changes in fatty acids (Tong et al., 2024). DHCR7 and SQLE were significantly enriched in steroid biosynthesis. Researchers have found that steroid biosynthesis plays a crucial role in AF accumulation in chickens (Zhu et al., 2024). DHCR7 catalyzes the final step in cholesterol synthesis by converting 7-dehydrocholesterol to cholesterol (Mei et al., 2024; Miyazaki et al., 2024). It plays a significant role in regulating the generation, differentiation, and metabolism of fat in goat preadipocytes (Li et al., 2024b). SQLE acts as a rate-limiting enzyme in the cholesterol biosynthetic pathway (Liu et al., 2021), and its upregulation enhances the formation of cholesteryl esters (Liu et al., 2018). Studies have shown that SQLE plays a vital role in duck lipid metabolism and deposition (Zhang et al., 2023). SCD is significantly enriched in fatty acid metabolism and functions as a lipogenic enzyme that introduces the first double bond into saturated fatty acyl chains (Wolosiewicz et al., 2024). Previous studies have shown that SCD is markedly upregulated in the high-fat group of Wannan chickens (Wei et al., 2024). The present study further demonstrates that SCD exhibits stage-dependent temporal expression, indicating its critical role in dynamically regulating hepatic lipid metabolism during chicken development.

In summary, this study reveals a strong positive correlation between serum TG content and AFP in Wannan chickens during development and identifies the plum2 module as the key hepatic co-expression module associated with both traits via WGCNA. Stage-specific expression patterns of 33 central regulators (e.g., GPAM, AGPAT2, MBOAT2, DHCR7, SQLE, and SCD) were characterized by Mfuzz clustering, which may mediate the dynamic regulation of hepatic TG metabolism and AF deposition. Despite these findings, several limitations should be acknowledged. The central regulators identified in this study were selected based on WGCNA, PPI network, and Mfuzz analyses. Their biological functions in hepatic TG metabolism and AF deposition have yet to be validated experimentally. Future studies should combine protein-level analyses and cellular functional experiments to validate the biological functions of these central regulators in hepatic TG metabolism and AF deposition.

CRediT authorship contribution statement

Wei Wei: Writing – review & editing, Writing – original draft, Visualization, Data curation, Conceptualization. Najun Huang: Writing – review & editing, Writing – original draft, Supervision. Chuchu Zhang: Software, Data curation. Jingran Jiao: Validation, Methodology. Zihan Chen: Supervision, Investigation. Hao Wang: Validation, Data curation. Chaomu Li: Methodology, Formal analysis. Xing Guo: Methodology, Conceptualization. Runshen Jiang: Writing – review & editing, Supervision, Project administration.

Disclosures

The authors confirm that there are no conflicts of interest.

Acknowledgments

This work was supported by the China Agriculture Research System of MOF and MARA (CARS-41).

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.psj.2026.107508.

Appendix. Supplementary materials

mmc1.docx (1.3MB, docx)
mmc2.xlsx (226.8KB, xlsx)

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