Photocycle driven lipid metabolism in Muscovy duck liver: A process mediated by the PPAR pathway

Abstract

The photoperiod is a key environmental factor regulating energy metabolism and growth in poultry, but its impact on the liver of Muscovy ducks is not yet clear. The aim of this study is to elucidate the effects of different photoperiods on the metabolism of Muscovy duck liver, and to reveal its molecular mechanisms from the perspectives of hormone regulation and RNA-sequencing (RNA-seq). The experiment set up a start egg laying group (S group, 6L: 18D) and the peak period of egg production group (P group, 14L: 10D), and collected serum and liver tissue samples for analysis. Through enzyme-linked immunosorbent assay, it was found that compared with the S group, long light treatment significantly increased the concentration of insulin-like growth factor-1 (IGF-1) in the liver and triggered unique lipid distribution characteristics: serum triglyceride (TG) levels increased while liver TG content significantly decreased. RNA-seq analysis showed significant enrichment of the peroxisome proliferator activated receptor (PPAR) signaling pathway. Further mechanistic studies have shown that prolonged exposure to light can selectively activate the PPAR β pathway and inhibit the PPAR γ pathway. Research has indicated that that prolonged exposure to light promotes the expression of IGF-1 in the liver by activating the hypothalamic pituitary growth axis, which in turn regulates liver metabolism from lipid storage to lipid turnover and output through the PPAR signaling pathway. This may be a key molecular basis for mediating lipid redistribution between serum and liver. This discovery not only deepens our understanding of the mechanism by which light regulates energy metabolism in poultry, but also provides an important theoretical basis for improving the production performance and meat quality of Muscovy ducks through precise light management.

Introduction

Light is a fundamental environmental factor that regulates critical biological processes in animals, including circadian rhythms, reproduction, and metabolism, via complex neuroendocrine pathways (Hounkpêvi JA, 2024; Meng JJ, 2023). In poultry production, photoperiod management (including duration, intensity, and spectrum) has become a key tool for improving productivity and welfare (Banerjee S, 2018; Boswell T, 2025; Rui H, 2025).

The liver acts as the central metabolic organ in birds, governing energy homeostasis, lipid synthesis and distribution, and protein metabolism (JG., 2016). Its performance directly influences growth efficiency, energy balance, and meat quality. Photoperiod impacts hepatic function primarily by modulating pineal melatonin secretion through retinal and hypothalamic pathways which in turn regulates metabolic hormones such as corticosterone, thyroid hormones, and growth hormone. These hormonal signals alter hepatic gene expression and enzyme activity involved in glucose and lipid metabolism(MOHAWK J A, 2012). However, the molecular mechanisms by which specific light regimes (especially photoperiod duration) affect lipid metabolism and related pathways in bird liver are still poorly understood.

As an important economic waterfowl, Muscovy duck has unique lipid metabolism characteristics and excellent meat quality, making it an important source of protein supply. In recent years, RNA-seq technology has been applied to analyze gene expression changes in Muscovy ducks under growth, nutrition, and other conditions (Qi J, 2022), but the transcriptional regulatory mechanisms of light cycle on their liver metabolism are still limited. Given the operability of light management in intensive farming, analyzing its molecular mechanism of regulating lipid metabolism in duck liver has both theoretical value and application potential.

Therefore, this study used Muscovy ducks as a model and treated them with short light (6L: 18D) and long light (14L: 10D), combined with serum biochemistry, liver lipid measurement, and RNA seq analysis, aiming to clarify the effects of different light cycles on liver lipid metabolism in Muscovy ducks; Identify the key signaling pathways and transcriptional networks involved in light regulation of liver metabolism. This study aims to reveal the mechanism of light regulation on lipid metabolism in Muscovy ducks at the molecular level, providing a theoretical basis for improving breeding efficiency through precise environmental management.

Materials and methods

Animal experiment design and sample collection

In this study, a light regulation strategy was implemented for Muscovy ducks: natural light conditions were used in the early stages of feeding, followed by artificial intervention with a light intensity of 40 lux. The specific lighting plan is to provide 20 h of artificial lighting per day for the age group of 14-17 weeks; At 18-25 weeks of age, shorten to 6 hours per day; At 26 weeks of age, adjust to 9 hours of daily light exposure; Extended to 11 h per day at 27 weeks of age; Maintain 14 h of artificial lighting daily from 28 weeks of age. The experiment set up two treatment conditions: the start egg laying group (S group) with a light duration of 6 h and the peak period of egg production group (P group) with a light duration of 14 h. Five Muscovy ducks were randomly selected from each group, and their liver tissue and serum samples were collected for detection. The sample was collected immediately after the Muscovy duck was euthanized by neck amputation. First, a whole blood sample was collected and allowed to solidify at room temperature (about 25°C). The serum was separated by centrifugation at 4°C and 3000 rpm for 15 min, and then packaged and stored at −80°C; Subsequently, the liver was completely removed, washed with pre cooled physiological saline, and cut into tissue blocks of approximately 100 mg. After rapid freezing with liquid nitrogen, it was transferred to a −80°C freezer for storage. The entire sample collection process was operated under ice bath conditions, and the entire time from euthanasia to sample freezing was controlled within 5 min to ensure the integrity of the samples and the reliability of experimental data.

Hormone testing

The operation steps for liver tissue extract are as follows: Add a small amount of liquid nitrogen to the pre-cooled mortar, quickly remove about 50 mg of liver tissue block from the -80°C refrigerator, and add it to the liquid nitrogen. Grind the tissue into a uniform fine powder and keep it in a frozen state throughout the process. Transfer all the ground tissue powder to a pre cooled and tare weighed organic solvent resistant centrifuge tube, quickly weigh and record the precise tissue weight. Prepare the extraction solution in a ratio of chloroform: methanol=2:1 (v/v). For approximately 50 mg of tissue, add 1.0 mL of pre cooled extract. Immediately vortex vigorously for 1 min to completely disperse the tissue powder in the solvent. Subsequently, place the centrifuge tube on ice or a 4°C shaker and shake for 15-20 min to allow the solvent to fully penetrate and extract the lipophilic components. Add 0.2 mL of ultrapure water (or 0.88% NaCl solution) to the above mixture to induce phase separation. Tighten the tube cap and vigorously vortex for 30-60 s to form an emulsion. Centrifuge the centrifuge tube at room temperature at 800-1000 rpm g for 10 min. After centrifugation, the solution is divided into three layers: the upper layer is the water methanol phase, the middle layer is the protein layer, and the lower layer is the chloroform phase containing the target substance. Slowly insert into the bottom of the tube, carefully extract all the chloroform phase from the lower layer, and transfer it to a new tube. Be careful to avoid inhaling the middle protein layer. Blow dry the chloroform phase completely. Subsequently, the dried material was resuspended and thoroughly homogenized using the sample buffer specified in the target hormone ELISA detection kit, allowing the lipid soluble hormone to dissolve in the aqueous system for subsequent detection. After serum and liver extracts were restored to room temperature (about 25°C), TG and IGF-1 levels were detected by enzyme-linked immunosorbent assay (ELISA). To prepare all reagents correctly, they were subjected to one hour of room temperature (about 25°C) exercise according to the manufacturer's instructions.

To construct the standard curve, 50 µL of the thoroughly homogenized and accurately diluted standard stock solution was aliquoted into the first well of a series. A serial dilution was then performed across six consecutive wells to establish a concentration gradient. Next, each serum sample got its 50 µL serving, and then a whole 100 µL of that enzyme-linked horseradish peroxidase (HRP) detective antibody was poured in, except for the blank wells, of course. The dish was then zipped up tight and sent to the incubator for a cozy 60 min nap at 37°C. Post-snooze, the dish was washed five times over with a 1:20 weakened wash buffer. Each rinse was a dance of adding 350 µL of buffer, standing for 20 beats, and then pouring it all out. The last bit of water was wiped away with a tissue to ensure the dish was dry to a semitransparent film shape. Once clean, each well got a 100 µL shot of a mixed-up substrate solution, A and B rolled into one. The dish was then tucked away in the dark for a 15-minute chill at 37°C. The party ended with a splash of 50 µL of stop solution to put a lid on it.

The final tally was done at 450 nm with a microplate detector for testing. The TG and IGF-1 concentration in each sample was calculated by matching the absorbance numbers with a four-parameter logistic (4PL) curve, which was generated by ELISA data analysis software.

RNA-seq experimental program

This research utilized RNA-seq to map out the transcriptomic landscape of Muscovy duck liver tissues. Here's the step-by-step experimental protocol: First, we extracted total RNA from the liver samples we'd collected. Next, we zeroed in on eukaryotic mRNA by leveraging Oligo (dT) magnetic beads to fish out those polyA-tailed transcripts. The isolated mRNA was then put through the wringer with ultrasonication to break it into pieces. Using random primers, we whipped up first-strand cDNA from the fragmented mRNA, then followed up with second-strand cDNA synthesis using DNA polymerase I after clearing the deck with RNase H to remove the template. The resulting double-stranded cDNA was cleaned up, polished with end-repair, got a 3′ end adenylation treatment, and was then connected to sequencing adapters. We fished out cDNA fragments in the 200-bp ballpark using AMPure XP beads and gave them a boost with PCR amplification. After one final purification, we had our sequencing libraries ready to roll, which were then put through their paces on an Illumina NovaSeq 6000 high-throughput sequencer. The RNA-seq of all samples was commissioned to Genedenovo Biotechnology Co., Ltd. (Guangzhou, China) and subsequent bioinformatics analysis was based on the raw data and analysis reports provided by the company.

After quality control and processing of the raw sequencing data (raw reads), use featureCounts to calculate the reads count aligned to each gene. Standardize gene expression levels using FPKM (Fragments Per Kilobase of transcript per Million mapped fragments) values. Differential expression analysis was performed on the gene counting matrix using the DESeq2 R package (v1.34.0) to identify differentially expressed genes between the S group and the P group. The screening criteria for differentially expressed genes were set as follows: | log2 Foldchange | > 1 and a P-value (FDR, false discovery rate) of < 0.05 after Benjamin Hochberg correction. Perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the differentially expressed gene sets obtained from the above screening to elucidate their potential biological functions and pathways. The enrichment significance of GO functional items and KEGG pathway was tested using hypergeometric distribution. The statistical threshold for significant enrichment is defined as a P-value (FDR) < 0.05 after Benjamin Hochberg correction.

Gene expression detection of Muscovy duck liver tissue

Total RNA extraction

Approximately 30 mg of liver tissue, flash-frozen and stored in liquid nitrogen, was rapidly ground into a fine powder using a sterile mortar pre-chilled with liquid nitrogen. The tissue powder was transferred to a pre-cooled centrifuge tube containing 1 mL of TRIzol reagent, immediately vortexed thoroughly, and incubated at room temperature (approximately 25°C) for 5 min to ensure complete lysis. Subsequently, 0.2 mL of chloroform was added to the lysate, vigorously shaken for 15 s, and allowed to stand at room temperature for 3 min. The mixture was then centrifuged at 12,000 rpm for 15 min at 4°C. Following centrifugation, the solution separated into three distinct layers: a colorless upper aqueous phase (containing RNA), an intermediate layer, and a pink lower organic phase. Approximately 500 μL of the supernatant (aqueous phase) was carefully transferred to a new RNase-free centrifuge tube. An equal volume of isopropanol was added, and the tube was slowly invert and mix well, followed by incubation at room temperature for 10 min to precipitate the RNA. The sample was then centrifuged at 12,000 rpm for 10 min at 4°C, resulting in a visible white RNA pellet at the bottom of the tube. The supernatant was discarded, and the pellet was washed with 1 mL of 75 % ethanol prepared with DEPC-treated water by gentle vortexing. After centrifugation at 7,500 rpm for 5 min at 4°C, the ethanol was carefully removed. The RNA pellet was air-dried at room temperature until semi-transparent (approximately 5-10 min, avoiding complete desiccation) and then dissolved in an appropriate volume of RNase-free water. RNA concentration and purity were assessed using a NanoDrop™ 2000 spectrophotometer, with acceptable quality defined by an A260/A280 ratio of 1.8–2.1 and an A260/A230 ratio greater than 2.0.

Fluorescence quantitative PCR (qPCR) quantitative analysis

Adjust the sample to a uniform concentration (500 ng/µL) according to the RNA concentration. Take 2 µL of RNA solution and add it to a 200 µL EP tube. Add 10 µL of RNase free water and 4 µL of 4 × g DNA wiper mix. After thorough mixing, incubate at 42 °C for 2 min. Then add 4 µL of 5 × Hiscript III qRT superMix, react at 50 °C for 15 min, and inactivate at 85 °C for 5 s. Dilute the synthesized cDNA 20 times and set aside for later use.

For the fancy qRT-PCR gig, we got each of our 10 µL reaction recipes together in a nippy 96-well PCR plate, combining 1 µL of the watered-down cDNA template with 9 µL of a custom concoction featuring SYBR Green master mix and a tailored-made primer suite. We popped the cap on and gave the plate a gentle whirl to get the good stuff in there, then it was showtime. We used the QuantStudio real-time PCR setup (a fancy schmancy Applied Biosystems system) to kick things off with an initial denaturation at a steamy 95 degrees Celsius for half a minute, followed by a series of 40 cycles where we kept it 95 degrees for 5 s, then down to 60 degrees for a more leisurely 30 s, and it ends in the final melting stage to ensure everything is normal.

In order to design gene specific primers for our target and reference genes, we used Primer 5.0 software, and all primers were synthesized by Tsingke Biotechnology Co., Ltd. All the sequences listed in this study are neatly arranged in Table 1.

Table 1.

PCR Primer Sequence.

Genes	Genebank ID	Primer sequence (5′ to 3′)
GAPDH	NM_204305.1	F: GCCATCACAGCCACACAGA
		R: TTTCCCCACAGCCTTAGCA
FABP1	XM_035326336.1	F: TACCGGCAAGGCATCTTCTC
		R: GTGTCTCCGTTGAGTTCGGT
FABP3	XM_035345540.1	F: TGGACACGGCCAATTTCGAT
		R: GGTCTTCACCGTCACCTTGT
MTTP	XM_035325773.1	F: CGGGCTAAAGCATCCCTGAA
		R: ACCATGGGCCTCTGTAAAGC
APOA1	XM_035346221.1	F: GGACGAACTCCAGAAGACCG R: GCGAAGCTGAGACAGACTCC
CYP8B1	XM_021275226.4	F: TACAAGCCCATCCAAGCCAG
		R: GAGGCTCGGGGTAGATTTCG
ALPL	XM_035312812.1	F: CTTTCTCCCACCCAGCCC
		R: GAACATGTATTTCCGCCCGC

Data statistics and analysis

In the present investigation, the findings were expressed as the average ± standard error (Mean ± SE) across three separate biological replicates for each experimental category. The statistical crunching was handled with SPSS 26.0 software. To identify any differences between the groups, we used a t-test. We deemed anything with a P-value under 0.05 as a meaningful deviation, while P-values over 0.05 suggested no cause for alarm. The figures in the report were crafted with the aid of GraphPad Prism.

Result

Changes in liver and abdominal fat index of Muscovy ducks

Fig. 1A shows the relevant data of sampling nodes S and P. Compared with group S, there was no significant difference in liver weight and its relative values between group P (P > 0.05); however, the abdominal fat content index of group P was significantly lower than that of group S (P < 0.05).

Fig. 1.

. Sampling time point and Muscovy duck phenotype.

Note: S, start egg laying group; P, peak period of egg production group, n = 5. (A) Sampling time point; (B) Muscovy duck weight (kg); (C) Muscovy duck liver index (g/kg); (C) Muscovy duck liver index (g/kg) (D) Muscovy duck abdominal fat weight and its index (g/kg).

Changes in TG and IGF1 levels in serum and liver of Muscovy ducks

As shown in Fig. 2, serum IGF-1 concentrations in S and P group Muscovy ducks were 395.32 μg/L and 478.45 μg/L, respectively, with no significant difference (P > 0.05). In contrast, hepatic IGF-1 content was significantly higher in the P group (656.58 μg/L) than in the S group (572.79 μg/L; P < 0.05). Concurrently, serum TG levels increased significantly in the P group (31.30 μmol/L) compared with the S group (27.55 μmol/L; P < 0.05). Conversely, hepatic TG content showed a significant decrease in the P group (26.79 μmol/L) relative to the S group (31.92 μmol/L; P < 0.05).

Fig. 2.

Hormone levels in serum and liver of Muscovy ducks.

Note: S, start egg laying group; P, peak period of egg production group, n = 5.

Results of Genome Comparison Analysis of Muscovy Duck liver

By utilizing HISAT2 for aligning the clean reads from each sample to the reference genome (GWHBJBF00000000), we were able to achieve a high mapping rate, typically surpassing 65 % under ideal conditions where the genome selection is spot-on and experimental contamination is non-existent. With a quality-assured reference genome, our RNA-Seq data aligned splendidly with the genome, boasting an over 86 % match with unique loci and an overall alignment rate exceeding 88 %. This impressive correspondence between theoretical and observed values confirms the validity of our results for further analysis (refer to Table 2).

Table 2.

Comparison of reference genomes.

Sample	Total	Unmapped (%)	Unique Mapped (%)	Multiple Mapped (%)	Total Mapped (%)
S1	38696078	4261278 (11.01 %)	33361310 (86.21 %)	1073490 (2.77 %)	34434800 (88.99 %)
S2	37575692	3226127 (8.59 %)	33186364 (88.32 %)	1163201 (3.10 %)	34349565 (91.41 %)
S3	42480840	4170336 (9.82 %)	37172898 (87.51 %)	1137606 (2.68 %)	38310504 (90.18 %)
P1	42880682	3039091 (7.09 %)	38375815 (89.49 %)	1465776 (3.42 %)	39841591 (92.91 %)
P2	39128708	3790669 (9.69 %)	33788407 (86.35 %)	1549632 (3.96 %)	35338039 (90.31 %)
P3	39790422	3600728 (9.05 %)	34766784 (87.37 %)	1422910 (3.58 %)	36189694 (90.95 %)

Note: Sample: Sample name; Total: The number of reads after ribosome filtration; Unmapped (%): The proportion of reads that have not been mapped to the reference genome and effective reads; Unique Mapped (%): The proportion of reads and effective reads in the reference genome; Multiple Mapped (%): The proportion of reads from the reference genome and effective reads in multiple comparisons; Total Mapped (%): The proportion of all reads and valid reads that can be mapped to the genome, n = 3.

Analysis of differentially expressed genes in Muscovy duck liver

In this investigation, principal component analysis (PCA) was utilized to streamline the complexity of gene expression data by condensing its dimensions. The PCA outcomes highlighted a clear link between how closely related samples were and their positioning, as samples from distinct treatment cohorts appeared well-separated in the PCA visualizations. Specifically, as depicted in Fig. 3A and 3B, the S and P groups each formed their own tight clusters, standing apart from one another. For pinpointing differentially expressed genes, the threshold was an FDR under 0.05 and a log2FC absolute value exceeding 1. The analysis uncovered 479 genes exclusive to the S group, 206 to the P group, and a substantial overlap of 8,850 genes common to both (Fig. 3C). When comparing the P group to the S group, there were 53 genes that were upregulated and 44 that were downregulated (Fig. 3D). Genes including APOA1, SERPINC1, COMT, FMO5, LCAT, AKR1B1, and AKR1D1 were more active in the S group, whereas HSPA5, HSP90B1, PPIB, VTG1, CTSE, and FAM20C were heightened in the P group (Fig. 3E and 3F). Standout genes with marked expression differences comprised HSP30C, APOA1, ALPL, and FABP3.

Fig. 3.

Basic analysis of transcriptome differences in liver tissue.

Note: S, start egg laying group; P, peak period of egg production group, n = 3. (A) Violin plots of the samples; (B) Principal component analysis plot of the samples; (C) Venn diagram of differentially expressed genes; (D) Bar chart of differentially expressed genes; (E) Heatmap of differentially expressed genes; (F) Radar chart of differentially expressed genes (top 20 with maximum fold change); (G) Volcano plot of differentially expressed genes, n = 3.Note: (A) Principal component analysis plot of the samples; (B) Sample clustering plot; (C) Statistical plot of differentially expressed genes (DEGs); (D) Differential Venn diagram, n = 3.

GO functional enrichment analysis of DEGs in Muscovy duck liver

GO database stands as a cornerstone bioinformatics resource, providing structured, controlled vocabularies (ontologies) for describing gene product functions. It is authored and curated by the Gene Ontology Consortium (Ashburner M, 2000; Gene Ontology Consortium; Aleksander SA, 2023). This system enables comprehensive functional annotation of targeted gene groups, structured around three primary classifications: Biological Process, Cellular Component, and Molecular Function. As depicted in Fig. 4, analysis of differentially expressed genes revealed significant enrichment across multiple functional domains, including the endoplasmic reticulum chaperone complex, smooth endoplasmic reticulum, extracellular region and space, endocytic vesicle lumen, along with metabolic processes involving oxoacids, organic acids, and carboxylic acids, as well as the endoplasmic reticulum lumen.

Fig. 4.

GO enrichment of differentially expressed genes.

Note: S, start egg laying group; P, peak period of egg production group, n = 3.

KEGG functional enrichment analysis of DEGs in Muscovy duck liver

We conducted KEGG pathway enrichment analysis on differentially expressed genes. KEGG is a knowledge base widely used for systematic analysis of gene function, metabolic pathways, and molecular interaction networks (Kanehisa M, 2000). Based on the annotation function of this database, researchers can systematically classify target gene sets according to the metabolic pathways or biological functions they participate in. This study conducted KEGG pathway enrichment analysis on differentially expressed genes in testicular tissue, and the results showed that compared to the S phase, DEGs were mainly concentrated in the P phase PPAR signaling pathway, Thyroid hormone synthesis, Metabolic pathways, Steroid hormone biosynthesis, Lipid and atherosclerosis, Antigen processing and presentation, Thiamine metabolism, Cholesterol metabolism, Glycerophospholipid metabolism, Protein processing in endoplasmic reticulum Among them, PPAR signaling pathway is significantly enriched (Fig. 5). Fig. 6 (Upper) depicts the significantly upregulated genes in the PPARγ signaling pathway from the transcriptomic analysis, revealing pathway activation. Fig. 6 (Lower) depicts the significantly downregulated genes in the PPARβ signaling pathway, indicating its suppression.

Fig. 5.

KEGG enrichment of differentially expressed genes.

Note: S, start egg laying group; P, peak period of egg production group, n = 3. (A) KEGG functional enrichment plot of differentially expressed genes. (B) KEGG functional enrichment GESA plot of differentially expressed genes.

Fig. 6.

Significantly differentially expressed genes in the PPAR signaling pathway.

Note: S, start egg laying group; P, peak period of egg production group, n = 3. (A) FABP1 expression level. (B) FABP3 expression level. (C) MTTP expression level. (D) APOB expression level. (E) APOA1 expression level. (F) CYP8B1 expression level. (G) ACOX2 expression level. (H) SREBF1 expression level. (I) SREBF2 expression level. (G) FABP5 expression level.

RNA seq data validation of Muscovy duck liver

This study evaluated the accuracy and consistency of RNA-seq results using quantitative real-time polymerase chain reaction (qRT-PCR), confirming that the expression levels of six different genes detected by RNA sequencing had changed. These genes were selected based on their association with the PPAR pathway and observed phenotypes. These findings emphasize the accuracy and reliability of RNA-seq data, demonstrating strong repeatability (Fig. 7).

Fig. 7.

PCR validation of differentially expressed genes.

Note: S, start egg laying group; P, peak period of egg production group, n = 3. Calculate the average relative gene expression levels obtained by PCR using the 2^-ΔΔCt method, and then calculate the log2.

Discussion

The intricate choreography of bodily circadian rhythms relies on precise coordination between internal and external environmental cues to sustain essential physiological processes like sleep, blood pressure regulation, and metabolism. Among these environmental influences, light exposure stands out as a pivotal regulator of animal development (Neptune TC, 2024; Villamizar N, 2014; Wyse CA, 2011). Chronic light exposure is a powerful environmental signal that typically inhibits the secretion of melatonin by the pineal gland, thereby enhancing its inhibitory effect on the hypothalamic pituitary growth axis (GH-IGF axis).This cascade of events kicks pituitary growth hormone (GH) pulsatile secretion into high gear while simultaneously making the liver more responsive to GH, ultimately giving IGF-1 synthesis a substantial boost (Ma IL, 2023; Rizky D, 2024). The results of this study indicate that long light treatment (P group, 14L: 10D) significantly altered the lipid metabolism pattern in the liver organs of Tibetan ducks. Specifically, although there was no significant change in the relative weight of the liver, the relative weight of abdominal fat decreased significantly. At the same time, the serum TG level significantly increased, while the liver TG content decreased synchronously. The above results indicate that long-term exposure to light does not simply work by inhibiting growth, but rather by accurately reconstructing the body's energy distribution network, guiding energy towards more productive tissues and organs. This phenomenon suggests that prolonged exposure to light may reshape the metabolic program of the liver through complex integration mechanisms. To further investigate this mechanism, this study first conducted RNA seq of liver tissue.

KEGG enrichment analysis showed that the PPAR signaling pathway has significant enrichment characteristics. As an important component of the nuclear hormone receptor superfamily, PPAR plays a core transcriptional regulatory role in regulating lipid metabolism, glucose homeostasis, and energy balance. Members of this family exhibit distinct circadian rhythm fluctuations in liver tissue and can regulate the transcriptional expression of downstream lipid metabolism related genes through various ligand activation mechanisms (S, 2014; Shirai H, 2007). This study found through continuous light intervention experiments that the PPARβ signaling pathway was significantly activated in the liver tissue of Muscovy ducks, while the PPAR γ pathway exhibited significant inhibitory characteristics. This differentiation regulation mode has been confirmed by downstream target gene expression profiles: PPARγ pathway inhibition leads to significant downregulation of key downstream genes related to lipid synthesis and storage, such as SREBF1/2 (sterol regulatory element binding transcription factor), FABP5 (fatty acid binding protein 5), and ACOX2 (acyl CoA oxidase 2) expression levels. This directly weakens the endogenous fat synthesis ability of the liver, providing a molecular explanation for the decrease in liver TG content (Vamecq J, 1999). At the same time, gene expression related to fatty acid oxidation, transport, and lipoprotein assembly is upregulated, such as FABP1, FABP3, MTTP (microsomal triglyceride transfer protein), and APOB (apolipoprotein B). Among them, FABP1/3 is involved in the transport of intracellular fatty acids to β - oxidation sites or export pathways, while MTTP and APOB are core components for the assembly and secretion of very low-density lipoprotein (VLDL). This indicates that prolonged exposure to light activates PPARβ, promotes the liver to direct fatty acids towards oxidative energy supply, and accelerates the assembly and secretion of VLDL primarily composed of TG (Aoyama T, 1998).

The high levels of IGF-1 found in this study are likely to be key upstream signals that initiate the metabolic reprogramming. Based on previous research, IGF-1 has been shown to interfere with the transcriptional activity of PPARγ and may enhance the function of PPARβ (Yakar S, 2018). The results of this study support the idea that the elevation of IGF-1 levels induced by long light exposure may reshape liver metabolism through a dual mechanism: on the one hand, it inhibits the lipid storage oriented PPARγ pathway, and on the other hand, it activates the lipid mobilization and oxidation oriented PPARβ pathway. This shifts the liver's function from a "lipid synthesis and storage center" to a "lipid turnover and export hub". Therefore, the observed increase in serum TG essentially reflects a significant acceleration in liver lipid output rate (VLDL secretion), exceeding the immediate uptake and utilization capacity of peripheral tissues for circulating lipids.

Conclusion

This study systematically investigated the effects of different photoperiods on lipid metabolism in the liver of Muscovy duck. Through a comprehensive study of serum hormone levels and liver transcriptomics, this study revealed that long light conditions (14L: 10D) can induce reprogramming of Muscovy duck lipid metabolism. Specifically, it is manifested as a significant increase in TG concentration in serum, a corresponding decrease in TG content in the liver, and a significant decrease in abdominal fat percentage. Transcriptomic analysis shows that this metabolic phenotype is closely linked to the key regulatory network of the PPAR signaling pathway in the liver, particularly the activation of the PPAR β pathway and inhibition of the PPAR γ pathway. Overall, prolonged exposure to light may promote the transfer of lipids from storage organs to the circulatory system by regulating the synthesis, breakdown, and excretion processes of lipids in the liver, thereby achieving energy redistribution. This study provides a molecular mechanism explanation for improving the meat quality of Muscovy ducks under long light conditions from the perspective of metabolic regulation and provides a theoretical basis for achieving the goal of "quality improvement and efficiency enhancement" through precise light management in aquaculture practice.

However, this study also has certain limitations. The current research conclusions are mainly based on transcriptomic expression profiling analysis. For the activity changes of key proteins in the PPAR β/γ pathway, the precise functions of downstream target genes, and the resulting changes in final metabolite flux, further verification through proteomics, metabolomics, and gene function validation experiments is still needed. In addition, the effects of lipid reprogramming observed in this study on the long-term physiological health, reproductive performance, and meat quality stability of Muscovy ducks still need to be further evaluated in experiments that extend the breeding cycle.

Declaration

Ethical review and consent for participation

The Animal Management and Ethics Committee (IACUC) of Jiangsu Academy of Agricultural Sciences has approved all animal experimental procedures. The sampling and slaughter process follows the "Ethical Management Guidelines for Laboratory Animals" (2006, No. 398) issued by the Ministry of Science and Technology of China and the "Management and Use Standards for Laboratory Animals" (2008, No. 45) formulated by the Jiangsu Provincial People's Government.

Funding

This work was funded by the Jiangsu Seed Industry Revitalization Project (JBGS [2021] 111)， Jiangsu Province Agricultural Science and Technology Independent Innovation Project (CX (24) 1012) and National Natural Science Foundation of China General Project (32573221).

CRediT authorship contribution statement

Yuyan Feng: Writing – original draft, Validation, Investigation, Formal analysis, Data curation. Jie Liu: Writing – review & editing, Supervision. Xiaojing Chen: Investigation. Haiyue Mei: Investigation. Zichun Dai: Investigation. Huifang Li: Investigation. Huanxi Zhu: Writing – review & editing, Funding acquisition.

Disclosures

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, and there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the content of this paper.

Data availability

The data that supports this study is available in the article and accompanying online supplementary material.