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. 2023 Oct 21;103(1):103223. doi: 10.1016/j.psj.2023.103223

N-carbamylglutamate supplementation induces functional egg production in layers by modulating liver transcriptome profiles

Qiulin Liu *,1, Jiabo li *,1, Di Han †, Jinfeng Wang *, Jian Zheng *, Wei Ma *,2, Chunqiang Wang *,2
PMCID: PMC10698670  PMID: 38035861

Abstract

Eggs rich in polyunsaturated fatty acids (PUFA), known as functional eggs, are animal products deemed beneficial to human health and possess high economic value. The production of functional eggs involves supplementing exogenous additives with the ability to regulate lipid metabolism. As N-Carbamylglutamate (NCG) serves as an endogenous arginine synthesizer, and arginine acts as the substrate for the formation of nitric oxide (NO), the biological function of NCG is partially mediated by NO. NO is a key regulatory molecule in lipid metabolism, suggesting that NCG may also have the ability to modulate lipid metabolism. In order to assess the capacity of NCG in regulating liver lipid metabolism and its potential application in producing functional eggs, we conducted a study to investigate the effects of dietary supplementation of NCG on production performance, serum, and liver NO levels, yolk fatty acid composition, and the liver transcriptome of layers. In this study, we utilized 30 layers of the Jinghong No.1 breed, all aged 45 wk. All the birds were randomly divided into 2 groups. Each group had 5 replicates, and each replicate had 3 birds. We provided them with different diets: one group received the basic diet, and the other group's diet was supplemented with 0.08% NCG. The experiment lasted for 14 wk. The results did not reveal any positive impact of NCG on production performance. However, NCG supplementation elevated NO levels in serum and liver, along with an increase in yolk PUFA, ω-3, and ω-6 fatty acids. Liver transcriptome analysis identified 124 upregulated differentially expressed genes (DEGs) and 43 downregulated DEGs due to NCG supplementation. Functional annotation using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database highlighted 3 upregulated DEGs (CPT1A, MOGAT1, and CHKA) and 2 downregulated DEGs (FASN and ETNPPL) associated with lipid metabolism. Pathway enrichment analysis revealed that CPT1A was enriched in the AMPK signaling pathway and the PPAR signaling pathway, while FASN was enriched in the AMPK signaling pathway. Thus, CPT1A and FASN are potential functional genes related to lipid metabolism facilitated by NCG supplementation. In summary, our study suggests that NCG supplementation modulates liver lipid metabolism, leading to the production of functional eggs in layers.

Keywords: N-Carbamylglutamate, layer, liver transcriptome analysis, functional egg, lipid metabolism

INTRODUCTION

With the growing focus on healthy living, consumers have shown a rising interest in functional foods (Willett et al., 2017). Functional foods have the potential to offer beneficial components to consumers, including essential fatty acids like polyunsaturated fatty acids (PUFA) (Dugan et al., 2015). Polyunsaturated fatty acids have been linked to a reduced risk of cardiovascular and cerebrovascular diseases (Thomas et al., 2021). Recognizing the health advantages of PUFA, consumers are increasingly willing to invest in animal products enriched with these beneficial fatty acids (Grunert et al., 2009).

Eggs are a widely consumed source of protein globally. However, their consumption has been linked to an increased risk of cardiovascular diseases (Miranda et al., 2015). To address this concern and improve the perception of eggs, animal nutritionists have taken various measures to enhance the content of PUFA in eggs (Fraeye et al., 2012; Zhao et al., 2013). These modified eggs, known as functional eggs, serve as carriers for delivering beneficial substances like PUFA to the human body (Bornkessel et al., 2014; Kraus, 2015). The development of functional eggs holds significant market potential and offers opportunities for promoting human health (Annunziata and Vecchio, 2011).

Dietary manipulation is a strategy used to change the fatty acid composition in animal products (Vahmani et al., 2017; Guo et al., 2019). Adding PUFA to the diet is a common method to increase the beneficial fatty acid content in eggs (Pappas et al., 2005). However, PUFA, when used as a feed additive, can easily oxidize and produce harmful compounds (Dey et al., 2005). Therefore, PUFA is not an ideal additive to enhance the beneficial fatty acid content of animal products, as it might have toxic effects and harm human health. In contrast, N-Carbamylglutamate (NCG) has emerged as a promising alternative. N-Carbamylglutamate activates endogenous arginine synthesis, leading to the production of nitric oxide (NO), a crucial regulator of lipid metabolism (Stuehr, 2004; Wu et al., 2010; Huang et al., 2019). Noda et al. (2015) found that NO has positive effects on lipid metabolism by activating hepatic sterol regulatory element-binding protein-2, a key factor in cholesterol metabolism, and increasing the expression of low-density lipoprotein receptors. Marra et al. (2007) observed that NO levels are closely related to fatty acid synthesis and β-oxidation. Hence, NCG supplementation is likely to impact lipid metabolism to some extent, partly mediated by affecting NO levels in the body.

In addition, recent studies by Ma et al. (2023) demonstrated that adding NCG to the diet enhanced the production performance and egg quality of layers. This improvement was attributed to the regulation of uterus function. Furthermore, Ma et al. (2020a) showed that NCG supplementation positively affected eggshell quality by altering endometrial morphology, influencing the expression of calcium metabolism-related genes, and influencing the secretion of hormones. Another study by Ma et al. (2020b) highlighted that NCG supplementation effectively stimulated ovarian follicle development by enhancing angiogenesis in chicken ovaries. Consequently, the inclusion of NCG in the diet may lead to positive outcomes in the production performance of layers.

Based on the findings mentioned above, we hypothesized that supplementing the diet with NCG could improve the fatty acid composition in eggs by regulating liver lipid metabolism and, in turn, improve the production performance of layers. In this study, we utilized Jinghong No.1, a commercial laying hybrid, as the experimental animal to explore the impact of dietary NCG supplementation on production performance, serum and liver NO levels, yolk fatty acid composition, and liver transcriptome. Jinghong No.1 Layer is a specific genetic line of laying hens known for their production performance and egg quality (Azmal et al., 2020). These anticipated results will provide valuable insights into the relationship between NCG supplementation and liver lipid metabolism, potentially paving the way for the production of functional eggs with enhanced nutritional value.

MATERIALS AND METHODS

Experimental Design

In this study, we utilized 30 layers of the Jinghong No.1 breed, all aged 45 wk. All the birds were randomly divided into 2 groups. Each group had 5 replicates, and each replicate had 3 birds. We provided them with different diets: 1 group received the basic diet, and the other group's diet was supplemented with 0.08% NCG. The experiment lasted for 14 wk. The formulation of diets was predicated on the optimization of nutritional requirements derived from the recommendations put forth by the National Research Council. This formulation has been effectively employed in commercial practices (Supplementary Table 1). The NCG used in our study was sourced from Yuanchang Industrial Co. Ltd. (Jiujiang, Jiangxi, China), with a cost of $8.20 per kilogram. The dosage of NCG was determined based on a prior study conducted by Ma et al. (2023). Ethical approval for all methods and procedures employed in the study was obtained from the Ethics Committee of Jinzhou Medical University (Jinzhou, Liaoning, China). We strictly followed the guidelines set forth by the Ministry of Agriculture of the People's Republic of China.

The layers were housed in a facility equipped with natural ventilation and programmable lighting. They were placed in adjacent steel cages that were furnished with nipple drinkers, a common trough for feed, and an egg collection plate. Throughout the study, the facility maintained an average temperature of 23°C. The lighting schedule consisted of 16 h of light and 8 h of darkness. The layers had unrestricted access to both feed and water.

Sampling and Measurements

Production Performance

We monitored the daily number and weight of eggs to compute both egg weight and the egg production rate. Daily feed intake was calculated to determine the average daily feed intake. Egg mass was calculated by multiplying egg weight with egg production. The feed conversion ratio (FCR) was calculated as the grams of feed intake per gram of egg mass produced. Additionally, we examined the number of deceased birds on a daily basis.

Serum and Liver NO Contents

On the final day of the study, blood samples were collected from the wing vein of all birds using a sterile syringe and stored at 4°C. Subsequently, all birds were euthanized by intravenously administering 1 cc of Euthasol. Liver tissue samples were then removed from the carcasses. The collected blood samples and half of the liver tissue were centrifuged at 3,000 × g for 15 min. The resulting supernatant was used to assess the levels of NO using a specific Enzyme-Linked Immunosorbent Assay kit obtained from Nanjing Jiancheng Institute of Bioengineering in Nanjing, China. The evaluation followed the methodology described by Sun et al. (2018).

Yolk Fatty Acid Composition

On the final day, we collected 1 fresh egg laid by each bird to measure the yolk fatty acid composition. Eggs were cracked, and the yolks were separated using an egg yolk separator. To separate the yolk, the egg was cracked into the separator, allowing the egg white to flow out, leaving the yolk in the separator (https://www.fresheggsdaily.blog/2022/08/7-methods-to-separate-eggs-like-pro.html?m=1).

Approximately 0.1 g of the yolk sample was mixed with 1 mL of 5 mmol/L NaOH solution. To this mixture, 0.5 mL of toluene and 2 mL of 5% KOH-MeOH were added. The sample was vortex-mixed and heated at 70°C for 8 min. After cooling in cold water, 2 mL of 14% BF3-MeOH was added, followed by heating at 70°C for another 8 min. The sample was then cooled and 3 mL of 5% NaCl solution was added and mixed thoroughly. To extract fatty acid methyl esters, 5 mL of distilled water and 0.5 mL of hexane were added to the sample. Undecanoic acid (C11:0) was used as the internal standard. The mixture was vortexed and then centrifuged at 3,000 × g for 5 min. The upper phase was collected and dried with sodium sulfate. The dried samples were analyzed for total fatty acids using an HP5890 gas chromatograph with a flame ionization detector. Separation of FAMEs was achieved using a Supelcowax-10 fused silica capillary column. The oven temperature was increased from 220 to 240°C at a rate of 2°C/min. The injector and detector temperatures were set at 240°C and 250°C, respectively. One microliter of the sample was injected into the column in split mode (50:1). The fatty acid peaks were identified and quantified by comparing the retention time and peak area of each fatty acid standard. The fatty acid composition was expressed as grams per 100 g of total fatty acids (Lei et al., 2018).

Transcriptomics Analysis of Liver Tissue

For transcriptomic analysis, 1 liver tissue sample was collected from each replicate. Approximately 0.2 g of the tissue was used for total RNA extraction, which was performed using Trizol reagent according to the manufacturer's instructions. The integrity and purity of the extracted RNA were evaluated through agarose gel electrophoresis and a NanoDrop 2000 spectrophotometer, respectively. The RNA concentration was accurately quantified using the Qubit 2.0 fluorometer, and the RNA sample integrity was assessed using the Agilent 2100 Bioanalyzer. Once the samples were confirmed to be suitable for analysis, 3 µg of RNA per sample was used for RNA sample preparations. Sequencing libraries were generated using the NEBNext Ultra RNA Library Prep Kit for Illumina, following the manufacturer's recommendations. Index codes were incorporated to attribute sequences to each sample, enabling sample identification during data analysis. The quality of the generated libraries was further assessed using the Agilent Bioanalyzer 2100 system, ensuring that the libraries met high-quality standards and were suitable for subsequent sequencing analysis (Wang and Ma, 2019).

The prepared sequencing libraries were sequenced on an Illumina HiSeq 2500 platform. To ensure data quality, in-house scripts were utilized for read quality control. Raw data in Fastq format underwent initial processing with in-house perl scripts, involving the removal of reads containing adapters, poly-N sequences, and low-quality reads, resulting in clean reads. Quality metrics such as Q20, Q30, and GC content were calculated for the clean data. All subsequent analyses were conducted using these high-quality clean reads. This study employed a paired-end 150 sequencing strategy. The reference genome sequence for the chicken (version 90) was downloaded from the genome website (ftp://ftp.ensembl.org/pub/current_fasta/gallus_gallus/dna/Gallus_gallus.Gallus_gallus-5.0.dna.toplevel.fa.gz). An index of the reference genome was built using Hisat2 v2.0.5, and the paired-end clean reads were aligned to the reference genome using Hisat2 v2.0.5. Gene expression levels were estimated using the fragments per kilobase of transcript per million fragments method, which takes into account transcript length and read counts. Differential expression analysis between the groups was performed using the DESeq2 R package (version 1.16.1) based on the read count data. Functional annotation and pathway enrichment analysis were conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/). The statistical enrichment of differentially expressed genes in KEGG pathways was assessed using the ClusterProfiler R package.

qRT-PCR Verification

To validate the accuracy of the transcriptome sequencing data obtained through RNA-seq, the expression levels of FASN and CPT1A genes were measured using quantitative real-time PCR (qRT-PCR). Total RNA was extracted from the liver tissue samples, and cDNA synthesis was performed using a total RNA reverse transcriptase kit (Takara, Dalian, China). Real-time PCR was conducted on an ABI 7500 Fast Real-Time PCR system using SYBR premix Ex TaqTM II (Takara). The optimized cycling conditions consisted of an initial denaturation step at 94°C for 5 min, followed by 45 cycles of denaturation at 94°C for 15 s and annealing/extension at 55°C for 15 s. Each sample was tested in triplicate to ensure result accuracy. The relative expression levels of the target genes were determined using the 2−ΔΔCt method (Schmittgen and Livak, 2008), with β-actin serving as the internal control for normalization. The primer sequences used for amplifying the target genes were specifically designed based on the sequences available in the GenBank database (Supplementary Table 2).

Statistical Analysis

An independent samples t test was conducted using SPSS software (Version 21.0). Before performing the t test, the normality of the data was assessed through the Shapiro–Wilk test and QQ plots. The t test results were presented as means ± standard deviation. Each replicate was considered an experimental unit. Statistical significance was defined as a probability value below 0.05.

RESULTS

Dietary supplementation of NCG had no significant effects on the production performance parameters including egg weight, egg production rate, average daily feed intake, egg mass, FCR, and mortality rate (Table 1).

Table 1.

Effects of dietary supplementation of N-carbamylglutamate on the production performance of layers.1

Items CON2 TRT3
Egg weight, g 63.26 ± 1.67 61.33 ± 1.34
Egg production rate, % 86.40 ± 5.29 92.31 ± 5.43
Average daily feed intake, g/d/hen 117.00 ± 1.00 117.30 ± 0.58
Egg mass, g/d/hen 54.72 ± 4.79 56.66 ± 4.57
FCR,4 g feed/g egg 2.15 ± 0.18 2.08 ± 0.17
Mortality rate, % 0 0
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Results were presented as mean ± standard deviation.

1

Values represent the means of 5 replicates per group (n = 5).

2

Layers were fed with a basal diet.

3

Layers were fed with a basal diet supplemented with 0.08% N-carbamylglutamate.

4

Feed conversion ratio was calculated as the average egg weight to the average daily feed intake ratio.

However, dietary intervention significantly increased the contents of NO in both serum (P < 0.05) and liver (P < 0.05) (Table 2).

Table 2.

Effects of dietary supplementation of N-carbamylglutamate on serum and liver nitric oxide contents of layers.1

Items CON2 TRT3
Serum, umol/L 33.65 ± 5.48b 55.75 ± 8.63a
Liver, umol/L 5.49 ± 0.92b 6.73 ± 0.84a
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Results were presented as mean ± standard deviation.

a,b

Different superscripts within a row indicate a significant difference (P < 0.05).

1

Values represent the means of 5 replicates per group (n = 5).

2

Layers were fed with a basal diet.

3

Layers were fed with a basal diet supplemented with 0.08% N-carbamylglutamate.

Additionally, in layers fed with the NCG-contained diet, the content of various fatty acids in the yolk was significantly higher compared to those fed with the basal diet. Specifically, caprylic acid (C8:0), γ-linolenic acid (C18:3 n6), α-linolenic acid (C18:3 n3), eicosadienoic acid (C20:2), arachidonic acid (C20:4 n6), eicosapentaenoic acid (C20:5), docosadienoic acid (C22:2), nervonic acid (C24:1), docosahexaenoic acid (C22:6), the sum of PUFA (Σ PUFA), the sum of ω-3 fatty acids (Σ ω-3), and the sum of ω-6 fatty acids (Σ ω-6) were all significantly higher (P < 0.05) (Table 3).

Table 3.

Effects of dietary supplementation of N-carbamylglutamate on the contents of fatty acids in yolk.1

Items, % CON2 TRT3
SFA
 Butyric acid, C4:0 0.010 ± 0.001 0.007 ± 0.004
 Caproic acid, C6:0 0.014 ± 0.002 0.009 ± 0.005
 Caprylic acid, C8:0 0.005 ± 0.001b 0.012 ± 0.008a
 Capric acid, C10:0 0.012 ± 0.005 0.016 ± 0.007
 Undecanoic acid, C11:0 0.004 ± 0.001 0.004 ± 0.001
 Lauric acid, C12:0 0.014 ± 0.005 0.028 ± 0.016
 Tridecanoic acid, C13:0 0.007 ± 0.001 0.011 ± 0.012
 Myristic acid, C14:0 0.412 ± 0.035 0.380 ± 0.017
 Pentadecanoic acid, C15:0 0.051 ± 0.002 0.048 ± 0.008
 Palmitic acid, C16:0 27.236 ± 1.486 26.544 ± 1.018
 Heptadecanoic acid, C17:0 0.158 ± 0.011 0.130 ± 0.013
 Stearic acid, C18:0 8.723 ± 0.571 8.245 ± 0.301
 Arachidic acid, C20:0 0.042 ± 0.008 0.040 ± 0.004
 Heneicosanoic acid, C21:0 0.049 ± 0.010 0.063 ± 0.034
 Behenic acid, C22:0 0.005 ± 0.002 0.005 ± 0.001
 Tricosanoic acid, C23:0 0.012 ± 0.002 0.015 ± 0.006
 Lignoceric acid, C24:0 0.013 ± 0.004 0.015 ± 0.003
 ∑ SFA 36.764 ± 1.027 35.568 ± 1.148
UFA
 Myristoleic acid, C14:1 0.093 ± 0.018 0.095 ± 0.012
 Pentadecenoic acid, C15:1 0.039 ± 0.004 0.044 ± 0.007
 Palmitoleic acid, C16:1 3.632 ± 0.588 3.659 ± 0.515
 Heptadecenoic acid, C17:1 0.133 ± 0.011 0.114 ± 0.010
 Oleic acid, C18:1 44.913 ± 0.995 44.166 ± 1.984
 Linoleic acid, C18:2 11.290 ± 0.612 12.711 ± 1.655
 γ-linolenic acid, C18:3 n6 0.184 ± 0.055b 0.254 ± 0.051a
 α-linolenic acid, C18:3 n3 0.215 ± 0.022b 0.306 ± 0.051a
 Gadoleic acid, C20:1 n9 0.230 ± 0.023 0.255 ± 0.037
 Eicosadienoic acid, C20:2 0.104 ± 0.006b 0.129 ± 0.014a
 Dihomo-γ-linolenic acid, C20:3 n6 0.148 ± 0.010 0.156 ± 0.006
 Arachidonic acid, C20:4 n6 1.710 ± 0.108b 1.838 ± 0.104a
 Eicosatrienoic acid, C20:3 n3 0.082 ± 0.073 0.101 ± 0.135
 Eicosapentaenoic acid, C20:5 0.004 ± 0.001b 0.007 ± 0.002a
 Erucic acid, C22:1 0.006 ± 0.002 0.010 ± 0.007
 Docosadienoic acid, C22:2 0.004 ± 0.001b 0.029 ± 0.030a
 Nervonic acid, C24:1 0.069 ± 0.004b 0.075 ± 0.005a
 Docosahexaenoic acid, C22:6 0.380 ± 0.037b 0.483 ± 0.049a
 ∑ UFA 63.236 ± 1.027 64.431 ± 1.148
 ∑ MUFA 48.885 ± 0.089 48.163 ± 0.363
 ∑ PUFA 14.351 ± 0.086b 16.269 ± 0.194a
 ∑ω-3 0.297 ± 0.048b 0.407 ± 0.093a
 ∑ω-6 2.042 ± 0.058b 2.248 ± 0.054a
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MUFA: monounsaturated fatty acids; PUFA: polyunsaturated fatty acids; SFA: saturated fatty acids; UFA: unsaturated fatty acids.

Results were presented as mean ± standard deviation.

a,b

Different superscripts within a row indicate a significant difference (P < 0.05).

1

Values represent the means of 5 replicates per group (n = 5).

2

Layers were fed with a basal diet.

3

Layers were fed with a basal diet supplemented with 0.08% N-carbamylglutamate.

The transcriptome analysis of the liver yielded a total of 47,892,306 and 54,826,737 clean reads from the different groups, with Q20 and Q30 values exceeding 90%, and a GC content close to 50% (Supplementary Table 3). Moreover, a total of 167 DEGs were obtained, in which 124 genes were significantly upregulated and 43 genes were significantly downregulated (Figure 1).

Figure 1.

Figure 1

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Volcano plot of differentially expressed genes in liver tissues among different groups. CON represents layers fed with a basal diet. TRT represents layers fed with a basal diet supplemented with 0.08% N-carbamylglutamate.

The functional annotation of DEGs in liver tissue, conducted using the KEGG database, revealed distinct enrichment patterns. Upregulated DEGs were predominantly associated with pathways such as glycan biosynthesis and metabolism, lipid metabolism, signaling molecules and interaction, and various physiological systems (Figure 2A). In contrast, downregulated DEGs were mainly enriched in energy metabolism, amino acid metabolism, nucleotide metabolism, and pathways related to diseases like cancer and viral infections (Figure 2B). Given the focus on lipid metabolism in this study, specific genes related to lipid pathways were extracted from the DEGs analysis. Notably, 3 upregulated DEGs (CPT1A, MOGAT1, and CHKA) (Figure 2A) and 2 downregulated DEGs (FASN and ETNPPL) were found to be associated with lipid metabolism (Figure 2B).

Figure 2.

Figure 2

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Functional annotation of upregulated (A) and downregulated (B) differentially expressed genes in liver tissue among different groups using the Kyoto Encyclopedia of Genes and Genomes database. CON represents layers fed with a basal diet. TRT represents layers fed with a basal diet supplemented with 0.08% N-carbamylglutamate.

In addition, pathway enrichment analysis of DEGs in liver tissue, conducted using the KEGG database, revealed significant enrichments in several key pathways (Figure 3). These pathways included drug metabolism—other enzymes, AMPK signaling pathway, glutathione metabolism, regulation of lipolysis in adipocytes, acute myeloid leukemia, PPAR signaling pathway, peroxisome, arrhythmogenic right ventricular cardiomyopathy, alanine, aspartate and glutamate metabolism, drug metabolism-cytochrome P450, metabolism of xenobiotics by cytochrome P450, and glycine, serine, and threonine metabolism. Furthermore, our analysis highlighted specific genes within these pathways. Notably, the CPT1A gene was enriched in the AMPK signaling pathway (P < 0.05) and the PPAR signaling pathway (P < 0.05), while the FASN gene was also enriched in the AMP-activated protein kinase signaling pathway (AMPK) (P < 0.05).

Figure 3.

Figure 3

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Pathway enrichment analysis of differentially expressed genes (DEGs) in liver tissues among different groups using the Kyoto Encyclopedia of Genes and Genomes database. The x-axis represents the rich factor (rich factor = number of DEGs enriched in the pathway/number of all genes in the background gene set). The y-axis represents the enriched pathway. Color represents enrichment significance, and the size of the bubble represents the number of DEGs enriched in the pathway. CON represents layers fed with a basal diet

TRT represents layers fed with a basal diet supplemented with 0.08% N-carbamylglutamate.

Analysis of expression levels revealed that CPT1A gene expression in TRT group was higher than that in CON group (P < 0.05), while FASN gene in TRT group was lower than that in CON group (P < 0.05) (Figure 4). The results verified by qRT-PCR were consistent with the RNA-seq data (Figure 5).

Figure 4.

Figure 4

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Expression of CPT1A and FASN genes regulated by N-carbamylglutamate supplementation. Values represent the means of 5 replicates per group (n = 5). a,bDifferent superscripts within the same item indicate a significant difference (P < 0.05). CON represents layers fed with a basal diet. TRT represents layers fed with a basal diet supplemented with 0.08% N-carbamylglutamate.

Figure 5.

Figure 5

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Comparison of FASN and CPT1A gene expression between qRT-PCR and RNA-seq. The data represent the logarithm of the gene expression level in the TRT group compared to its expression level in the CON group (based on 2). Values represent the means of 5 replicates per group (n = 5).

DISCUSSION

Production performance is a pivotal economic factor in poultry husbandry. Larger eggs often command higher value (Soares et al., 2023). Improved egg production rates bolster the economic sustainability of poultry farms, ensuring a consistent market supply and enhancing revenue and profitability (Ayojimi et al., 2023). Egg mass, representing the total weight of eggs produced, directly influences revenue for poultry farms. A higher egg mass translates into more products available for sale, resulting in increased economic returns (Habashy and Adomako, 2023). The feed conversion ratio, measuring the efficiency of converting feed into egg production, is crucial. A lower FCR indicates efficient feed utilization, reducing production costs and enhancing profitability (Selim and Hussein, 2020). Importantly, while previous studies have demonstrated the efficacy of dietary NCG supplementation in increasing the egg production rate of layers (Ma et al., 2023), this study found no significant impact on the production performance of layers. Consequently, any observed changes in the liver transcriptome and other physiological parameters can be directly attributed to NCG supplementation, unaffected by alterations in production performance.

The effectiveness of NCG in stimulating the synthesis of endogenous arginine has been extensively documented (Huang et al., 2019). Since arginine serves as the biological precursor for NO production (Stuehr, 2004), the biological functions of NCG are partially mediated by NO. In this study, increased levels of NO were observed in both the serum and liver of layers fed a diet supplemented with NCG. Similar results have been noted in ruminants, where NCG supplementation enhanced the expression of NO synthase (Cai et al., 2018). Furthermore, a study by Zhang et al. (2019) on intrauterine growth-restricted suckling lambs found that a diet containing NCG led to elevated circulating levels of NO. These findings suggest that NCG serves as an effective activator that stimulates the synthesis of endogenous NO. This study provides the first evidence of NCG supplementation effectively raising internal NO levels in layers.

NO has been demonstrated to exert a regulatory role in liver lipid metabolism by activating soluble guanylyl cyclase and increasing intracellular cyclic guanosine monophosphate levels (García-Villafranca et al., 2003). Noda et al. (2015) discovered that NO has beneficial effects on lipid metabolism by activating hepatic sterol regulatory element-binding protein-2, a key factor in cholesterol metabolism, and increasing the expression of the low-density lipoprotein receptor. Marra et al. (2007) observed a close relationship between NO levels and fatty acid synthesis and β-oxidation. Moreover, NO also exerts regulatory effects on mitochondrial fatty acid metabolism through reversible protein S-nitrosylation (Doulias et al., 2013). Given that NCG supplementation leads to an increase in internal NO levels, it is plausible that this increase may have a regulatory effect on lipid metabolism. The liver, being a crucial site for lipid metabolism (Demeure et al., 2009), was subjected to transcriptome analysis. The analysis revealed that a total of 124 genes were significantly upregulated, and 43 genes were significantly downregulated by NCG supplementation, indicating that NCG supplementation indeed regulated gene expression in the liver of layers.

The analysis of the liver's DEGs using the KEGG database revealed that 3 upregulated DEGs (CPT1A, MOGAT1, and CHKA) and 2 downregulated DEGs (FASN and ETNPPL) were associated with lipid metabolism. Pathway enrichment analysis indicated that 1 upregulated DEG (CPT1A) was enriched in the AMPK signaling pathway and the PPAR signaling pathway, while 1 downregulated DEG (FASN) was enriched in the AMPK signaling pathway. The AMPK signaling pathway plays a pivotal role in regulating lipid metabolism. AMPK, a cellular energy sensor, helps maintain energy balance by promoting catabolic processes, such as fatty acid oxidation and mitochondrial biogenesis. It inhibits anabolic pathways like lipid synthesis and gluconeogenesis. AMPK activation stimulates lipolysis, the breakdown of fats into fatty acids (Zhang et al., 2021). Additionally, PPARs are nuclear receptors that regulate genes involved in various metabolic processes, including lipid metabolism. When activated, PPARs control the expression of genes related to lipid metabolism, including fatty acid oxidation, uptake, and storage (Wu et al., 2023). Hence, the findings from functional annotation and pathway enrichment analysis support the effectiveness of NCG in regulating liver lipid metabolism. Moreover, the expression of specific genes like CPT1A and FASN likely plays a crucial role in this regulatory process.

The expression level analysis of CPT1A and FASN genes confirmed that NCG supplementation led to an increase in CPT1A expression and a decrease in FASN expression in the liver. To validate these results from RNA-seq, qRT-PCR was performed, and the outcomes were consistent with the RNA-seq data. CPT1A encodes an enzyme crucial for mitochondrial fatty acid oxidation. By facilitating the transport of long-chain fatty acids into mitochondria, it initiates β-oxidation, a process where fatty acids are broken down to produce energy. Higher CPT1A expression promotes the release of fatty acids from fat storage in adipose tissue (Stefanovic-Racic et al., 2008; Akkaoui et al., 2009). On the other hand, FASN encodes an enzyme responsible for the de novo synthesis of long-chain fatty acids. It plays a pivotal role in converting precursors like acetyl-CoA and malonyl-CoA into fatty acids. When FASN is downregulated, it promotes lipolysis, leading to increased fatty acid production (Anderson et al., 2019). Considering these findings, it is plausible that NCG supplementation regulates lipid metabolism in the liver by modulating the expression of CPT1A and FASN.

Indeed, changes in gene expression can significantly impact animal performance (Dang et al., 2023). In the context of layers, alterations in liver lipid metabolism can lead to changes in the fatty acid composition of the yolk (Ahmad et al., 2012). In our study, we observed notable shifts in the yolk's fatty acid profile following NCG supplementation. Specifically, there was an increase in the levels of several fatty acids, including C8:0, C18:3 n3, C18:3 n6, C20:2, C20:4 n6, C20:5, C22:2, C24:1, C22:6, as well as the overall PUFA content, ω-3 PUFA, and ω-6 PUFA. C8:0 is a valuable medium-chain saturated fatty acid, particularly beneficial for individuals dealing with pancreatic insufficiency, impaired lymphatic chylomicron transport, and fat malabsorption, as it serves as a specialized energy source (Lemarié et al., 2016). ω-3 PUFA has been linked to a reduced risk of cardiovascular disease, mental disorders, and various other health conditions, making it highly sought after in diets (Song and Zhao, 2007; Asif, 2011; Saini and Keum, 2018). Similarly, ω-6 PUFA is recommended in European cardiovascular prevention guidelines due to its effective role in reducing cardiovascular disease risk (Djuricic and Calder, 2021). High levels of ω-3 and ω-6 PUFA are characteristic of functional foods (Tanna and Mishra, 2018). Therefore, NCG supplementation can lead to the production of functional eggs with elevated levels of ω-3 and ω-6 PUFA in the yolk. Given that egg consumption has been associated with a potential risk of cardiovascular diseases (Miranda et al., 2015), the increase in ω-3 and ω-6 PUFA content in the yolk is expected to mitigate the negative effects of eggs (Hugo and Roodt, 2007; Palmquist, 2009).

In conclusion, this study demonstrated that NCG supplementation regulated the lipid metabolism in the liver of layers by increasing serum and liver NO contents, thereby inducing the layers to produce functional eggs. Additionally, we found that the liver lipid metabolism-regulating ability of NCG supplementation is achieved by upregulating CPT1A expression and downregulating FASN expression in the liver, thereby regulating the AMPK signaling pathway and the PPAR signaling pathways.

It is worth to denote that the safety of NCG has been assessed in previous studies. Wu et al. (2015) conducted toxicity evaluations of NCG in Sprague–Dawley rats and found no observed toxicity effects, mutagenicity, or genotoxicity even with high-dose NCG supplementation in the diet. Hence, the safety concerns related to NCG supplementation are likely minimal.

Overall, NCG shows promise as an additive for producing functional eggs by regulating liver lipid metabolism.

ACKNOWLEDGMENTS

Qiulin Liu: writing—original draft, investigation, writing—review and editing; Jiabo li: writing—original draft, investigation, writing—review and editing; Di Han: investigation and methodology; Jinfeng Wang: formal analysis, investigation; Jian Zheng: conceptualization, methodology; Wei Ma: supervision, writing—review and editing; and Chunqiang Wang: supervision, writing—review and editing. This study was supported by the Liaoning Natural Science Foundation Project (2022-M S-387); Liaoning Science and Technology Special Project (2022030808-JH5/104); Jinzhou Medical University School of Animal Husbandry and Veterinary Medicine Scientific Research Project (2023rk02, 2023rk10, 2023py05).

DISCLOSURES

No potential conflict of interest was reported by the authors.

Footnotes

Supplementary material associated with this article can be found in the online version at doi:10.1016/j.psj.2023.103223.

Contributor Information

Wei Ma, Email: mwworld@163.com.

Chunqiang Wang, Email: wcqworld@126.com.

Appendix. Supplementary materials

mmc1.docx (19.4KB, docx)

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