Identification of genes related to growth and amino acid metabolism from the transcriptome profile of the liver of growing laying hens

Abstract

The growing period is a critical period for the growth and development of hens and affects their production performance during the laying period. During the early stage of growing, bone and muscle growth is rapid, making it necessary to provide sufficient amino acids (AA) to support the growth and development of laying hens. In this experiment, RNA-Sequencing (RNA-Seq) was applied to compare the liver tissues from 6- to 12-wk-old growing laying hens to identify candidate genes related to growth and AA transport and metabolism. In the liver tissues, 596 differentially expressed genes (DEG) were identified, of which 424 genes were up-regulated and 172 were down-regulated. Through enrichment analysis and DEGs analysis, some DEGs and pathways related to AA transport and metabolism were identified. Additionally, there were significantly increased activities in the liver of glutamate dehydrogenase (GDH), glutamic oxaloacetic transaminase (GOT), and glutamate pyruvate transaminase (GPT). Meanwhile, the level of serum insulin-like growth factor binding protein-5 (IGFBP-5) significantly elevated, and insulin-like growth factor-1 (IGF-1) levels significantly reduced at 12 wk compared to 6 wk. The AA contents in the breast muscle were not significantly altered, while the levels of the free AA in the serum underwent significant changes. This study discovered that the transport and metabolism of AAs in growing laying hens at different ages changed, which influenced the growth and development of growing laying hens. This contributes to future research on the mechanisms of growth and AA metabolism during the growing period of laying hens.

INTRODUCTION

Amino acids (AA) are an essential constituent of proteins, which are used for poultry growth, egg production, immunity, and enzyme activity (Beski et al., 2015). Poultry and eggs contain sufficient and appropriate proportions of AAs to provide high-quality protein, playing an influential role in human health (Rehault-Godbert et al., 2019). Only with an adequate and appropriate intake of AAs can poultry production reach maximum efficient (Baker, 2009). The growth and production of poultry can be affected by over- or under-intake of AAs (He et al., 2021). The growing period is a critical stage for the growth and development of the hens and affects their production performance after laying. The size and weight of growing laying hens have a direct impact on egg production performance, which ultimately affects the economic profitability of poultry farming.

The liver is the center of nutritional metabolism in animals and is also a vital organ in the regulation of growth and development. AAs within the diet undergo synthesis, deamination, and transamidation in the liver (Stoll et al., 1998). Furthermore, the liver is the primary site where AA catabolism begins and completes in poultry (He et al., 2021). The intake of nutrients, such as AAs, acts as a signaling factor that induces transcription, protein synthesis, and modifications that affect DNA replication and the regulation of gene expression in poultry cells (Ruemmele and Garnier-Lengline, 2012). Thus, the expression of genes in the organism can be regulated by supplementation or deficiency of exogenous AAs (Kilberg et al., 2005).

The advancement of RNA-Sequencing (RNA-Seq) provides a new technology for further research, revealing abundant information about the transcriptome at the mRNA level through transcriptomic and gene enrichment analyses (Lian et al., 2018). Currently, most RNA-Seq in poultry is focused on growth traits (Allais et al., 2019), meat qualit (Teng et al., 2019), and ovaries (Zhou et al., 2020), with little research on AA metabolism in growing laying hens. Therefore, we believe that this technology is appropriate for uncovering the gene expression profile and crucial pathways of AA metabolism in growing laying hens. In this experiment, we utilized RNA-Seq technology to generate transcriptome profiles of liver tissues. We conducted bioinformatics analysis and validation experiments to identify functional genes and regulatory mechanisms that regulate growth and AA metabolism in laying hens. This research provides a theoretical basis for regulating AA metabolism to improve the growth of hens.

MATERIALS AND METHODS

Animals and Sample Collection

All hens used in this experiment were handled by the Chinese Animal Welfare Guidelines, and the experiments were approved by the University Animal Welfare Committee of Huazhong Agricultural University (approval number: HZAUCH-2022-0015).

The Jing Tint 6 used in this study were obtained from Hebi Hongsheng Poultry Industry Co. Ltd (Henan, China). All laying hens were housed in wire cages, with free access to clean water and feed provided daily at 8:00 am and 5:00 pm. They were kept in an environment with a temperature of 22°C ± 2°C and a humidity of 55% ± 5%. The basal diets and nutritional levels are shown in Table S1.

A total of 24 laying hens (12 birds per week of age) were randomly selected at 6 and 12 wk of age and their body weights were 367.78 ± 9.39 g and 868.88 ± 9.64 g, respectively. After overnight fasting, blood samples were collected via the wing vein, and the blood samples were centrifuged at 3,000 rpm for 15 min to obtain serum, which was stored at −80°C until use. Liver and breast muscle tissue samples were collected from randomly selected laying hens at different stages and stored at −80°C until use.

Total RNA Extraction and RNA-Seq Library Construction

Following the manufacturer's instructions (Invitrogen, Thermo Fisher Scientific, Waltham, MA), total RNA was extracted from chicken liver tissue using TRIzol reagent with DNase I (Takara, Dalian, China) to remove genomic DNA. Subsequently, the concentration and purity of RNA were detected by NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific). The integrity of RNA was examined by 1% agarose gel electrophoresis, and the RNA value was determined using a 2100 Bioanalyzer (Agilent Technologies Inc., Santa Clara, CA).

According to the manufacturer's instructions (Illumina, San Diego, CA), the Shanghai Majorbio Bio-pharm Biotechnology Co. Ltd (Shanghai, China) performed RNA library construction and sequencing. The transcriptome library was prepared using 1 μg of total RNA according to the TruSeq RNA sample preparation kit from Illumina.

Quality Control and Read Mapping

Raw paired-end reads were used by the fastp software for trimming and quality control using default parameters (Chen et al., 2018). The clean reads were then individually compared to the orientation pattern of the reference genome using the HISAT2 software (Kim et al., 2015). Each sample's mapped readings were assembled by the StringTie software in a reference-based manner (Pertea et al., 2015).

Differential Expression Analysis and Functional Enrichment

The expression levels of each gene were calculated using the fragments per kilobase per million reads (FPKM) method to determine the differentially expressed genes (DEG) between the 2 groups. Gene abundance was quantified by RSEM (http://deweylab.biostat.wisc.edu/rsem/). Basically, differential expression analysis was performed using DESeq2, and DEGs with |log₂(fold change)| ≥ 0.585 and P-value < 0.05 were considered as genes with a significantly different expression. Subsequently, the analysis of functional enrichment including Gene Ontology (GO, http://www.geneontology.org) and Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/) was also performed at P-value ≤ 0.05 to determine which DEGs were significantly enriched in GO terms and metabolic pathways by comparison with the whole transcriptome background. The GO functional enrichment and KEGG pathway analyses were conducted by Goatools (https://github.com/tanghaibao/Goatools) and KOBAS (http://kobas.cbi.pku.edu.cn/home.do).

Gene Expression Analysis by qRT-PCR

To verify the reproducibility and accuracy of RNA-Seq gene expression data, we performed quantitative real-time PCR (qRT-PCR) validation of DEGs in biological replicates of each group. We utilized TRIzol (Takara) for RNA extraction from liver tissues and quantified the extracted RNA using NanoDrop 2000 (Thermo Fisher Scientific).

Reverse transcription of 1 μg RNA was performed using the PrimeScript RT reagent Kit with the gDNA Eraser (Takara). qRT-PCR amplification was performed using TB Green Premix Ex Taq Ⅱ (Tli RNaseH Plus) (Takara) on a Bio-Rad CFX384 real-time PCR system, as described previously (Luo et al., 2019). The relative expression levels of the genes were calculated using the 2^−∆∆Ct method, with the use of β-actin as a control (Livak and Schmittgen 2001). The primer sequences are shown in Table 1. All primers were designed by Sangon Biotech Co., Ltd. (Shanghai, China).

Table 1.

Primer sequences of the genes used in qRT-PCR testing.

Gene	GenBank number	Primer sequence (5′-3′)	Product (bp)
SLC7A1	NM_001145490.2	CTCTGGCTTGGTGGTGAACATCTC	89
		GCGTGCTTGGCTTGAGGGTAG
SLC7A10	XM_414136.8	AGCGGCTGGAACTTCTTGAACTATG	146
		TGGGGTGACATGGCAGTGAAATATG
SLC7A11	XM_426289.7	GCTGTCGTGACGGTGCCTAATG	87
		CTCTTGTGGCTGCCTGCTGTC
SLC6A6	XM_046899913.1	TGCTTCTGTTGCTTGGACTGGATAG	107
		TTCCCGTCGGTAACCCTTCCTTAG
SLC6A12	XM_046901482.1	TGCCAACCGCTTCTACGACAAC	114
		AACAAGAAGACAGCCAAGCAGAGAC
AASS	XM_015282251.4	TGGCTATCAGGAGGGAAGATGTCAG	271
		TGGGCTTTAATGGTGTGCGAGAAG
IGF-1	NM_001004384.3	GCAGTAGACGCTTACACCACAAGG	83
		ACAGTACATCTCCAGCCTCCTCAG
IGFBP-5	XM_422069.8	GCGACCGAAAGGGATTCTACAAGAG	128
		CAGGTCTCCGCTCAGGTAGTCAG
β-actin	NM_205518.2	GCCCTGGCACCTAGCACAATG	129
		CTCCTGCTTGCTGATCCACATCTG

Abbreviations: AASS, aminoadipate-semialdehyde synthase; IGF-1, insulin like growth factor 1; IGFBP-5, insulin like growth factor binding protein 5; SLC6A12, solute carrier family 6 member 12; SLC6A6, solute carrier family 6 member 6; SLC7A1, solute carrier family 7 member 1; SLC7A10, solute carrier family 7 member 10; SLC7A11, solute carrier family 7 member 11; β-actin, actin beta.

Measurement of Amino Acid-Metabolizing Enzymes Activity

For the determination of amino acid-metabolizing enzymes, liver tissue homogenates were prepared according to the requirements of the corresponding kits. Glutamate dehydrogenase (GDH), glutamic oxaloacetic transaminase (GOT), and glutamate aminotransferase (GPT) activities were measured using corresponding commercial kits (Jiancheng Bioengineering Institute, Nanjing, China). The protein content of the samples was measured using the BCA kit (Beyotime Biotechnology, Shanghai, China).

Measurement of Serum Growth Factor Concentration

Serum concentrations of growth hormone (GH), insulin-like growth factor-1 (IGF-1), and insulin-like growth factor binding protein-5 (IGFBP-5) were measured using the ELISA kits (Jiangsu Meimian Industrial Co., Ltd, Jiangsu, China), respectively.

Measurement of Amino Acid in Muscle and Free Amino Acid in Serum

The automatic amino acid analyzer was used to determine the AA content in muscle and the free AA content in serum. Briefly, 0.1 g of breast muscle, was spiked with 6 mol/L of HCl solution, placed in an oven at 110°C for 24 h, cooled, filtered, and fixed to 50 mL; subsequently, 0.5 mL was pipetted for nitrogen blowing, and determination was performed using the L-8900 automatic amino acid analyzer (Hitachi Limited, Tokyo, Japan). Then, 400 μL of the serum was pipetted, spiked with 1.2 mL of 8% sulfosalicylic acid, deproteinized, left for 24 h, and centrifuged at 20,000 rpm; the supernatant was extracted and measured using the L-8900 automatic amino acid analyzer (Hitachi Limited).

Statistical Analysis

The statistical analysis was performed using the independent samples Student's t-test and the results are expressed as mean ± SEM. The level of significance of the data was designated as P < 0.05, and these analyses were performed using SPSS 24.0 (SPSS Inc., IBM, New York, NY). These figures were generated with GraphPad Prism 8 (Graph Pad Software Inc., San Diego, CA).

RESULTS

Sequencing Data and Quality Control

After the transcriptomic analysis of liver tissue samples from 6- and 12-wk-old growing laying hens, 96.91 Gb of clean reads were obtained by quality control. The clean reads for each sample reached values above 6.12 Gb, and the percentage of Q30 bases was above 94.18%. The guanine-cytosine (GC) content ranged between 48.71% and 50.74%. More than 93.56% of the reads could be mapped to the genome of the laying hens (Six, with 93.56%–95.25% alignments; Twe, with 93.73%–94.79% alignments) (Table 2). The mapped reads are distributed throughout various regions of the chicken reference genome, and most of them are mapped to the coding sequences of both exons and introns. The total fragments of the coding region ranged from 79.55% to 82.51%, the number of introns ranged from 3.30% to 5.29%, and the percentage of intergenic regions ranged from 2.76% to 3.54%. These results demonstrate that the data were sufficiently reliable to proceed to further analysis.

Table 2.

Sequencing data.

Sample	Raw reads	Raw bases	Clean reads	Clean bases	Total mapped	Q30 (%)	GC content (%)
Six1	46,203,900	6,976,788,900	45,862,434	6,884,662,190	43,533,152 (94.92%)	94.62	49.38
Six2	65,197,422	9,844,810,722	64,691,782	9,629,107,831	60,742,918 (93.90%)	94.94	50.74
Six3	53,863,294	8,133,357,394	53,438,978	7,995,836,600	50,234,475 (94.00%)	94.29	49.85
Six4	40,993,590	6,190,032,090	40,691,690	6,122,688,411	38,758,233 (95.25%)	94.41	48.71
Six5	47,626,678	7,191,628,378	47,190,442	7,075,836,539	44,728,105 (94.78%)	94.24	49.19
Six6	54,428,198	8,218,657,898	53,971,232	8,058,847,645	50,494,964 (93.56%)	94.18	49.31
Twe1	42,838,232	6,468,573,032	42,472,364	6,353,362,822	39,808,879 (93.73%)	94.37	49.91
Twe2	54,934,772	8,295,150,572	54,538,030	8,141,998,094	51,522,790 (94.47%)	95.11	49.66
Twe3	69,363,514	10,473,890,614	68,881,176	10,226,935,206	64,661,589 (93.87%)	94.81	49.87
Twe4	58,029,252	8,762,417,052	57,628,904	8,558,802,321	54,243,139 (94.12%)	94.83	49.53
Twe5	61,791,992	9,330,590,792	61,357,992	9,129,682,373	57,965,925 (94.47%)	94.91	49.33
Twe6	58,847,116	8,885,914,516	58,329,962	8,734,283,981	55,292,732 (94.79%)	94.56	49.39

Note: Raw reads: total number of entries of raw sequencing data; Raw bases: total amount of raw sequencing data; Clean reads: total number of entries of post-quality control sequencing data; Clean bases: total amount of post-quality control sequencing data; Total mapped: the number of Clean reads that can be localized to the genome; Q30 (%): quality assessment of post-quality control sequencing data, referring to the percentage of bases with sequencing quality above 99.9% of the total bases; Guanine-cytosine (GC) content (%): the sum of G and C bases corresponding to quality control (QC) data as a percentage of the total bases.

Gene Expression Profile and Differential Expression Analysis

Gene expression levels are typically measured by FPKM or transcripts per million reads (TPM). The FPKM method can eliminate the effect of gene length and sequencing volume differences on the calculation of the gene expression and was therefore selected to perform the expression analysis. The overall distribution of expression levels is shown in Figure 1A. Principal component analysis (PCA) was performed, and the results of the PCA are shown in Figure 1B. We obtained 31.26% for PC1 and 15.25% for PC2. Based on this, RNA-Seq data analysis of liver tissues from the 6-wk-old group (Six) and 12-wk-old group (Twe) detected a total of 18,736 expressed genes, which were subjected to differential expression analysis to further identify transcriptional changes between liver tissues of laying hens of different ages. A total of 596 DEGs were identified by comparing the gene expression between the 2 groups (|log₂(fold change)| ≥ 0.585, P-value < 0.05), with 424 up-regulated and 172 down-regulated genes. The volcano plot obtained according to the RNA-Seq analysis showed statistically significant differences in gene expression levels between 2 age groups (Figure 1C). Cluster analysis was performed to compare the gene expression patterns of the DEGs in the 2 different age groups. Different colors in the heat map represent different gene expression levels, with red indicating a higher expression of the gene and blue indicating a lower expression. As shown by the heat map, the expression patterns of the same group were similar, and the results are shown in Figure 1D.

Figure 1.

Analysis of the relationship between samples. (A) Distribution of expression levels; (B) Principal component analysis (PCA) analysis between samples; (C) Volcano plot of differentially expressed genes (DGE) between the 6 wk and 12 wk groups; (D) Heatmap of the expression levels of DEGs.

Go and KEGG Enrichment Analysis of DEGs

The GO is a database created by the Gene Ontology Consortium. It enables the categorization of genes in the selected gene set: biological processes involved, components that make up the cell, and molecular functions achieved, among others. For this, we employed GO analysis to functionally annotate RNA-Seq. A total of 43 GO terms were identified by comparing 2 different age groups of laying hens, and the top 30 GO terms for DEGs in the comparative library in the liver are listed in Figure 2. These GO terms include cellular processes, biological regulation, metabolic process, binding, catalytic activity, transporter activity, membrane part, organelles, and cell part, among others. Significant enrichment of biological processes with GO terms related to growth and AA catabolism, synthesis, metabolism, and translocation is listed in Table S2. Genes associated with these GO terms may be essential for the synthesis, transport, and metabolism of AAs required for the growth and development of growing laying hens.

Figure 2.

The top 30 gene ontology (GO) terms of differentially expressed genes (DEG) of in the comparative library in the liver.

To better understand the modulatory role of DEGs in AA metabolism at different ages, we performed KEGG enrichment analysis. The total DEGs were condensed into 211 KEGG pathways, and the significantly enriched pathways are shown in Figure 3. In liver tissue, DEGs showed significant enrichment in processes such as circadian rhythm, calcium signaling pathway, cardiac muscle contraction, glycine, serine, and threonine metabolism, and tryptophan metabolism. The KEGG enrichment pathway for AA metabolism is shown in Table 3, and a more comprehensive list of the KEGG enrichment pathways for growth and AA metabolism is shown in Table S3. Insights on the temporal and spatial expression of genes associated with AA metabolism in the liver can be gained by analyzing the genes connected to the pathway connected to AA metabolism.

Figure 3.

Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially expressed genes (DEGs) between the 6 wk and 12 wk groups.

Table 3.

KEEG enrichment pathway associated with amino acid metabolism.

Number	Pathway id	Description	P-value	Gene ids	Gene names
4	map00260	Glycine, serine and threonine metabolism	0.0020	ENSGALG00000016196|ENSGALG00000002397|ENSGALG00000016651|ENSGALG00000032836	;PSPH;;
3	map00380	Tryptophan metabolism	0.0166	ENSGALG00000006236|ENSGALG00000012197|ENSGALG00000032836	TPH1;ACMSD;
2	map00360	Phenylalanine metabolism	0.0197	ENSGALG00000033365|ENSGALG00000032836	ALDH1A3;
3	map00330	Arginine and proline metabolism	0.0237	ENSGALG00000008177|ENSGALG00000032836|ENSGALG00000015977	NOS1;;SMOX
2	map00340	Histidine metabolism	0.0377	ENSGALG00000033365|ENSGALG00000032836	ALDH1A3;
2	map00410	beta-Alanine metabolism	0.0599	ENSGALG00000033365|ENSGALG00000015977	ALDH1A3;; SMOX
2	map00350	Tyrosine metabolism	0.0779	ENSGALG00000033365|ENSGALG00000032836	ALDH1A3;
1	map00430	Taurine and hypotaurine metabolism	0.1099	ENSGALG00000006565
1	map00220	Arginine biosynthesis	0.2078	ENSGALG00000008177	NOS1
1	map00250	Alanine, aspartate and glutamate metabolism	0.3645	ENSGALG00000013728	PPAT
1	map00270	Cysteine and methionine metabolism	0.4631	ENSGALG00000016196
1	map00310	Lysine degradation	0.5750	ENSGALG00000008953	AASS
1	map00480	Glutathione metabolism	0.4700	ENSGALG00000006565

Validation of DEGs by qRT-PCR

According to the RNA-Seq expression profile, we identified 8 DEGs (solute carrier family 7 member 1 (SLC7A1), solute carrier family 7 member 10 (SLC7A10), solute carrier family 7 member 11 (SLC7A11), solute carrier family 6 member 6 (SLC6A6), solute carrier family 6 member 12 (SLC6A12), aminoadipate-semialdehyde synthase (AASS), insulin like growth factor 1 (IGF-1), and insulin like growth factor binding protein 5 (IGFBP-5)), all of which are related to AA metabolism, transport and organism growth, by qRT-PCR validation.

As shown in Figure 4A, the relative expressions of SLC7A1, SLC7A10, SLC7A11, and AASS were extremely significantly increased (P < 0.01), and that of IGFBP-5 was significantly increased (P < 0.05) in the 12 wk group compared with the 6 wk group, whereas the expression levels of SLC6A6 and SLC6A12 showed an extreme decrease (P < 0.01), and that of IGF-1 showed a significant decrease (P < 0.05). These findings are consistent with the variation trend of the RNA-Seq results as shown in Figure 4B, demonstrating the reliability and accuracy of our sequencing data and indicating that RNA-Seq is an excellent reference method for expression profiling.

Figure 4.

Quantitative real-time PCR (qRT-PCR) validation of RNA sequencing (RNA-seq) results. (A) The result of qRT-PCR. The results are presented as mean ± SEM (n = 8). (B) The results of the RNA-seq. The results are presented as mean ± SEM (n = 6). * for P < 0.05, ** for P < 0.01.

Activity of Hepatic Amino Acid-Metabolizing Enzymes

To compare the variations in the levels of amino acid-metabolizing enzymes in the hepatic liver at different weeks of age, we measured the activity levels of GDH, GOT, and GPT. As demonstrated in Figure 5, the 12 wk group exhibited a highly significant increase in levels of GDH and GPT viability in comparison to the 6 wk group (P < 0.01), and the level of GOT activity significantly enhanced (P < 0.05). As a result, the level of activity of hepatic amino acid-metabolizing enzymes increased significantly with age throughout the growing period.

Figure 5.

Variation of amino acid metabolizing enzyme activity in hepatic at different age groups. (A) Glutamate dehydrogenase (GDH), (B) glutamic oxaloacetic transaminase (GOT), and (C) glutamate pyruvate transaminase (GPT). Data are expressed as mean ± SEM (n = 12). Significant difference between 6 wk and 12 wk groups, * for P < 0.05, ** for P < 0.01.

Serum Growth Factor Concentrations

To assess variations in serum growth factor levels at different growth stages, we quantified the levels of GH, IGF-1, and IGFBP-5. According to Figure 6, there was no significant change in serum GH levels in the 12 wk group compared to the 6 wk group. However, IGFBP-5 levels showed a significant increase (P < 0.05), and IGF-1 levels markedly reduced (P < 0.05). Hence, serum growth factor levels exhibit varying changes during growth and development.

Figure 6.

Variation of serum growth factors at different age groups. (A) Growth hormone (GH), (B) insulin-like growth factor-1 (IGF-1), and (C) insulin-like growth factor binding protein-5 (IGFBP-5). Data are expressed as mean ± SEM (n = 12). Significant difference between 6 wk and 12 wk groups, * for P < 0.05.

Amino Acid Content in Muscle and Free Amino Acid in Serum

To further investigate the variation in AAs in the organism, we examined the AA contents in muscle and the levels of free AA in serum. The AA contents of muscle during different age weeks are shown in Table 4. The AA contents of the muscle in the hens in the 12 wk group showed no significant difference from the 6 wk group, indicating that the AA content of muscle does not change significantly with aging.

Table 4.

Amino acid content of breast muscle in growing layer hens at different ages.

Amino acid (%)	6 wk	12 wk
Asp	1.69 ± 0.04	1.73 ± 0.05
Thr	0.82 ± 0.02	0.83 ± 0.04
Ser	0.73 ± 0.02	0.73 ± 0.04
Glu	2.76 ± 0.04	2.79 ± 0.05
Gly	0.89 ± 0.03	0.93 ± 0.07
Ala	0.98 ± 0.02	1.00 ± 0.04
Val	0.94 ± 0.02	0.96 ± 0.04
Met	0.31 ± 0.04	0.25 ± 0.05
Ile	0.88 ± 0.02	0.89 ± 0.04
Leu	1.48 ± 0.04	1.50 ± 0.06
Tyr	1.06 ± 0.04	1.07 ± 0.05
Phe	0.73 ± 0.02	0.74 ± 0.02
Lys	1.61 ± 0.04	1.62 ± 0.06
His	0.60 ± 0.03	0.62 ± 0.02
Arg	1.18 ± 0.03	1.17 ± 0.04

Note. Results are represented as the mean ± SEM (n = 12).

The levels of free AA in the serum of hens at different ages are shown in Table 5. With the exception of Val, Phe, and His, all free AA in the serum of the hens in the 12 wk group significantly changed from the 6 wk group. Among them, the level of Thr, Ser, Ala, Cys, and Met were extremely markedly reduced (P < 0.01), whereas the level of Glu, Gly, Ile, Leu, Tyr, and Arg were highly significantly increased (P < 0.01), and Lys levels showed a marked increase (P < 0.05). Contrary to AA in muscle, the levels of free AA in serum drastically altered, suggesting that AA metabolism undergoes different age-related changes in laying hens.

Table 5.

Serum free amino acid levels in growing layer hens at different ages.

Amino acid (μg/mL)	6 wk	12 wk
Thr	142.67 ± 4.50	105.41 ± 10.94**
Ser	86.10 ± 8.75	52.87 ± 4.86**
Glu	23.12 ± 1.92	30.56 ± 2.71**
Gly	26.63 ± 1.72	33.00 ± 2.52**
Ala	63.59 ± 6.68	50.25 ± 4.44**
Cys	18.27 ± 0.46	13.03 ± 1.97**
Val	13.92 ± 1.15	14.92 ± 1.96
Met	6.95 ± 0.71	5.93 ± 0.43**
Ile	7.21 ± 1.38	8.78 ± 0.78**
Leu	14.30 ± 2.08	16.94 ± 2.09**
Tyr	20.92 ± 3.14	32.47 ± 3.62**
Phe	15.62 ± 1.65	16.83 ± 1.98
Lys	43.31 ± 5.91	49.27 ± 6.28*
His	13.66 ± 0.83	13.96 ± 0.27
Arg	30.09 ± 4.41	40.91 ± 3.25**

Note. Results are represented as the mean ± SEM (n = 12).

Significant difference between 6 wk and 12 wk groups, * for P < 0.05, ** for P < 0.01.

DISCUSSION

The growing period is a pivotal stage that affects the growth and development of hens and their production performance after laying. Consequently, it is essential to provide hens with sufficient and appropriate AAs and proteins during this period. A large number of genes associated with growth and AA metabolism have been identified in broilers, such as MyoG, Myf5 (Zhang et al., 2014), IGF-1 (Beccavin et al., 2001), and SLC7A1, SLC7A2, SLC7A3 (Khwatenge et al., 2020), among others. However, few studies have been conducted to provide information related to liver AA metabolism and growth during the growing period in laying hens from a transcriptomic perspective. In this experiment, RNA-Seq was performed on laying hens of different ages to reveal DEGs associated with AA transport, metabolism, and growth. A total of 596 DEGs were identified by comparing the gene expression between the 6 wk and 12 wk groups of growing laying hens.

The results of our GO enrichment analysis revealed that down- and up-regulated genes identified DEGs, which in turn were associated with GO biological process terms, and GO biological processes were mainly involved in biological processes, cellular components, and molecular functions. Additionally, the screening of significantly enriched GO terms related to AA anabolism, catabolism, and transport processes revealed that they were primarily involved in Ser biosynthetic and metabolic processes, Lys catabolic and metabolic processes, glutamate homeostasis, secretion, and metabolic processes, sulfur amino acid biosynthetic, and metabolic processes, Thr catabolic and metabolic processes, cellular modified AA metabolic and biosynthetic processes, and α-AA biosynthetic and metabolic processes. The main DEGs detected in these GO terms include PSPH, SLC7A11, AASS, and others.

Pathways associated with amino acid metabolism were screened by KEGG enrichment analysis, with 5 significantly enriched pathways including Gly, Ser, and Thr metabolism, Trp metabolism, Phe metabolism, Arg and Pro metabolism, and His metabolism. Among the primarily detected DEGs, the PSPH gene is associated mainly with Ser biosynthesis and metabolism. PSPH has wide distribution throughout multiple cell types and plays a crucial role in L-serine biosynthesis (Park et al., 2019). The TPH1 and ACMSD genes are mainly involved in Trp metabolism, Trp undergoes conversion into 5-hydroxytryptophan, which is facilitated by tryptophan hydroxylase, predominantly encoded by the TPH1 gene (Chong et al., 2000); ACMSD is a crucial enzyme involved in tryptophan metabolism (Kurniati et al., 2023), whereas ALDH1A3 is linked to Phe and His metabolism. Finally, NOS1 and SMOX are associated with Arg and Pro metabolism.

In addition, our screening of DEGs identified several genes linked to AA transportation and metabolism, such as SLC7A1, SLC7A10, SLC7A11, SLC6A6, and SLC6A12. Among them, SLC7A1, SLC7A10, and SLC7A11 all belong to the SLC7 family, which are non-Na⁺-dependent AA transporters, SLC7A1 predominantly transports Lys, Arg, and His, SLC7A10 mainly transports Gly, Ala, Ser, and Cys, and SLC7A11 mainly carries Glu and Gys (Hyde et al., 2003). Increasing the concentration of lysine in broiler diets increases the mRNA expression of SLC7A1, indicating an increase in lysine transport, which in turn affects protein synthesis (Khwatenge et al., 2020). The 2 amino acid response element (AARE)-like sequences in the promoter region of the SLC7A11 gene act as ATF4 binding elements, thereby inducing transcription of the SLC7A11 gene (Sato et al., 2004). This study demonstrated a significant up-regulation of SLC7A1, SLC7A10, and SLC7A11 mRNA expression during growth, indicating an increase in non-Na⁺-dependent amino acid transport capacity. Both SLC6A6 and SLC6A12 are neutral Na⁺-dependent amino acid transporters that can transport taurine (Hou et al., 2019) and γ-aminobutyric acid (De Paepe et al., 2018). The SLC6A6 signaling encoded by the taurine transporter affects cell proliferation and cell survival (Warskulat et al., 2007). Based on the results of our study, the mRNA expression of SLC6A6 and SLC6A12 decreased with growth, suggesting a decrease in the associated AA transport capabilities. It is widely recognized that these AA transporters are involved in AA transport via the mTOR signaling pathway (Goberdhan et al., 2016; Broer and Broer, 2017). This process modifies the expression of genes involved in AA transport and mTOR regulation in the liver with advancing age. Consequently, this stimulates protein synthesis via mTOR. The AASS is a bifunctional protein containing lysine ketoglutarate reductase (LKR) and saccharopine dehydrogenase (SacD), which mainly acts in the initial 2 steps of the irreversible lysine catabolism (Markovitz et al., 1984). A study conducted on mice showed an increase in LKR activity, AASS mRNA expression, and AASS protein expression with an increase in lysine levels (Kiess et al., 2013). The findings of this experiment demonstrate an increase in AASS mRNA expression with age, indicating an enhanced metabolic capacity. In overview, the expression levels of genes related to amino acid metabolism and transport were altered to varying levels during the growth period, which in turn affected the metabolism and transport of the corresponding AAs.

We additionally screened for growth-related DEGs, including IGF-1 and IGFBP-5. Of these, IGF-1 is an endocrine hormone growth factor that stimulates body growth and exerts feedback to the pituitary gland (Kuemmerle, 2012). The IGF binding proteins (IGFBPs) are important physiological regulators of the interaction of IGFs with liver receptors (Kuemmerle, 2012). IGFBP-5 proteins are important in controlling cell survival, differentiation, and apoptosis (Hwang et al., 2016), and IGFBPs are capable of enhancing or inhibiting the activity of IGFs in a manner that is cell and tissue specific (Beattie et al., 2006). The results of this experiment show that both the IGFBP-5 mRNA expression level and serum levels increase with growth during the growing period, while IGF-1 shows the opposite trend, and IGFBP-5 may inhibit the activity of IGF-1, and the specific mechanism needs further study. The level of serum GH, associated with growth, was also tested and was found to remain constant throughout growth. It is possible that during the growth period the organism is in a period of rapid growth, so GH has been at a more stable level.

To further investigate AA metabolism, we assayed the activities of amino acid-metabolizing enzymes. The GDH is widespread in hepatic tissues and can convert α-ketoglutarate to glutamate (Legendre et al., 2020). In addition, during AA deamidation, GDH is able to form Gln and Asn with transaminases and convert them to urea, which is a crucial enzyme for the AA catabolic and anabolic pathways. Both GOT and GPT are the main aminotransferases for the metabolic conversion of AAs in the liver (Zhang et al., 2021), which in turn provides gluconeogenic precursors. One research verified that GPT is crucial for glucose and AA intermediate metabolism (Napolitano et al., 2018). According to another study, heat stress increased the activity of GOT and GPT in broiler liver, indicating that heat stress increases the metabolism of AAs in the liver of broiler chickens (Ma et al., 2021). Additionally, it has been demonstrated that protein levels can influence the activity of liver AA metabolism conversion enzymes, and appropriate protein levels can promote the growth and development of the organism (Tanabe et al., 2002). Our results demonstrated that the activities of GDH, GOT, and GPT in the liver increased with growth during the growing period of laying hens, indicating the elevated metabolizing capacity of AAs in the liver.

To further investigate the changes in AA metabolism in the organism at different weeks of age during the growing period, we examined the AA contents in breast muscle and the free AA in serum. There was no significant difference in the AA contents of the breast muscle of laying hens in the 2 age groups. According to one study, feeding different experiment diets had no significant effect on the AA in broiler breast muscle, indicating that it is difficult to alter the contents of AA in muscle (Saleh et al., 2021). As laying hens grow, muscle weight increases and so does AA deposition. However, there were significant variations in most of the serum free AA, which may be related to the AA transporter SLC7 family, in which SLC7A1, SLC7A10, and SLC7A11 mRNA expression levels were significantly elevated to enhance the transport of Gly, Glu, Lys, and Arg, and consequently their concentrations in serum. Although the expression level of SLC7A10 mRNA was increased, the serum levels of Ser, Ala, and Cys were decreased, which may be related not only to AA transport but also to AA synthesis. A significant alteration in free AAs concentration was observed in the serum, while no significant changes were detected in muscle AA contents. Further studies are required to fully understand AA metabolism.

CONCLUSIONS

In this study, we demonstrated that amino acid metabolism changes significantly in growing laying hens. Through transcriptome sequencing, we examined DEGs associated with growth, AA transport, and AA metabolism, including PSPH, TPH1, ACMSD, ALDH1A3, NOS1, SMOX, SLC7A1, SLC7A10, SLC7A11, SLC6A6, SLC6A12, AASS, IGF-1, and IGFBP-5. The DEGs obtained in this study could serve as potential candidate genes that could provide a theoretical basis for further studies on the customized response of growing laying hens to individualized AA nutrition.