Integrated transcriptomic and metabolomic analyses reveal potential regulatory pathways regulating bone metabolism pre- and postsexual maturity in hens

Abstract

At the onset of sexual maturity, the increasing circulating estrogen stimulates the formation of medullary bone, which provides available calcium for eggshell formation. The bone loss of laying hens is caused by the continuous dynamic changes of structure bone leading to bone fragility and susceptibility. The degree of medullary bone mineralization in sexual maturity is positively correlated with bone quality in the late laying stage. This study aimed to explore the molecular regulation mechanism of bone metabolism pre- and postsexual maturity in hens based on the joint analysis of transcriptome and metabolome. A total of 50 Hy-line Sonia pullets with comparable body weight at 13 wk were selected. Eight pullets were killed at 15 wk (juvenile hens, JH) and 19 wk (laying hens, LH), and LHs were killed within 3 h after oviposition. Differentially expressed genes and metabolites in tibia were analyzed based on transcriptome and metabolome, and then combined to construct the relevant metabolisms and hub genes. In the LH hens, plasma levels of estrogen and tartrate-resistant acid phosphatase were significantly elevated by 1.7 and 1.3 times. In addition, the midpoint diameter, bone mineral density and bone mineral content of the tibia and femur were higher at 19 wk of age. A total of 580 differentially expressed genes were found between the JH and LH group in the tibia, including 280 up-regulated, and 300 down-regulated genes in the LH group. Gene set enrichment analysis (GSEA) showed that the intracellular biosynthesis and secretion of matrix vesicles were significantly enrichment in the LH hens. A total of 21 differential metabolites were identified between JH and LH group. Estradiol valerate positively correlated with L-theanine, tryptophan betaine, dopamine, and perindopril. Joint analysis showed that the top 20 hub genes were enriched in cholesterol biosynthesis and phospholipid metabolism, which played a key regulatory role in bone metabolism during pre- and postsexual maturity. These results provide a theoretical foundation for maintaining efficient egg production and reducing bone health problems in laying hens.

INTRODUCTION

The skeletal structure of female avian species consists of cortical bone, trabecular bone and medullary bone (Benavides-Reyes et al., 2021). Cortical bone and trabecular bone constitute the primary structural components of the skeleton, while medullary bone, a specialized woven bone found within the medullary cavity of avian bones, is a highly unstable secondary bone that develops in sexually mature female birds in response to estrogen and androgen hormones in the bloodstream (Fleming et al., 1998). Circulating estrogen levels rapidly increase with the onset of sexual maturity until laying eggs, and remain high during the peak egg-laying period (Whitehead and Fleming, 2000; Beck and Hansen, 2004).

The laying hens exhibit a distinct bone metabolism pattern, with medullary bone serving as a “calcium store” during the eggshell formation period. Medullary bone undergoes turnover on a daily basis, approximately 30 to 40% of the calcium required for eggshell formation is supplied by the resorption of medullary. Medullary bone forms rapidly during the period of inactive eggshell mineralization and when hens consume a calcium-enriched feed after laying (Wilson and Duff, 1990). Hence, the bone resorption by osteoclasts as well as bone formation by osteoblasts undergoes periodically with the progress of oviposition (Whitehead, 2004).

In the 24-h egg-laying cycle of chickens and quails, the number of osteoclasts remains unchanged. However, morphological and ultrastructural studies indicate significant changes in the structure of osteoclasts (Kerschnitzki et al., 2014). Bone resorption of osteoclasts is accompanied by continuous cytoskeletal rearrangement and proceeds through multiple steps, including attachment to the bone surface, remodeling of the cytoskeletal structure, polarization of the membrane, and trafficking of vesicles (Lakkakorpi and Väänänen, 1996; Søe, 2020).

Extending the production cycle of laying hens and improving eggshell quality are future breeding objectives. As the age of high-production laying hens increases, factors such as egg rate, eggshell quality, and bone density decrease, becoming the primary limitations for the extended maintenance of laying hens in commercial egg production (Rufener et al., 2019; Wei et al., 2020). Recent studies have shown that the mineralization of medullary bone during the sexual maturity period is a primary factor influencing bone quality in the later stages of egg production (Alfonso-Carrillo et al., 2021; Dunn et al., 2021). Therefore, medullary bone mineralization pre-sexual maturity plays a crucial role in the occurrence of osteoporosis in laying hens and the biomechanics of bones in the late stages of egg production. The aim of our study was to examine the bone quality and plasma biochemical indicators in sexual immaturity and sexual maturity hens, and combine transcriptome and metabolome to explore the mechanism of bone metabolism in laying hens pre- and postsexual maturity.

MATERIALS AND METHODS

Ethics Statement

All the following experimental procedures were approved by the Animal Ethics Committee of Hebei Agricultural University (University Identification Number: 2022115), including animal use and welfare.

Animals and Experimental Design

A total of 50 Hy-line Sonia layers were selected at 13-wk-old and were housed individually in the conventional cages (40 cm × 38 cm × 35 cm) of a 3-tier system in the animal house of Hebei Agricultural University. The birds had ad libitum access to water and diets and were kept under identical conditions of temperature (20 ± 2) °C. The nutrient levels of different ages were as described in our previous studies (Yue et al., 2023). The light cycle during the breeding period is based on Hy-line Sonia's management guidelines. Eight breeding hens of uniform body weight were randomly selected at 15 (juvenile hens, JH) and 19 (laying hens, LH) weeks of age, respectively, and the blood was collected from brachial wing vein. The bone from LH hens were collected within 3 h after oviposition. The left tibia, femur and humerus were preserved at 4 °C for phenotypic analysis, and the right tibia was immediately frozen in liquid nitrogen for RNA extraction and metabolites detection.

Bone Properties

The collected left skeleton excluding muscle and connective tissue was measured by electronic balance for bone weight (accuracy 0.01 g), and vernier calipers (accuracy 0.1 mm) were used to determine bone length, midpoint diameter and cortical bone thickness. Cortical bone thickness was taken as the average of 3 measurements at the location of the midpoint bone fracture.

Bone mineral density (BMD) and bone mineral content (BMC) were analyzed using a small animal dual-energy X-ray bone mineral density and body composition analyzer (iNsight Vet DXA, OsteoSys, Korea). Bones were scanned individually, and the area containing cortical and medullary bone was set as the region of interest. The measurement time was 25 s, and each result was analyzed using Insight software (version 1.0.6).

A 3-point bending test was used to determine bone fracture strength by placing each bone on 2 pivot points (with a 4.8 cm span for the humerus, 6.0 cm for the femur, and 6.4 cm for the tibia) and applying a constant force using a masseter with a 30 kg load cell (TA.XT Plus, Surrey, UK) until the bone fractured. Bone fracture strength (g) was read by connecting the software to the peak of the curve, which was calibrated appropriately and then corrected using the corresponding chicken live weight.

Hematological Measurements

The collected blood samples were centrifuged at 3, 000 r/min for 15 min. The serum was separated and stored at −20 °C for analysis. Estrogen (E₂), alkaline phosphatase (ALP), bone gamma-carboxyglutamic-acid-containing proteins (BGP), 1,25-dihydroxy vitamin D (1,25-(OH)₂D₃) and anti-tartaric acid phosphatase (TRAP) concentrations were measured with a commercial enzyme immunoassay kit (Shanghai Jianglai Biotechnology Co., Ltd., Shanghai, China) according to the instructions of the manufacturer. The concentrations of serum Ca and P were analyzed by Ca and P_i detection kit assay (Nanjing Jiancheng Bioengineering Institute, Nanjing, China) using a microplate reader.

RNA Extraction and Sequencing of Tibia

Total RNA was extracted from laying hen tibial tissue using TRIzol Plus RNA Purification Kit (Invitrogen, Waltham, MA, 12183555) following the manufacturer's instructions. Both the concentration and purity of total RNA were estimated using the Qubit RNA Assay Kit (Invitrogen, Waltham, MA, Q10211) in a Qubit 2.0 Fluorometer (Life Technologies, Carlsbad, CA) and a NanoPhotometer spectrophotometer (IMPLEN, Westlake Village, CA), accordingly. Furthermore, RNA integrity was evaluated via the RNA Nano ,6000 Assay Kit (Agilent Technologies, Santa Clara, CA, 5067-1511). cDNA libraries were constructed with reference to the protocol of the NEBNext Ultra RNA Library Prep Kit for Illumina (NEB, Ipswich, MA, E7530L), sequenced on the Illumina Hiseq platform and generated 150-bp paired-end reads.

RNA-seq Data Analysis

The clean reads were aligned against the Gallus gallus genome (Gallus_gallus-6a, GCA_000002315.5) using Hisat2 software. The Cuffquant and cuffnorm software (v2.2.1) were used to estimate the expression levels of all transcripts and to analyze mRNA expression levels by calculating fragments per kilobase of exon per million fragments mapped (FPKM). The false discovery rate (FDR) was controlled using the Benjamini and Hochberg correction method. The resulting P values were corrected and differentially expressed genes (DEGs) were screened between the 2 groups using the DESeq2 R package when |log2(Fold Change)| ≥ 1.0 and FDR < 0.01.

Six DEGs were randomly selected to validate the RNA-seq data by qRT-PCR. The information of qRT-PCR primers for 5-Hydroxytryptamine Receptor 1B (HTR1B), 5-Hydroxytryptamine Receptor 2A (HTR2A), G Protein Subunit Gamma 5 (GNG5), Pentraxin 3 (PTX3), ETS Proto-Oncogene 2 (ETS2) and HRas Proto-Oncogene, GTPase (HRAS) have been shown in Table S1. The reaction system and steps of qRT-PCR are consistent with our previous study (Yue et al., 2023). The expression level of each tested gene was normalized to the expression level of LBR and quantification of relative transcript levels between different samples using the 2^−ΔΔCt method.

Functional and Gene Set Enrichment Analysis of DEGs

Enrichment analysis of the DEGs was performed to assess their biological significance and the signaling pathways involved in regulation. GOseq R package and KOBAS software were used to enrich DEGs for GO function and KEGG signaling pathway, respectively, and significant enrichment was considered at P < 0.05. Gene Set Enrichment Analysis (GSEA) and visualization were performed using clusterProfiler and enrichplot R packages. Significant enrichment was determined based on P_adj < 0.05 and |NES|>1.5, providing insights into the functional enrichment of gene sets associated with DEGs.

Liquid Chromatograph-Mass Spectrometer Analysis

Tibia samples was ground separately in liquid nitrogen and subsequently resuspended in precooled methanol/acetonitrile/water solution (2:2:1, v/v), vortexed and mixed, sonicated at low temperature for 30 min, left at -20 °C for 10 min, centrifuged at 14,000 g for 20 min at 4 °C, the supernatant was dried under vacuum and added to 100 μL aqueous acetonitrile solution (acetonitrile: water = 1:1, v/v) for mass spectrometry analysis, vortexed, centrifuged at 14,000 g for 15 min at 4°C, and the supernatant was taken into the sample for analysis.

Metabolic profile of samples in positive and negative electrospray ionization (ESI) mode using an Agilent 1,290 Infinity LC ultra performance liquid chromatography UHPLC system (Agilent, Santa Clara, CA) and an AB Triple TOF 6,600 mass spectrometer (SCIEX, Washington, DC, USA). Chromatographic separations were performed in a binary solvent system (solvent A: water +25 mM ammonium acetate +25 mM ammonia; solvent B: acetonitrile). The gradient was 95% for 0.5 min, decreasing linearly to 65% in 6.5 min, then to 40% in 1 min and maintained for 1 min. After that, the linearity increased to 95% in 0.1 min and maintained for a re-equilibration period of 3 min. The entire analysis was performed with samples in a 4°C autosampler.

We used Compound Discoverer 3.0 (CD 3.0, Thermo Fisher, Waltham, MA) to match peaks, take peaks, and quantify each metabolite from the raw data files obtained by UPLC-MS/MS. After converting the raw data to mzXML format, XCMS software was used to perform peak alignment, retention time correction and extraction of peak areas. Principal component analysis (PCA) and orthogonal least partial squares discriminant analysis (OPLS-DA) were used to determine the magnitude of differences between samples. The selection of differential metabolites (DM)was determined based on the variable weight value (VIP) and the P-value of Student's t test, and metabolites with VIP ≥ 1 and P < 0.05 were considered as significantly DMs.

Combined Transcriptome and Metabolome Analysis and Hub Gene Identification

We carried out joint analysis on DEGs and DMs to construct transcript-metabolite networks using gene-metabolite networks with a Pearson correlation coefficient > 0.7. Network diagram was drawn using the r package igraph and modular analyses were performed using the Ghphi (0.10) software. We utilized the CytoHubba plugin within Cytoscape (3.7.0) software to compute key metrics for gene importance across the gene regulatory network, employing a range of algorithms. These algorithms encompassed various node centrality metrics (degree centrality, MCC centrality, DMNC centrality, bottleneck centrality, betweenness centrality, closeness centrality, and radiality centrality), subgraph metrics (MNC and EPC), as well as node attributes and subgraph attributes (stress, clustering coefficient, and eccentricity). Subsequently, we conducted a comprehensive ranking and statical assessment of the genes using the COINr6 package in the R software. Ultimately, the top 20 genes were selected hub genes.

Statistical Analysis

The overall differences between the JH and LH groups on bone quality and plasma biochemical parameters were analyzed using Student's t test. Differences were considered significant at P < 0.05 and highly significant at P < 0.01.

RESULTS

Differences in Bone Quality of Hens Between Stages of Pre- and Postsexual Maturity

Table 1 showed the differences in bone quality parameters of laying hens from pre- and postsexual maturity. Compared with the JH group, the increased midpoint diameter, BMD and BMC of tibia and femur of laying hens in the LH group were observed (P < 0.05), while LH group hens decreased humeral bone weight and corrected breaking strength (P < 0.05). However, there were no significant differences in other bone quality parameters between the 2 groups (P > 0.05).

Table 1.

Comparison of bone quality parameters in laying hens between JH and LH groups.

Items	Tibia		Femur		Humerus
	JH	LH	JH	LH	JH	LH
Bone weight /g	7.55±1.05	7.51±0.93	6.28±0.70	6.67±0.79	3.28±0.28a	2.51±0.57b
Bone length /mm	114.52±5.38	116.64±4.61	82.10±2.44	81.66±3.68	77.33±1.50	76.54±2.12
Midpoint diameter/mm	6.48±0.52b	7.11±0.47a	7.29±0.25b	7.86±0.31a	7.39±0.39	7.61±0.31
Cortical bone thickness /mm	0.58±0.06	0.65±0.08	0.53±0.05	0.56±0.08	0.41±0.03	0.44±0.05
Bone breaking strength /(g/g)	15.53±2.60	11.83±2.00	13.48±1.44	11.21±1.70	12.52±1.96a	9.43±1.49b
BMD/(g/cm2)	0.19±0.01b	0.22±0.02a	0.16±0.01b	0.20±0.03a	0.12±0.00	0.13±0.02

BMC/(g) 1.18 ± 0.20^B 2.40 ± 0.29^A 1.24 ± 0.17^B 1.75 ± 0.20^A 0.09 ± 0.01^b 1.16 ± 0.21^a.

Differences in Serum Biochemistry of Hens Between Stages of Pre- and Postsexual Maturity

Table 2 showed the differences in serum biochemical indices of laying hens from pre- and postsexual maturity. The serum concentrations of E₂, TRAP, and Ca were significantly higher in the LH group than that in the JH group (P < 0.01), whereas the serum concentration of 1,25(OH)₂D₃ and P_i levels were lower in the LH group (P < 0.01). No significant difference in serum concentrations of ALP and BGP were observed between the 2 groups.

Table 2.

Comparison of serum biochemical indicators in laying hens between JH and LH groups.

Items	JH	LH
E2 (pg/mL)	236.62±39.60B	407.00±31.51A
ALP (ng/mL)	99.07±19.04	101.28±16.82
BGP (ng/mL)	9.64±1.43	9.25±1.07
1,25(OH)2D3 (ng/mL)	33.29±6.02A	28.13±4.14B
TRAP (pg/mL)	339.29±85.61B	439.09±97.06A
Pi (mmol/L)	1.65±0.40A	1.00±0.38B
Ca (mmol/L)	1.23±0.12B	1.58±0.11A

Analysis of DEG

Six cDNA libraries were constructed for the tibia samples from pre- and postsexual maturity laying hens, obtaining 128 million clean reads numbers and 40.34 G clean base numbers, sequencing with GC content higher than 51.08%. The average Q30 quality data were 92.51% and 93.96% for the JH and LH groups, respectively (Table S1). A total of 580 DEGs were identified in the LH group compared with the JH group (|log2(Fold Change)|≥ 1 and FDR < 0.01 as the screening criteria), including 280 up-regulated genes and 300 down-regulated genes (Figures 1A and 1B). To validate the expression profiles obtained in the Illumina sequencing analysis, 6 DEGs were selected for qRT-PCR analysis using the same RNA samples. The results showed that the expression patterns of all 6 DEGs were similar to Illumina's sequencing results, representing a high reliability of the data (Figures S2 and S3). The top 20 GO terms and KEGG pathways that most likely affect the skeletal metabolism of laying hens pre- and postsexual maturity were shown in Figures S4 A and B.

Figure 1.

Transcriptome analysis of tibial tissue in laying hens between JH and LH groups. (A) The volcano plot for the genes between JH and LH groups. Red represents significantly up-regulated differential genes, green indicates significantly down-regulated differential genes. (B) A clustering heat map describing differential genes in the tibial tissue between the 2 groups. The color represents the value after the normalized transformation (scale number) of the expression level log (FPKM+1) of the gene in the sample.

Gene Set Enrichment Analysis of DEGs

Gene set enrichment analysis-based GO analysis of the overall trends in gene expression during bone mineralization in sexually mature chickens revealed significant upregulation of terms related to vesicles, intracellular vesicles, early endosomes, endosomes, endosome membranes, intracellular transport, vesicle-mediated transport, and exocytosis in the tibia. Conversely, terms related to extracellular matrix organization, glycosaminoglycan binding, collagen-containing extracellular matrix, and extracellular matrix were significantly downregulated (Figure 2).

Figure 2.

GSEA-based GO enrichment plot of candidate gene sets. (A) The gene sets involved in intracellular matrix vesicle biosynthesis. (B) The gene sets involved in matrix vesicle secretion. (C) The gene sets related to the extracellular matrix mineralization.

The GSEA-based KEGG analysis results showed that the normalized enrichment scores (NES) for the following signaling pathways were positive: steroid biosynthesis, oxidative phosphorylation, lysosome, phagosome, apoptosis, endocytosis, SNARE interactions in vesicular transport, and glycosaminoglycan degradation. Conversely, the calcium signaling pathway had a negative NES value (Figure 3).

Figure 3.

GSEA-based KEGG enrichment plot of candidate gene sets.

DEGs Associated With Steroid Biosynthesis, Energy Metabolism, and Osteoclast Differentiation

The steroid biosynthesis and oxidative phosphorylation were the most representative pathways. To investigate the potential regulatory mechanisms involved in bone metabolism in pre- and postsexual maturity laying hens, we analyzed the expression patterns of various genes involved in steroid biosynthesis energy metabolism, and osteoblast differentiation. A total of 19 DEGs encoding steroid biosynthesis were identified in tibia samples and were upregulated in the LH group compared to the JH group (Figure 4A). Steroid and terpenoid skeletal biosynthetic pathways can produce cholesterol, which is a precursor of sex hormones including E₂.

Figure 4.

The DEGs related to steroid biosynthesis (A), energy metabolism (B), and osteoclast differentiation (C) in the tibia pre- and postsexual maturity hens.

Moreover, we found that both oxidative phosphorylation and pentose phosphate pathways are involved in the energy metabolism in sexually mature laying hens (Figure 4B). Compared with the JH group, the DEGs of mitochondrial oxidative phosphorylation complex IV (COX6C and COX7A2) were significantly upregulated, whereas those associated with complexes I, III, and V were significantly downregulated in the LH group. The DEGs related to osteoclast differentiation are higher in sexual maturity hens than sexual immaturity hens (Figure 4C). These results suggest that higher consumption of ATP after sexual maturity, resulting in increased activity of complex IV to produce more ATP for bone remodeling.

Identification of Differential Metabolites

In this study, metabolomics analyses were performed on tibia samples from pre- and postsexual maturity laying hens to detect overall biochemical changes (n = 5). The metabolic profiles obtained from LC-MS were plotted using PCA and the results showed that samples from the JH and LH groups were distinguishable in both positive and negative modes and QC samples were all highly aggregated, indicating that the analytical platform has some stability and that significant differences originated from between-group differences rather than instrumentation (Figures S1A and B). Moreover, a clear separation of metabolites between the JH and LH groups was observed in the positive model (Figure S1C) and the negative model (Figure S1D) by a more complex OPLS-DA analysis, with the permutation test confirming the accuracy of OPLS-DA models; the R² and Q² intercepts in the positive and negative modes reached 0.9122, -0.116 and 0.9884, 0.1984, respectively (Figures S1E and F).

A total of 21 significantly different metabolites (5 negative model and 16 positive model) were identified by setting thresholds VIP > 1and P-value < 0.05 (Figures 5A and 5B, and Table S3). Compared with the JH group, 17 DMs were up-regulated in the LH group, while 4 DMs were down-regulated. Overall, the DMs belonged mainly to lipids and lipid-like molecules (25.52%), organic acids and derivatives (22.71%), organic heterocyclic compounds (13.47%), and benzenes (10.19%) (Figure 5C). These results suggest that sexual maturity caused changes in lipid metabolism and organic compounds in the tibia of laying hens.

Figure 5.

Metabolome analysis of tibial tissue in laying hens between JH and LH groups. (A) The volcano plot for the differential metabolite expression in the negative model between JH and LH groups. Red indicates an increase in metabolite content, blue indicates a decrease in metabolite content, and gray indicates no significant difference. (B) The volcano plot for the differential metabolite expression in the positive model between JH and LH groups. Red indicates an increase in metabolite content, blue indicates a decrease in metabolite content, and gray indicates no significant difference. (C) Classification of differential metabolites in the HMDB database. (D) Heat map of annotated differential metabolites. Each line means a metabolite and each column a sample. JH1–JH5 represent replicates in the presexual group and LH1–LH5 in the postsexual group. The upregulated metabolites are shown in red color, whereas the downregulated metabolites are presented in blue color.

Metabolites Associated With Tibia Parameters

Heatmap analysis of DMs in each sample between JH and LH groups is shown in Figure 5D. The results of correlation analysis among the annotated DMs showed that β-estradiol 17-valerate strongly correlated with L-theanine, tryptophan betaine, perindopril, L-serine, and dopamine (r > 0.80, P < 0.01) (Figure 6A).

Figure 6.

Analysis of annotated differential metabolites in tibial tissues from laying hens between the JH and LH groups. (A) Heatmap of correlation coefficient matrix between annotated differential metabolites. Strong correlations are indicated by large circles, whereas small circles indicate weak correlations. The red indicates positive correlation and blue indicates negative correlation. (B) Heatmap of correlation coefficient matrix between annotated differential metabolites and tibia parameter. The red indicates a positive correlation, and the blue shows a negative correlation.

Spearman rank correlation analysis was performed to investigate correlation coefficients between DMs with tibia parameters (Figure 6B). A total of 18 DMs were associated with 3 tibia parameters, respectively. Specifically, perindopril, L-theanine, pyridine, pheniramine, piperazine-1-ethanesulfonic acid, β-estradiol 17-valerate, dopamine, and D-xylose were negatively associated with tibia breaking strength (r < -0.75, P < 0.01). However, there was a positive relation between N-methyl-n-(tetrahydro-2-furanylmethyl)-4-piperidinamine and tibia breaking strength (r = 0.83, P = 0.0029).

Relationship of DEGs and DMs in Tibia

To demonstrate the correlation of DEGs and DMs, we constructed a gene-metabolite interaction network to analyze the different multiplicity of DEGs and DMs. Among them, DEGs and DMs with Pearson correlation coefficients greater than 0.7 were clustered into 4 categories. The metabolites in cluster 1 were D-xylose and Pheniramine, cluster 2 contained L-theanine, Pro-leu and Piperazing-1-ethanesulfonic acid, cluster 3 had Sucrose, N-acetyl-d-glucosamine, trigonelline, DL-serine, PE(P-16:0/20:4(5Z,8Z,11Z,14Z)), 2′-deoxuctidine, Dopamine, Perindopril, β-estradiol 17-valerate, and cluster 4 contained Gramine, 2-piperidone, N-methyl-n-(tetrahydro-2furanylmethyl)-4-piperidinamine, Tryptophan betaine, DL-tyrosine, Pyridine, and Erucamide (Figure 7A). Based on the values of COINr6 package, the top 20 hub genes were identified and the transcript levels of hub genes were remarkably upregulated in LH hens except the PLCB4 and MYH11 (Figure 7B and Table S4).

Figure 7.

Integrating transcriptome and metabolome analysis in tibial tissues from laying hens between the JH and LH groups. (A) The gene-metabolite interaction network with 4 clusters. The circle with annotation represents the DMs, the circle without annotation represents the DEGs. (B) The heatmap of the top 20 hub genes. The transition from blue to red strips represents an increase in the gene expression levels.

DISCUSSION

In this study, as an effort to elucidate the regulation of tibia turnover metabolism in hens involved in pre- and postsexual maturity, the plasma estrogen increased, and the bone quality indicators, including the midpoint diameter, BMD and BMC were enhanced in postsexual maturity hens compared to the pre-sexual hens. A total of 580 DEGs were identified by tibia transcriptome, and 21 DMs by metabolomic analysis. A correlation test was performed in DEGs and DMs, and 20 hub genes were revealed, most of which were related to cholesterol biosynthesis and phospholipid metabolism.

Circulating estrogen levels rapidly increase with the onset of sexual maturity until laying eggs, and remain high during the peak egg-laying period (Whitehead and Fleming, 2000; Beck and Hansen, 2004). For females, during the early stages of puberty, low levels of estrogen contribute to rapid bone growth, while towards the end of puberty, elevated estrogen levels lead to the closure of growth plate (Lara-Castillo, 2021). This study analyzed the changes in the bone quality of laying hens pre- and postsexual maturity and found that the midpoint diameter of the tibia and femur in the LH group increased significantly, while the bone weight and breaking strength of the humerus decreased significantly, which means the growth of the humerus and tibia significantly affected by different stages of reproduction. During laying, bone width affects bone health by increasing bone breaking strength, as wider bones are associated with higher bending forces (Rauch, 2007). Although the phenotypic correlation of BMD with body weight and age at first egg is low, it is still indicated that larger birds with later sexual maturity have higher bone density (Podisi et al., 2012). The tibia and humerus of avian system hens had more cortical bone mass prior to lay and continued throughout the laying period, implying that good bone quality in pre-sexual maturity hens contributes to bone health in the later stage of hens (Regmi et al., 2017). The mineral content of medullary bone is relatively high during oviposition and further increased at the stage of 6 h after oviposition. In the later stage (12 h and 18 h after oviposition), the mineralization of the medullary bone decreases dramatically (Kerschnitzki et al., 2014). In our study, the samples were collected within 3 h of oviposition in sexual maturity hens, which means that the medullary bone is in a stage of increased mineralization.

The extracellular matrix crystallization process initiates from the inner leaflet of MV membranes and requires an adequate supply of inorganic phosphate to commence. Alkaline phosphatase (ALP) is a hydrolase that can decompose phosphorus from β-glycerol phosphate into free mineral elements. Bone galactosin (BGP) combines with Ca²⁺ in the extracellular matrix to form calcification. Both are involved in bone formation and mineralization processes (Li et al., 2014; Qin et al., 2014). The unchanged levels of ALP and BGP within 3 h of oviposition in hens indicated that extracellular matrix mineralization has not yet commenced, and bone mineralization is still in its early stage. Tartrate-resistant acid phosphatase (TRAP), as a bone resorption marker, promotes osteoclast formation and function by modulating the activity of extracellular acidifying enzymes (Hayman, 2008). The higher level of plasma TRAP in LH hens indicated the enhanced formation and differentiation of osteoclasts at sexual maturity in layer hens.

Mineralization involves the deposition of calcium phosphate crystals from matrix vesicle (MV) into the extracellular matrix and primarily encompasses processes such as intracellular MV biogenesis, MV secretion, and extracellular crystallization (Iwayama et al., 2022). Intracellular MV biogenesis is mediated through the interaction between mitochondria and lysosomes, although the exact nature of their interaction remains unclear (Pei et al., 2018). Gene set enrichment analysis enrichment analysis revealed upregulation of GO terms or KEGG pathways related to vesicles, intracellular vesicles, lysosome, phagosome, intracellular transport, and vesicle-mediated transport in the tibia of sexually mature hens. The results suggests that the mineralization status of the tibia is in the intracellular MV biogenesis phase during ovulation within 3 h laying hens.

The potential secretion mechanism of MV could be endocytosis-exocytosis or budding from the plasma membrane, with predictions of involvement of the endosomal and autophagy pathways (Shapiro et al., 2015; Iwayama et al., 2019). Gene set enrichment analysis enrichment analysis revealed that, GO term and KEGG pathways involved in exocytosis, endocytosis, early endosomes, endosomes, endosome membranes, apoptosis, and SNARE interactions in vesicular transport are all upregulated in the tibia in sexually mature hens, indicating that a preference for intracellular MV being secreted into the ECM through endocytosis-exocytosis.

Mitochondrial activity plays a crucial role in the differentiation of osteoblasts. Mesenchymal progenitor cells activate oxidative phosphorylation (OXPHOS) during osteogenic differentiation, while maintaining a glycolytic profile similar to undifferentiated cells (Shum et al., 2015). Recently, the importance of OXPHOS in in vivo osteoblast differentiation has been confirmed. Targeted deletion of the evolutionarily conserved signaling intermediate in Toll pathways (ECSIT), a core subunit of mitochondrial complex I, in skeletal progenitor cells has been shown to result in skeletal deformities, weakness, and brittle bone fractures in mice (Lin et al., 2022). Furthermore, research indicates that estrogen reduces the number of osteoclasts by attenuating mitochondrial oxidative phosphorylation and ATP generation, as well as by stimulating mitochondrial apoptosis in early osteoclast precursors rather than mature osteoclasts (Kim et al., 2020).

β-estradiol 17-valerate is a naturally or synthetically produced steroid which is rapidly metabolized to 17β-estradiol and valeric acid. The hub genes obtained through the integrated transcriptome and metabolome are associated with cholesterol synthesis, which serves as a substrate for estrogen synthesis. Mineralization is the deposition of calcium phosphate crystals from MV into the extracellular matrix. Phospholipids, containing phosphate groups, serve as precursors for hydroxyapatite (HA) formation, and their hydrolysis products can function as a reservoir of phosphate to maintain mineralization. The MV membranes exhibit high concentrations of phosphatidylethanolamine (PE) and phosphatidylcholine (PC), serving as reservoirs for both classes of phospholipids during the initial phases of mineralization (Wuthier, 2011). The levels of PE and PC decline as mineralization advances (Wu et al., 2002). Phosphatidylethanolamine levels increase significantly at later stages of osteoclast differentiation, when mononuclear osteoclasts undergo intercellular fusion to produce multinucleated mature osteoclasts. (Irie et al., 2017). Phosphatidylethanolamine is converted to PC by 3 successive methylation reactions catalyzed by phosphatidylethanolamine N-methyltransferase (PEMT) using S-adenosylmethionine as the methyl-group donor. Estrogen has been identified as positive activator of transcription of the PEMT gene (Resseguie et al., 2007). Metabolomic results reveal a significant decrease in PE content in the tibia of LH hens, indicating that the tibia is undergoing a mineralization phase at this time.

Phospholipase C (PLC) cleaves the polar head phosphate form phospholipids, producing diacylglycerol. A potential role of PLC in MV is the generation of phosphoethanolamine and phosphocholine, which can be further hydrolyzed by PHOSPHO1 and TNAP, leading to the production of the P species essential for MV-mediated mineralization (Roberts et al., 2007). Research indicates that mutations in the binding site of RUNX2 within the phospholipase C beta 4 (PLCB4) promoter region are associated with osteoporosis (Tsai et al., 2021).Fibroblast growth factor (FGF23), a member of the fibroblast growth factor family, circulates as an endocrine regulator of phosphate and calcium (Ca²⁺) homeostasis. FGF23 has been shown to phosphorylate PLC and activate the Ca²⁺/calcineurin/NFAT and PLC/IP3 signaling pathways, ultimately enhancing cardiac fibroblast proliferation and migration (Grabner et al., 2015; Lee et al., 2022). The increased level of β-estrogen 17-valerate in tibia of LH hens promotes phospholipid metabolism and hence MV-mediated mineralization.

CONCLUSIONS

These data suggested that elevated plasma E₂ and TRAP levels indicate enhanced osteoclast differentiation in sexual maturity hens. The midpoint diameter, mineral content, and density of the tibia and femur improved after sexual maturity. In sexual maturity hens, steroid biosynthesis, oxidative phosphorylation, extracellular matrix mineralization, and osteoclast differentiation were increased in the tibia within 3 h of oviposition. In addition, the joint analysis results indicated that cholesterol biosynthesis and phospholipid metabolism were involved in matrix vesicle-mediated bone mineralization at the early oviposition stage.