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. 2024 Apr 4;103(6):103703. doi: 10.1016/j.psj.2024.103703

Melatonin alleviates endoplasmic reticulum stress to improve ovarian function by regulating the mTOR pathway in aged laying hens

Er-ying Hao *,1, Xue-lu Liu *,1, Li-yun Chang †, Han Xue *, Bo-fei Su *, Yi-fan Chen *, De-he Wang *, Lei Shi *, Hui Chen *,2
PMCID: PMC11040121  PMID: 38631228

Abstract

Granular cell apoptosis is a key factor leading to follicular atresia and decreased laying rate in aged laying hens. Endoplasmic reticulum stress (ERS) induced cell apoptosis is a new type of apoptosis pathway. Previous studies have shown that the ERS pathway is involved in the regulation of follicular development and atresia, and can be regulated by mTOR. Melatonin (MEL) can protect the normal development of follicles, but the precise mechanism by which MEL regulates follicular development is not yet clear. So, we investigated the potential relationship between MEL and ERS and mTOR signaling pathway in vivo through intraperitoneal injection of MEL in aged laying hens. The results show that the laying rate, ovarian follicle number, plasma MEL, E2, LH, FSH concentrations, as well as the mRNA expression of mTOR signaling-associated genes TSC1, TSC2, mTOR, 4E-BP1, and S6K in old later-period chicken control (Old-CN) group was significantly decreased (P < 0.01). In contrast, the ERS-related of plasma and granular cell layer mRNA expression of Grp78, CHOP, and Caspase-3 was significantly increased (P < 0.01). While both of the effects were reversed by MEL. Then, aging granulosa cells were treated with MEL in vitro, followed by RNA seq analysis, and it was found that 259 and 322 genes were upregulated and downregulated. After performing GO enrichment analysis, it was found that DEGs significantly contribute to the biological processes including cell growth and apoptosis. Using pathway enrichment analysis, we found significant overrepresentation of cellular processes related to mTOR signaling and endoplasmic reticulum (ER) stress, involving genes such as GRB10, SGK1, PRKCA, RPS6KA2, RAF1, PIK3R3, FOXO1, DERL3, HMOX1, TLR7, VAMP7 and INSIG2. The obtained results of RT-PCR showed consistency with the RNA-Seq data. In summary, the underlined results revealed that MEL has significantly contributed to follicular development via activating the mTOR signaling pathway-related genes and alleviating ERS-related genes in laying hens. The current study provides a theoretical background for enhancing the egg-laying capability of hens and also providing a basis for elucidating the molecular mechanism of follicular selection.

Key words: chicken, melatonin, transcriptome analysis, granulosa cell, mTOR signaling pathway

INTRODUCTION

The ovaries of poultry take follicles as the basic functional unit. Granulosa cells are the main constituent of ovarian follicles that are surrounding the oocyte in single or multiple layers and have a significant contribution to the developmental process of follicles in birds as well as in mammalian. It has been shown that follicle selection and atresia depend on granulosa cell apoptosis (Nakayama et al., 2000; Manabe et al., 2004). Granulosa cell apoptosis leads to a decrease in the number of follicles capable of further development and is the main cause of the decline in egg production rate in poultry.

Melatonin (N-acetyl-5-methoxytryptamine. MEL) exists in the follicular fluid, and its concentration is closely related to the health of the follicle. Reported studies have been revealed that in laying hens the level of MEL in the blood reduces with increasing age. Exogenous MEL can considerably enhance the egg-laying rate in aged laying hens (Jia et al., 2016). With the atresia of follicles, the concentration of MEL decreases, so it is speculated that MEL may have a role in the regulation of the proliferation and apoptosis of follicles. Early studies focused on the ovary (Zhang et al., 2019), ovarian follicles (Yang et al., 2008), liver (Ding et al., 2008), and shell glands (Yang et al., 2007) which are associated with the variability of egg-laying in chickens, while research has confirmed that the steroid hormones are mainly synthesized via granulosa cell layers of the follicles (Huang et al., 1979). The MEL role in the growth and development of granular cells has been revealed but still, there is a lack of clarity regarding its mechanism.

Endoplasmic reticulum stress (ERS) is an important life phenomenon that is believed to be prevalent in the basic life processes of eukaryotic organisms (Lei et al., 2019; Ham et al., 2020). Various situations that lead to protein misfolding or nonfolding and abnormal protein accumulation can induce ERS, such as intracellular energy deficiency, calcium ion homeostasis imbalance, oxidative stress response, etc (Huang et al., 2013; Hasanain et al., 2015; Varshney et al., 2018; Aljohani et al., 2019). MEL alleviates cadmium induced ovulation dysfunction by inhibiting ERS and cell apoptosis (Yang et al., 2017), improves pig oocyte maturation (Park et al., 2018), slows down apoptosis of mouse testicular spermatogonia stem cells induced by white sulfur (Cui et al., 2017), inhibits apoptosis of rat cardiomyocytes (Xiong et al., 2018), and improves palmitic acid induced apoptosis of mouse granulosa cells (Chen et al., 2019).

Mammalian target of rapamycin (mTOR) can promote follicular development and maintain follicular function by reducing excessive consumption of the primordial follicle pool (Zhang et al., 2014; Chen et al., 2015). Meanwhile, mTOR signaling pathway involved in mediating ERS to regulate autophagy and apoptosis (Chang et al., 2018; Yang et al., 2018; Tian et al., 2019). MEL can inhibit the expression of ERS key genes pIRE1, ATF and apoptosis key genes CHOP, Caspase12 (Cui et al., 2017). It can also regulate the proliferation and development of mouse follicular cells by activating the mTOR signaling pathway and reduce the loss of primordial follicles (Behram et al., 2017). Therefore, exploring the effects of MEL on the expression patterns of genes related to the ERS and mTOR pathway in aged laying hens at the in vivo level and investigated MEL treated granulosa cells at the in vitro level, then followed by RNA sequencing technology to exploring the RNA expression level of granulosa cells in chicken under the MEL treatment. The obtained results established the basis to explore the mechanism through which MEL contributes to the growth and development of follicle and provided a theoretical basis for in-depth research on the establishment and extension of follicle grading and the utilization cycle of laying hens.

MATERIALS AND METHODS

Animals and Experimental Design

The birds were obtained from the Hebei Xing Rui Group (Baoding, China). The hens were divided into 3 groups: 1) Young peak-period chicken control group (Young-CN, n = 60) composed of 35-wk-old chickens; 2) Old later-period chicken control group (Old-CN, n = 60), composed of 70-wk-old chickens; 3) MEL group (MEL, n = 60), consisting of 70-wk-old chickens. Each group included 6 replicates. The study lasted 4 wk. All birds were maintained in individual cages. All animal protocols were in line with national requirements and approved by the Animal Use and Ethics Committee (Number: 2019010).

MEL Exposure

MEL (Sigma-Aldrich, St Louis, MO) was dissolved in absolute ethanol and then diluted with saline. The ethanol concentration in the final solutions was 10%. Daily intraperitoneal injections of MEL (20 mg/kg/d) were given to chickens in the MEL group over a period of 28 d. The same volume of 10% normal saline containing 10% ethanol was administered to the other 2 groups.

Laying Performance

The total egg number, feed intake, feed consumption, and egg weight were recorded daily, and the laying rate (LR) and average egg weight (AEW) were calculated daily. The FER-feed-egg ratio (FER) and average daily feed intake (ADFI) were measured every 2 wk.

Egg Quality

Ten eggs were collected from each replicate and the egg quality was evaluated at the end of the test. The shape of the eggs was measured using an egg shape determinator (FHK, Fujihira Industry Co., Ltd., Tokyo, Japan), and the egg weights (EW) were measured on a digital scale (Huachaoice Electrical Appliances, Shanghai, China). Eggshell thickness (EST) was measured using an echo-meter (ESTG-1; ORKA. Israel) and an Egg Force Reader (ESTG-01, ORKA) was used to test the eggshell strength (ESS). The Haugh unit (HU) and albumen height (AH) were assessed using an Egg Analyzer (EMT-7300II, Robotmation Co., Ltd., Japan).

Measurement of Plasma Levels of Reproductive Hormones

Blood samples were collected from 12 randomly selected chickens from each group at the end of the test period. The blood was centrifuged for 15 min at 3,000 rpm and the plasma was collected and stored at -20°C. Commercial ELISA kits were used to measure the plasma concentrations of MEL, estradiol (E2), follicle-stimulating hormone (FSH), and luteinizing hormone (LH) (Jiang Lai Biotechnology Co., Ltd., Shanghai, China).

Determination of Ovarian Follicle Numbers

At the end of the experiment, 12 chickens were randomly selected from each group and sacrificed. The ovaries were removed and the number and size (diameter) were recorded. The follicle types were classified as follows: 1) preovulatory follicle (POF), in which the diameter was greater than 10 mm; 2) small yellow follicle (SYF), in which the diameter was between 8 and 10 mm; 3) large white follicle (LWF) with diameters between 6 and 8 mm; 4) medium white follicle (MWF), with a diameter of 4-6 mm; 5) small white follicle (SWF), with a diameter of 2-4 mm; 6) primary follicle (PF), in which the diameter was less than 2 mm (Johnson 2015).

Granulosa Cell Culture and MEL Treatments

The laying hens of Old-CN were anesthetized with sodium pentobarbital, and 6 to 8 mm SYFs were separated from the ovary, followed by washing via PBS (ice-cold). The membranous layer was also separated from the layer of granular cells. The membranous layer was then washed via PBS. Next, the cell digestion was carried out via treating with 2 mL (0.25%) Trypsin and EDTA solution for 2.5 min at 37°C. The cell digestion was terminated by M199 medium (2 mL), comprised of 1 percent penicillin-streptomycin and 10 percent FBS. The obtained cells of first-generation were exposed to M199 medium comprised of 0 (CON) and 200 μM/L of MEL (MT, Sigma Aldrich) for 2 d.

Total RNA Extraction, cDNA Library Preparation, and Sequencing

Six cDNA libraries sequences were obtained from MEL and CON groups sequencing analysis. Total RNA extraction was carried out from granulosa cells collected from both MEL and CON groups. The Qubit RNA Assay Kit in Qubit 2.0 Fluorometer (Life Technologies) was employed for the RNA quantitative analysis. The RNA purity was checked via NanoPhotometer (IMPLEN, CA). The RNA Nano 6000 Assay Kit (Bioanalyzer 2100 system) was employed for the evaluation of the integrity of RNA. At Novogene Bioinformatics Institute, mRNA selection, preparation of library, and sequencing were carried out via Illumina HiSeqTM2000/MiseqTM sequencer, as suggested by the manufacturer.

Differential Expression Analysis

Clean reads were attained via the removal of reads, comprising an adapter, ploy-N, as well as poor quality, reads from raw reads. The processed reads were mapped to the G. gallus genome using TopHat software. Cuffnorm and cuffquant, (both of the same versions i.e., Ver-2.2.1) were employed for the calculation of FPKMs of genes present in each sample. DESeq R package was employed for the analysis of the differential expression of 2 groups (3 replicates for each group). The Benjamini and Hochberg's method was applied for the adjustment of obtained P-values to minimize the rate of incorrect data. Genes, having FDR smaller than 0.05 (found by DESeq R package) was designated as differentially expressed gene (Anders and Huber, 2010; Love et al., 2014). Heatmap was employed for the identification of genes having a similar pattern of expressions. The underlined genes might analogous in functions or might have a common metabolic pathway. The RPKM approach was applied for obtaining the hierarchical clustering.

GO Annotation and KEGG Pathway Analysis of DEGs

GO comprised 3 ontology that revealed the cellular components, functions of molecular pathways, and genes processing. GO seq package was used for GO analysis of DEGs, containing adjusted p-values greater than 0.05. KEGG is considered to be the central public database of pathways (Yuan et al., 2018). In this study, KOBAS software ver-2.0 was used for the detection of the gene enrichment of DEGs in the KEGG pathway to identify significant pathways of cellular signaling, metabolism, and identification of variations cellular pathways among the groups. The pathways of KEGG and GO having adjusted p-values greater than 0.05 were found to be statistically considerable enrichment.

Quantitative Real-Time PCR

Total RNA was isolated from sera using TRIZOL (EP013, ELK Biotechnology Co., Ltd., Wuhan, China) and reverse-transcribed to cDNA using a kit (EQ031, ELK Biotechnology). The mRNA expression of the ERS-associated genes, mTOR pathway-related genes and validation of DEGs were measured using an SYBR® Green PCR Master Mix Kit (TaKaRa, Osaka, Japan) with a 20 µL of total reaction volume in the biorad IQ5 Real-Time PCR Detection System (Bio-Rad, Hercules, CA). The underlined parameters were used: 95 °C for 10 min; 40 cycles of 95 °C and 60 °C for 15 s and 1 min, respectively. Each reaction was conducted in triplicate for all samples. The value of relative gene expression was determined via the 2−ΔΔCt method using β-actin as a normalization control. The primer sequences are shown in Tables 1 and 2.

Table 1.

Primers for qRT-PCR.

Gene Product size (bp) Primer Sequence (5′-3′)
Grp78 108 F CATCCAACCCTGAGAACACTG
R CAACAACCTTGAAGGGCAGAT
ATF4 91 F CCTCTTGAACAACGAGATGCT
R GTCATCGAGGAGTCCCAGACTT
CHOP 114 F TGCTTAGCAGAATGGGATGG
R CACGCTGACACATGTAATAAAAC
PERK 142 F CGGCGGCAGATCTCTAGTAAT
R TACCTCTGGTTTACTGAGGCTG
eIF-2α 97 F GCAGATGATGAAAGTATTTGTGC
R GCAAATGAAGTTTATTTGCAATAGT
BAX 143 F CTTTATCCGCAAGGTCTACGC
R GGAAGACAGCGTATGAGGAGTAG
Caspase-3 125 F GGCCTGTAGAGGAACAGAATTAG
R AGTAGCCTGGAGCAGTAGAA
Bcl-2 261 F TATAAACTCTCGCAGCGGG
R GGCGAAGTCCCTCTGGTAG
TSC1 94 F CTGAGCCCGCAGTCAATAAA
R AGGTTGGTGGAGTGGTCATA
TSC2 125 F CTTACGTGTTGGTGCCTTAGA
R GTGTCTGTGGAGCAGGATTAG
Mtor 266 F TAAGAGCCTGCCTTATCCTCAC
R TTTGCAGTACTTGTCATGCACG
4E-BP1 159 F GATGGAGTGCCGTAATTCTCC
R TCAAACTGCTCTTCTTCACCTACAT
S6K 176 F CAATTTGCCTCCCTACCTCA
R AAGGAGGTTCCACCTTTCGT
β-actin 152 F GAGAAATTGTGCGTGACATCA
R CCTGAACCTCTCATTGCCA
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Table 2.

Primers for qRT-PCR.

Gene Size(bp) Primer Sequences(5′to3′)
GRB10 237 F GGGTAGCCAAACTCCACTCC
R CCATCATCCTCACACGGCAA
SGK1 272 F GGGTCCCGCTTTGACTTACTC
R GAAAGTCCGATGGCTTGGCA
PRKCA 146 F GGCGGACAGGAAGAATACAGAGG
R GAAGCTGTGTCAGGAATGGTGGTT
PIK3R3 109 F GGATCTCAACCGCATCCAAG
R AGCTACCACAGAACAGGCAT
STAR 153 F CTGCTGTTGAGTGCTTTGGT
R CTGAATCAGGCTCCTCGCTA
IL1B 191 F ACATCGCCACCTACAAGCTA
R GCAACGGGACGGTAATGAAA
CDKN1A 99 F GTAGACCACGAGCAGATCCA
R CGTCTCGGTCTCGAAGTTGA
WNT16 112 F AACGTGTGTGCCTATGGAGA
R TTGGCACACCACATTCACTG
RAF1 115 F GATGCAAGACAGCAATCCGT
R CTGGTCGCGGTTGTTTATGT
RPS6KA2 90 F GTCAGGAAACCCAGACTCCA
R TAGCAGGGAGTCATGAGCAG
FOXO1 199 F GTTGGGTGTCAGGCTAGGAT
R CTGCCAACTCTGACGAAAGG
BOK 120 F GGCAAGGTGGTGTCTCTCTA
R GGTCTTGCGGACAAACTCTC
FST 179 F AATTTGCCCTGAGCCTACCT
R TCCCAGCACTGCACTGAATA
BMP5 159 F CTGGAATTGCTGGGTTCTGG
R AATGGTCTGGGTCTGTGAGG
AIFM3 139 F CCCAGGTTCCAGGTGAAGAT
R GCACATTGGTACTGCTCAGG
PAWR 82 F CCAGCAATTCAGCAACGAGT
R CCCACTTGCAGCAGTGTATC
β-actin 145 F ATCAGCAAGCAGGAGTACGA
R AAAGCCATGCCAATCTCGTC
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Statistical Analysis

SPSS 20.0 (IBM Corp., Armonk, NY) and GraphPad Prism 8.0.2 (GraphPad, San Diego, CA) were used for data analysis, and the data are expressed as mean ± SEM. The value of relative gene expression between the qRT-PCR and RNA-Seq were compared via unpaired Student's T-test, other-group differences were assessed using ANOVA and Duncan's method for multiple comparisons. P < 0.05 was considered statistically significant.

RESULTS

Effect of MEL on Production Performance of Laying Hens

Table 3 shows the effects of MEL on the laying performance of the hens. In wk 2 and 4, the egg production of the Old-CN group was significantly lower than Young-CN group (P < 0.01). In wk 2, the average egg weight and feed-egg ratio increased significantly (P < 0.01), whereas after MEL treatment the laying rate was markedly raised compared with the Old-CN group (P < 0.05). In wk 4, accompanied by a significant increase in the average egg weight (P < 0.01), while MEL treatment significantly elevated the laying rate compared to the Old-CN group (P < 0.01).

Table 3.

Effect of MEL on laying performance in hens.

Period Items Young-CN Old-CN MEL P value
Wk 2 ADFI (g) 120.34±3.64 119.38±5.78 116.4±8.42 0.27
AEW (g) 58.04±4.13B 65.93±6.54A 64.72±3.57A 0.00
LR (%) 85.64±2.56A 67.58±4.12Bb 75.18±5.18Ba 0.00
FER 2.42±0.17B 2.70±0.15A 2.55±0.13AB 0.000
Wk 4 ADFI (g) 122.83±4.85 118.39±7.61 121.32±3.38 0.31
AEW (g) 59.74±2.62B 66.49±3.51A 65.37±5.45A 0.00
LR (%) 84.52±4.59A 66.84±3.66B 78.28±2.73A 0.00
FER 2.43±0.16 2.57±0.20 2.44±0.18 0.14
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ADFI, average daily feed intake; AEW, average egg weight; LR, laying rate; FER, FER-feed-egg ratio.

Values are means ± SEM.

Different superscript letters indicate significant differences (n = 60).

a-b

P < 0.05

A-B

P < 0.01.

Effect of MEL on Egg Quality of Laying Hens

The effects of MEL on the egg quality of the laying hens are shown in Table 4. In wk 2, both the HU (P < 0.05) and ESS (P < 0.01) were significantly decreased in the Old-CN group while the EW was significantly increased (P < 0.05). In wk 4, there was a significant reduction in ESS (P < 0.05), while the EW was significantly increased in the Old-CN group (P < 0.05).

Table 4.

Effects of MEL on the egg quality of laying hens.

Period Items Young-CN Old-CN MEL P Value
Wk 2 ESI 1.31±0.04 1.33±0.04 1.33±0.03 0.16
EST (mm) 0.37±0.02 0.36±0.03 0.35±0.03 0.46
AH (mm) 6.34±0.25 5.85±0.65 5.6±1.29 0.59
HU 78.85±9.77b 72.26±6.94a 70.18±8.74a 0.05
EW (g) 57.88±4.03b 63.9±5.81a 61.36±3.96a 0.03
ESS (N) 41.91±2.22A 34.05±5.72B 35.34±3.65B 0.00
Wk 4 ESI 1.32±0.02 1.33±0.02 1.34±0.03 0.27
EST (mm) 0.36±0.03 0.36±0.03 0.35±0.03 0.16
AH (mm) 6.55±0.74 5.79±1.31 5.98±0.85 0.34
HU 80.08±8.63b 75.43±10.97ab 71.38±15.86a 0.02
EW (g) 60.78±3.62b 65.73±1.39a 64.6±2.14a 0.03
ESS (N) 40.71±3.42a 32.77±3.25b 36.79±4.08b 0.02
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ESI, egg shape index; EST, eggshell thickness; AH, albumen height; HU, Haugh unit; EW, egg weight; ESS, eggshell strength.

Values are means ± SEM.

Different superscript letters indicated significant differences (n = 40), If there is no marked letter, the difference is not significant.

a-b

P < 0.05.

A-B

P < 0.01.

Effects of MEL on Plasma Parameters and mRNA Expression of ERS-Related Indexes in Laying Hens

Figure 1A shows that while the plasma MEL levels were significantly decreased in the Old-CN group (P < 0.01), the levels were markedly elevated following MEL treatment (P < 0.01). The effects of MEL on the plasma levels of FSH, LH, and E2 levels in laying hens are illustrated in Figures 1B-1D. Compared with the Young-CN group, the plasma FSH, LH, and E2 levels were significantly reduced in the Old-CN group (P < 0.01), whereas MEL treatment significantly increased the plasma FSH, LH, and E2 concentrations compared with the Old-CN group (P < 0.01, Figures 1B-1D).

Figure 1.

Figure 1

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Effects of MEL on plasma MEL(A), FSH(B), LH(C), E2(D), and plasma mRNA expression of Grp78(E), Caspase-3(F), CHOP(G) level between the young peak period chicken control group (Young-CN), later period chicken control group (Old-CN), and melatonin (MEL) group. The results are presented as the means ± SEM. *P < 0.05, ⁎⁎P < 0.01 represents significant differences compared with the Young-CN group. #P < 0.05, ##P < 0.01 represent significant differences compared with the Old-CN group.

The effects of MEL on the plasma expression of ERS-related indexes are shown in Figures 1E-1G. The plasma expression of Grp78, Caspase-3, and CHOP was significantly increased in the Old-CN group (P < 0.01), whereas MEL treatment significantly decreased the Grp78, Caspase-3, and CHOP mRNA expression compared with the Old-CN group (P < 0.01, Figures 1E-1G).

Effects of MEL on Follicle Numbers and the mRNA Expression of mTOR-Related Genes in Follicles

The effects of MEL treatment on the mRNA expression of ERS-related genes in follicles are shown in Figure 2. It can be seen that the mRNA expression of Grp78 and CHOP were significantly increased in the Old-CN group in POF, SYF, NWF, and PE (P < 0.01, Figures 2A-2B), whereas MEL treatment significantly reduced the expression levels of Grp78 and CHOP in POF, SYF, and NWF (P < 0.01, Figure 2A-2B), as well as the expression of CHOP in PE (P < 0.05, Figure 2B). Expression of Caspase-3 was significantly increased in POF (P < 0.01), SYF (P < 0.01), and NWF in the Old-CN group (P < 0.01), while MEL treatment reduced the Caspase-3 mRNA expression in SYF, NWF, and PE (P < 0.01, Figure 2C). The mRNA expression of eIF-2α was elevated in both POF and SYF in the Old-CN group (P < 0.01), whereas MEL treatment significantly decreased eIF-2α mRNA expression in POF, SYF, and NWF (P < 0.01, Figure 2D).

Figure 2.

Figure 2

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Comparison of the mRNA expression of Grp78(A), CHOP(B), Caspase-3(C), eIF-2α(D), TSC1(E), TSC2(F), mTOR(G), 4E-BP1(H) and S6K(I) in follicles between the young peak period chicken control group (Young-CN), later period chicken control group (Old-CN), and melatonin (MEL) group. The results are presented as the means ± SEM. *P < 0.05, ⁎⁎P < 0.01 represents significant differences compared with the Young-CN group. #P < 0.05, ##P < 0.01 represent significant differences compared with the Old-CN group.

The effects of MEL on the expression of mTOR pathway-related genes in follicles are shown in Figure 2E-2I. Compared with the Young-CN group, the expression of TSC1 and TSC2 was reduced in POF and SYF in the Old-CN group (P < 0.01, Figures 2E-2F). Levels of mTOR were significantly decreased in POF but elevated in SYF in the Old-CN group (P < 0.01, Figure 2G). Compared with the Young-CN group, the expression of S6K was markedly lower in POF, NWF, and PE (P < 0.05, Figure 2I), while that of 4E-BP1 was reduced in POF (P < 0.05, Figure 2H) and PE (P < 0.01, Figures 2D-2E) in the Old-CN group. After MEL treatment, the mRNA levels of TSC1 (P < 0.01), TSC2 (P < 0.01), mTOR (P < 0.01), 4E-BP1 (P < 0.05), and S6K (P < 0.01) were significantly increased in POF and SYF (Figures 2E-2H), and expression of TSC1 and TSC2 was raised in NWF (P < 0.01, Figures 2E-2F), while both 4E-BP1 (P < 0.05) and S6K (P < 0.01) levels were raised in PE (Figure 2G-2H).

The effects of MEL on ovarian follicular numbers in laying hens are shown in Figure 2J. Both LWF and PE were significantly reduced in the Old-CN group (P < 0.01), whereas MEL treatment significantly increased the numbers of LWF, MWF, and PE (P < 0.01).

RNA Sequencing Data of Chicken Granulosa Cells

Illumina Hiseq2500/X platform (NEB) was employed for the sequencing of 6 cDNA library preparations (3 from the control group: C1, C2, C3, and 3 from the MEL group: M1, M2, and M3). The RIN values was C1:9.7, C2:9.6, C3:9.8, M1:9.7, M2:9.6, M3:9.4, respectively. As represented in Table 5, a total of 243.11 million reads per sample with an average of 40.51 million reads (range from 36.51 to 45.51 million) were acquired post removal of the poor quality and adaptor sequences. The percentage of mapping reads ranged from 87.83 to 89.21%. The average values of Q20 and Q30 are 97.34 and 92.50%, respectively.

Table 5.

RNA-seq statistics for 6 samples.

Samples1 Total_read Total_mapped Multi_mapped Uniq_mapped Q20(%) Q30(%)
C1 39969600 35562604(88.97%) 2756151(6.89%) 32806453(82.08%) 97.39 92.65
C2 45510408 40386509(88.74%) 3104740(6.82%) 37281769(81.92%) 97.41 92.63
C3 41115000 36191022(88.02%) 2794878(6.79%) 33396144(81.23%) 97.20 92.24
M1 40410380 35919588(88.89%) 2697195(6.67%) 33222393(82.21%) 97.29 92.38
M2 39590776 35319979(89.21%) 2671996(6.75%) 32647983(82.46%) 97.31 92.41
M3 36514688 32070244(87.83%) 2452532(6.72%) 29617712(81.11%) 97.44 92.68
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1

C1–C3 represent the control group (Granule cells are cultured in normal medium), and M1-M3 represent the treatment group (Granule cells are treated with 200umol/L melatonin).

The Analysis of Differential Expression

The analysis of the expression level of the DEGs (581) in the granulosa cells between 2 groups was carried out (log2|fold change|≥2 or ≤ 0.5 at P < 0.05) in which 259 and 322 genes were upregulated and downregulated, respectively, as represented in Figure 3A and Table S1.

Figure 3.

Figure 3

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Summary of sequencing results. (A) The volcano diagram of differentially expressed genes. Upregulated genes are shown in red, downregulated genes are shown in green, and genes with no significant difference in expression are indicated in blue; significance was indicated by P-value < 0.05. (B) GO enrichment analysis of DEGs in the yellow follicle granular layer tissues. The 3 main categories used for GO analysis are shown. The y-axis and x-axis indicate subcategory name and gene number, respectively. (C) The GO analysis of up-regulate differentially expressed gene—molecular function. Different colors represent different significance, the deeper the color, the more significant. (D) The GO analysis of up-regulate differentially expressed gene—biological process. KEGG enrichment analysis of DEGs in the granulosa cells. (E) The significantly enriched 30 KEGG pathways are presented. The y-axis and x-axis indicate pathway name and enrichment factor, respectively. The size of the circle indicates gene number.

Functional Annotation as Well as Pathways Assignment of Key DEGs

The GO analysis of DEGs was performed to evaluate the functional correlations of the 581 effective DEGs. The obtained results revealed that these genes were grouped into 3 major categories: biological processes, cellular component, and molecular function, as depicted in Figure 3B. Biological processes are mostly involved in immune responses (GO:0006955), positive regulation of cellular biosynthesis (GO:0031328), positive regulation of cell cycle (GO:0045787), negative regulation of apoptotic process (GO:0043066), the metabolic process of ROS (GO:0072593), response to oxidative stress (GO:0003979), the negative regulation of p53 mediators in response to DNA damage, cellular signaling (GO:0043518), cell cycle (GO:0022403), the regulation of steroid biosynthesis process (GO:0050810), the process of steroid biosynthesis (GO:0006694) and other biological processes, as represented in Table 6. The analysis showed that there were 231 genes significantly enriched entries in the biological process category, 59 genes significantly enriched entries in the molecular function category and 20 genes significantly enriched entries in the cellular component category (P < 0.05) (Supplementary File S2). The GO analysis of highly expressed differential genes is associated with their biological processes as well as the regulation of molecular processes, as depicted in Figure 3D. GO analysis-molecular function analysis, depicted in Figure 3C, is mainly enriched in binding to receptors. The annotation of DEGs was performed via KEGG for the identification of enriched cascades. The obtained results revealed that 581 DEGs were mapped into 85 canonical reference cascades, as depicted in Figure 3E and Supplementary File S3. The considerably enriched cascades related to the proliferation and differentiation of cells were mTOR signaling cascades (comprising 6 DEGs), PI3K-AKT signaling cascades (comprising 27 DEGs) as well as FOXO1 signaling cascades (comprising 15 DEGs).

Table 6.

The GO analysis of differentially expressed gene.

Category Term P-Value Numbers of DEGs
Cellular component GO:0005615-extracellular space 1.51E-09 24
Cellular component GO:0031012-extracellular matrix 0.000167 6
Cellular component GO:0045177- apical part of cell 0.010677 4
Cellular component GO:00016020-membrane 0.015142 13
Biological process GO:0050810- regulation of steroid biosynthetic process 0.022891 1
Biological process GO:0006955- immune response 2.67E-08 11
Biological process GO:0031328- positive regulation of cellular biosynthetic process 7.78E-07 4
Biological process GO:0045787- positive regulation of cell cycle 0.005674 3
Biological process GO:0043066- negative regulation of apoptotic process 0.007546 11
Biological process GO:0043280- positive regulation of cysteine-type endopeptidase activity involved in apoptotic process 0.012809 2
Biological process GO:0000082- G1/S transition of mitotic cell cycle 0.014292 3
Biological process GO:0007346- regulation of mitotic cell cycle 0.021347 2
Biological process GO:0072593- reactive oxygen species metabolic process 0.021511 2
Biological process GO:0043518- negative regulation of DNA damage response, signal transduction by p53 class mediator 0.023004 2
Biological process GO:0022403- cell cycle phase 0.023461 1
Biological process GO:0003979- response to oxidative stress 0.024644 3
Biological process GO:0006694- steroid biosynthetic process 0.030759 2
Molecular function GO:0008009-chemokine activity 5.78E-09 7
Molecular function GO:0005125-cytokine activity 2.5E-05 8
Molecular function GO:0004508-steroid 17-alpha-monooxygenase activity 0.021808 1
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The Confirmation of RNA-Seq Data via Quantitative RT-PCR

According to Figure 3A and Table S1, the DEGs contributed to the mTOR signaling pathway were GRB10, MYH1G, JUND, SGK1, PRKCA, RAF1, RPS6KA2, RASSF1, RASGRP3, WNT16, PIK3R3, SGK3, and genes associated with the proliferation and apoptotic process of follicles were STAR, CDKA1A, CDK2AP1, BOK, IL1B, IL34, BMP5, FST, CYP3A7, G0S2, PAWR, CD44, AIFM3, and FOXO1. The differentially expressed genes related to ERS were DERL3, HMOX1, TLR7, VAMP7, and INSIG2.

To confirm the accuracy of expression results of RNA sequencing, the 16 genes that may correlate with mTOR pathway and the proliferation and apoptotic process of follicles were chosen from 2 groups to validate RT-PCR analysis. As shown in Figures 4 and 5, GRB10, SGK1, PRKCA, RPS6KA2, RAF1, PIK3R3, WNT16, CDKA1A, STAR, IL1B, BOK, FAT, BMP5, AIFM3, PAWR, and FOXO1 were the significant genes chosen from the underlined groups. The obtained results revealed that the expression tendencies between the control and the MEL group showed consistency with the results of RT-PCR which validated the accuracy of our transcriptome data.

Figure 4.

Figure 4

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qRT-PCR validation of upregulated DEGs identified by transcriptome analysis.

Figure 5.

Figure 5

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qRT-PCR validation of downregulated DEGs identified by transcriptome analysis.

DISCUSSION

In this study, we have explored the effects of MEL on the expression patterns of genes related to the ERS and mTOR pathway in graded follicles at the in vivo level and investigated MEL treated granulosa cells at the in vitro level, then followed by RNA sequencing technology to exploring the RNA expression level of granulosa cells in chicken under the MEL treatment, in order to elucidate the molecular mechanism by which melatonin contributes to the growth and development of follicle and provided a theoretical basis for in-depth research on the establishment and extension of follicle grading and the utilization cycle of laying hens.

The laying rate is closely related to the number of follicles. The number of follicles decreases as the hens age (Zakaria et al., 1983). Our study demonstrated that the laying rate was significantly reduced in the Old-CN group, while MEL treatment significantly reversed the change. The numbers of LWF and PE follicles were significantly decreased in the Old-CN group, and MEL treatment was found to significantly increase the numbers of LWF, MWF, and PE follicles. MEL has been shown to have antioxidant functions in aging mice, increasing the number of ovulations (Hao et al., 2020; Tamura et al., 2017). These results suggest that MEL treatment could improve the laying rate by increasing follicle numbers.

Follicular granulosa cell apoptosis and follicular atresia gradually decline with increasing age (Faddy, 2020). These are mainly regulated by the synthesis and secretion of the GnRH, FSH, LH, and E2 hormones through the hypothalamus-pituitary-gonadal (HPG) axis. FSH mainly promotes the growth and development of follicles, while LH promotes follicular maturation and ovulation and E2 regulates the development of follicular small yellow follicles to grade follicles (Johnson, 1993; Chen, 2003). In our study, the plasma levels of FSH, LH, MEL, and E2 in the Old-CN group were significantly lower than those in the Young-CN group but were significantly improved after MEL treatment. Odell et al found that the levels of LH and FSH could be increased by high doses of MEL, whereas ovulation was inhibited by low plasma levels of LH and FSH (Odell and Swerdloff, 1968). This is consistent with our findings. In vitro injection of MEL was found to increase the level of MEL in the plasma, increase the contents of FSH, LH, and E2 in the blood, and promote ovulation.

In ERS, PERK dissociates from Grp78, resulting in activation and leading to phosphorylation of eIF-2α. This triggers the expression of the transcription factor ATF4, mediated by the stress protein CHOP (Liu et al., 2018; Ma et al., 2020; Hu et al., 2020). CHOP activates Caspase-3 by inducing mitochondria to release cytochrome C (Cyt c), leading to apoptosis (Wang et al., 2021). In our study, the mRNA expression of ERS-related genes was significantly increased in the Old-CN group. In vitro injection of MEL could alleviate ERS by decreasing the expression of ERS-related genes. The mRNA expression of Caspase-3 was significantly increased in the Old-CN group. Follicular development and atresia are affected by proteins of the caspase family. The activation of Caspase-3 is an indicator of apoptosis. MEL treatment significantly reduces Caspase-3 mRNA expression level (Riaz et al., 2019). The activation and expression of Caspase-3 in bovine oocytes in vitro could be inhibited by MEL and in bovine oocytes in vitro. This is consistent with this study. Therefore, in vivo injection of MEL alleviates ERS by reducing the expression of ERS-related genes.

TSC1 and TSC2 function upstream of mTOR signaling, while 4E-BP1 and S6K1 function downstream of mTOR (Gorre et al., 2014; Zhang et al., 2016). Previous research found that the follicular pool was overly activated in mice after knockdown of TSC1 and TSC2, which induced the depletion of follicles (Adhikari et al., 2009). The present study showed that MEL treatment significantly increased TSC1, TSC2, mTOR, 4E-BP1, and S6K mRNA expression in the Old-CN group, which was consistent with previous reports that MEL treatment significantly increased the mRNA expression of mTOR and its downstream target gene P70S6k (Behram et al., 2017). It was observed that the mTOR pathway was activated after being treated with MEL.

In vivo, we found that MEL treatment significantly increased the laying rate compared with the Old-CN group. This outcome could have 2 causes. On the one hand, MEL treatment regulates the ovulation process by increasing the plasma concentrations of FSH, LH, E2, and MEL, thus increasing the number of follicles, and thereby increasing egg production; on the other hand, MEL treatment reduced follicular granulosa cell apoptosis by reducing the expression of ERS-related genes and activating the mTOR pathway in the Old-CN group, thereby increasing the laying rate. However, the specific signal transduction mechanism was not clear. Therefore, with the advancements in high-throughput DNA sequencing, the animal breeding approaches have been progressively transformed from traditional breeding to the strategies in which many technologies of omics have been combined. Under particular physiological conditions, high-throughput sequencing including RNA sequencing gives a platform to explore the complex biological processes which include a network of coding genes (Shiue et al., 2006).

In the present research work, granulosa cells were separated from SYFs and exposed to MEL to identify the impact of the underlined hormone on the granulosa cells. Post bioinformatics analysis and statistics, a total of 19115 genes were identified in 6 samples treated with and without MEL, and 581 effective DEGs were calculated. Among them, 259 and 322 genes were up-regulated and down-regulated, respectively. It was found that the DEGs contributed in the mTOR signaling cascades are GRB10, MYH1G, JUND, SGK1, PRKCA, RAF1, RPS6KA2, RASSF1, RASGRP3, WNT16, PIK3R3, SGK3 and genes associated with the proliferation and apoptotic process of follicles are STAR, CDKA1A, CDK2AP1, BOK, IL1B, IL34, BMP5, FST, CYP3A7, G0S2, PAWR, CD44, AIFM3, FOXO1. The differentially expressed genes related to ERS were DERL3, HMOX1, TLR7, VAMP7, and INSIG2. INSIG2 was previously reported to be an ER protein that binds sterol regulatory element binding protein cleavage-activating protein (SCAP) and blocks export of sterol regulatory element binding proteins (SREBPs) (Li et al., 2010). In our results, the mRNA expression of the INSIG2 was downregulated indicates that MEL could alleviated ERS. The results of GO enrichment analysis revealed that the main enriched components of the cell are cell membrane and cell-matrix. The biological process is mainly enriched in the process of actively regulating the cell cycle, proliferation, and inhibiting apoptosis. It is also involved in oxidative stress and ROS. The regulation process and participation in the regulation of the steroid biosynthesis process indicate that MEL can actively regulate the cell cycle, proliferation, and other activities, inhibit apoptosis, and oxidative stress, etc. MEL also participates in the regulation of steroid hormones.

KEGG analysis results mainly focused on the PI3K-AKT signal pathway, mTOR signal pathway, and other signal pathways. mTOR is a serine/threonine protein kinase. The mTOR pathway is regulated by a variety of cell signals and regulates the cell growth, development, and survival of the cell (Laplante and Sabatini, 2009). Studies have shown that after intraperitoneal injection of MEL in mice, oxidative stress indicators are reduced, and it participates in regulating the proliferation of mouse follicles via stimulating the mTOR signaling cascade (Behram et al., 2017). The results of this experiment show that PRKCA, SGK1, PIK3R3, RAF1, and RPS6KA2 contributes to the regulation of the chicken follicular mTOR signaling cascade. MEL reduces cisplatin-activated apoptosis in HepG2 cells by maintaining a balance between the effects of apoptosis-related proteins and autophagy associated proteins (Bennukul et al., 2014). MEL reduces mitochondrial-related oxidative stress and increases the expression of mTOR, p-mTOR, Rictor, and Raptor proteins, alleviating apoptosis induced by high glucose in cells (Tiong et al., 2019). Phosphatidylinositol 3-kinase-protein kinase B (PI3K-Akt) is a classic signaling cascade, and PI3K/Akt-facilitated signaling is a highly crucial cascade for the regulation of cell survival, proliferation, and metabolism (Bellacosa et al., 1998). Studies have found that FSH alleviates the autophagy damage induced by granular cell oxidative stress via triggering the PI3K-AKT-mTOR signaling cascade which plays a regulatory role (Shen et al., 2017). The PI3K-AKT pathway has a significant role in the downstream of MEL signaling in mammalian cells (Picinato et al., 2007). Inhibition of autophagy through the PI3K-AKT pathway is involved in the protective effect of MEL-mediated granulosa cells on oxidative damage (Shen et al., 2018). MEL inhibits the phosphorylation of p53 via PI3K/Akt cascade, leading to increased neuronal survival (Kilic et al., 2017). MEL activates the PI3K/AKT-PGC-1α signaling pathway, so protects the liver cells from alleviating oxidative damage (induced via NaF) (Song et al., 2017). The obtained results of the KEGG cascade revealed that 27 genes contribute to the regulation of the PI3K-Akt signaling cascade. Among them, PKC, SGK1, and PIK3R3 participate in the development of chicken follicles through the cell cycle, proliferation, differentiation and hormone secretion regulation. The underlined genes also have a role in the regulation of the PI3K-Akt signaling cascade, which indicates that the PI3K-Akt pathway and the mTOR signaling cascade jointly regulate the development of follicles. MEL may directly affect the function of granulosa cells via PI3K-AKT-mTOR cascade.

CONCLUSIONS

In conclusion, the in vivo results showed the presence of ERS in aged laying hens. MEL treatment inhibits the expression of ERS-related genes and activates the mTOR signaling pathway-related genes. In vitro, RNA-seq sequencing showed significant enrichment of differentially expressed genes associated with proliferation, differentiation, apoptosis, ERS and mTOR signaling pathway under MEL treatment. Both the in vivo and in vitro results showed that MEL can activated the mTOR signaling pathway and alleviated ERS-induced apoptosis of follicle, thereby increasing the laying rate of aged laying hens. The underlined results give a reference to explore the regulatory mechanism of MEL in aged laying hens, provides a theoretical background for enhancing the egg-laying capability of hens and also providing a basis for elucidating the molecular mechanism of follicular selection in mammals.

Acknowledgments

ACKNOWLEDGMENTS

This study was supported by the China Agriculture Research System of MOF and MARA (CARS-40), the Modern Agricultural Industry Technology System Construction Project of Hebei Province (HBCT2023210201), the General Program of National Natural Foundation of China (32002220 and 32202736), the Hebei Natural Science Foundation (C2022204149), Hebei Agricultural University Talent Introduction Research Project (YJ2021012).

DISCLOSURES

The authors declare no conflicts of interest.

Footnotes

Supplementary material associated with this article can be found in the online version at doi:10.1016/j.psj.2024.103703.

Appendix. Supplementary materials

Supplementary Materials: File S1: CON vs MEL differentially expressed genes, File S2: Statistics of GO enrichment items in CON vs MEL, File S3: Statistics of KEGG enrichment items in CON vs MEL.

mmc1.xlsx (125.3KB, xlsx)
mmc2.xls (79KB, xls)
mmc3.xlsx (176.5KB, xlsx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Materials: File S1: CON vs MEL differentially expressed genes, File S2: Statistics of GO enrichment items in CON vs MEL, File S3: Statistics of KEGG enrichment items in CON vs MEL.

mmc1.xlsx (125.3KB, xlsx)
mmc2.xls (79KB, xls)
mmc3.xlsx (176.5KB, xlsx)

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