Identification of potential candidate genes and regulatory pathways related to reproductive capacity in hypothalamus and pituitarium of male ducks (Anas platyrhynchos) by differential transcriptome analysis

Abstract

The improvement of reproductive capacity of poultry is important for the poultry industry. The existing studies on reproductive capacity mainly focus on the testis tissue, but few reports on regulationary effect of brain neuroendocrime on reproductive capacity have been available. The hypothalamus–pituitarium–gonad (HPG) axis is an important pathway regulating spermatogenesis and sexual behavior. This study analyzed the gene expression in the hypothalamus and pituitary tissues of male ducks in high-semen-quality group (DH), low-semen-quality group (DL), and non-response group (DN) by RNA-sequencing. A total of 1980 differentially expressed genes (DEGs) were identified, and significantly less DEGs were found in pituitary gland than in hypothalamus. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that these DEGs were mainly enriched in nerve-related and synapse-related biological processes, mitochondrial inner membrane formation pathway, and ribosome structure pathway. Notably, the neuroactive ligand–receptor interaction pathway significantly enriched in all three comparisons (DH vs. DL, DH vs. DN, and DL vs. DN) was related to different reproductive performance such as semen quality and sexual response. Furthermore, six genes, including POMC, CPLX2, HAPLN2, EGR4, TOX3, and MSH4, were identified as candidate genes regulating reproductive capacity. Our findings provide new insights into the regulation mechanisms underlying the reproductive performance of male poultry, and offer a valuable reference for duck breeding programs aimed at promoting reproductive capacity.

The potential genes identified in our study can serve as candidate genes for further exploration of selectable markers in ducks to improve their reproductive performance. The identified relevant biological pathways may provide an insight into the potential molecular mechanisms by which the neuroendocrine system regulates reproductive abilities in male ducks.

Introduction

Duck (Anas platyrhynchos) is one of the most essential domesticated birds in the world. With the improvement of intensification level, artificial insemination (AI) has been widely used in the breeding programs of ducks to improve the reproductive efficiency (K. et al., 2014). But the sperm collection from ducks is more difficult than that from other poultry, such as chicken. The semen quality and sexual sensitivity of male ducks determine the economic effect and breeding process. The factors affecting semen quality include nutrition (Fouad et al., 2020), environment (Shanmugam et al., 2014; Du et al., 2021), season (Łukaszewicz et al., 2020), and heredity. The genetic selection of varieties with higher semen quality and sexual sensitivity can promote breeding programs and reduce raising costs.

In recent years, molecular breeding has been widely employed to identify candidate genes responsible for semen quality traits, which contribute greatly to the improvement of reproductive performance. Compared with those in normal individuals, cytochrome c oxidase subunit 7B (COX7B) and prostaglandin D2 synthase (PTGDS) were significantly highly expressed in testes of rooster with asthenospermia, wnt family member 2 (WNT2), hematopoietic prostaglandin D synthase (HPGDS), and cyclin F (CCNF) were significantly lowly expressed (Fu et al., 2015). The low expression of HPGDS and WNT2 in testes of Beijing-you chickens may be related to the abnormal testicular morphology of low sperm motility birds (Sun et al., 2019). Additionally, RNA-seq technology has also been utilized to analyze the expression profiles of semen quality-related long noncoding RNAs and mRNAs in the testes of domestic pigeon (Xu et al., 2020), chicken (Liu et al., 2017) and turkey (Słowińska et al., 2020) so as to identify related candidate genes and reveal molecular mechanisms.

The majority of the existing research on semen quality is focused on the testis, but few studies have been conducted to investigate the role of hypothalamus and pituitary gland in spermatogenesis. The HPG axis controls spermatogenesis through the coordination of multiple hormones, of which gonadotropin-releasing hormone (GnRH), typically secreted in pulses, can stimulate the secretion of the gonadotropins (GnH), including luteinizing hormone (LH) and follicle-stimulating hormone (FSH) from the anterior pituitary gland. These hormones can stimulate the gonads to produce gametes and promote the release of sex steroids such as testosterone (T), estradiol (17β-estradiol, E2), and progesterone (P4). In addition to reproductive function in the peripheral tissues, these gonadal steroids can also feedback modulate upstream HPG components (Bédécarrats, 2015; Acevedo-Rodriguez et al., 2018). Therefore, the HPG axis plays a key role in spermatogenesis, and it determines semen quality. The current studies on the effects of HPG axis on reproductive traits of poultry have mainly focused on female poultry, such as the identification of candidate genes related to egg production traits in hens (Yan et al., 2022). Considering this, it is necessary to further explore the mechanism by which the HPG axis regulates the reproductive traits in male poultry.

In this study, the semen quality of male ducks was measured. We found that some male ducks were insensitive to sexual behavior, and there were great differences in semen quality among different individuals. To explore the reasons for the differences in the semen quality and poor sexual behavior of male ducks, this study identified the differentially expressed genes (DEGs) in the hypothalamus and pituitary gland among different reproductive-trait groups. Our identified reproductive-trait related key candidate genes will provide a reference for studying the genetic basis of semen quality traits and molecule-assisted breeding in male ducks.

Materials and Methods

Experimental animals

All animal experiments were carried out following standard procedures and approved by the Ethics Committee of Huazhong Agricultural University, China.

The experimental animals were selected from the 170-day-old breeding male duck population of Wuqin-10 meat duck, which were raised in the breeding duck farm of Wuhan Academy of Agricultural Sciences (Wuhan, China). The population of male ducks was screened by estrus stimulation, and after stimulation, some individuals showed obvious sexual behavior such as mounting, pecking neck, and mating, whereas others exhibited no response to female ducks. Totally, 41 individuals with sensitive sexual behavior and 15 individuals with insensitive sexual behavior were selected. The individuals with sensitive sexual behavior were trained to enhance their sexual response, and semen was collected from these sexually sensitive individuals for a subsequent semen quality test.

Semen quality evaluation and sample collection

The collected semen was placed in an incubator, and the semen quality test was completed within 20 min. Semen volume was measured with a 100 μL pipette. Sperm concentration was detected using a hemocytometer. Sperm motility was observed under the microscope and calculated as the ratio of linearly moving sperm to the total sperm count. If 90% of the sperm exhibited linear motility, sperm motility score was recorded as 9, similarly, if 80% of the sperm displayed linear motility, sperm motility score as 8, and so on (Xu et al., 2020). After Trypan blue staining, sperm viability (%) was calculated as the proportion of viable sperm count to total sperm count. After Eosin staining, the sperm deformity rate was calculated. The above indicator determination was repeated three times.

According to the above semen quality assessment results, the experimental ducks were divided into three groups of high (DH), medium (DM), and low (DL) sperm motility. In addition, the sexually insensitive individuals were defined as the non-response group (DN). Three male ducks were selected from each group (DH, DL, and DN) for slaughter sampling. After 12 h of fasting, ducks were euthanized. Hypothalamus and pituitary tissues were extracted respectively, snap-frozen in liquid nitrogen, and stored at −80°C for RNA extraction and transcriptome sequencing. The hypothalamic samples from three groups were named DhH, DhL, and DhN, respectively. Similarly, pituitary samples from the three groups were named DpH, DpL, and DpN. Testicular tissue was also extracted, fixed with paraformaldehyde, and stained with hematoxylin and eosin (H&E) for histomorphological observation, according to the reported procedures (Andrés-Manzano et al., 2015).

RNA extraction and sequencing

Total RNA was extracted from hypothalamus and pituitary tissues with TRIzol reagent (Invitrogen, San Diego, CA) following the manufacturer’s instructions for transcriptome sequencing and real-time quantitative PCR. The RNA integrity was detected by 1.5% agarose gel electrophoresis, and the purity and concentration of RNA were measured by a nucleic acid analyzer NanoDrop 2000 (NanoDrop Technologies, Wilmington, USA).

The qualified RNAs of three individuals from each group were applied to construct the cDNA library using the NEBNext Ultra Directional RNA Library Prep Kit and the RNA quality was assessed on the Agilent Bioanalyzer 2100 system. The index-coded samples were clustered on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumina) and sequenced on the Illumina NovaSeq 6000 platform with 150 bp paired-end reads. RNA library construction and sequencing were completed by Novogene (Beijing, China).

Principal component analysis (PCA) and differential expression analysis

The quality control of raw sequencing reads was performed using the FastQC software, and adapters were trimmed by the Trimmomatic. Clean reads were mapped to the duck reference genome (https://www.ncbi.nlm.nih.gov/data-hub/genome/GCF_015476345.1/) using the STAR software (Version 2.7.10a). The featureCounts (Version 2.0.1) was used to count the reads mapped to each gene. The abundance of each gene was expressed as the fragments per kilobase of exon per million fragments mapped (FPKM), then the FPKM matrix of all genes (except genes with the sum of FPKM values less than 1) was subjected to PCA analysis. PCA was performed using R (Version4.1.2), and PCA results were visualized using the R package ggplot2. The gene differential expressions in obtained read count matrices were analyzed using R package DESeq2. The padj (adjusted P-value after multiple-corrections) ≤0.05 and |log2FoldChange| ≥ 1 were thresholds for the identification of DEGs.

Enrichment and protein–protein interaction (PPI) analyses of DEGs

GO and KEGG enrichment analyses of DEGs were implemented by R package clusterProfiler. PPI analysis of DEGs was carried out using the STRING (https://string-db.org/) with the required minimum interaction score of 0.900, and PPI analysis results were visualized by using Cytoscape v3.9.1 software. The hub genes were identified using the plug-in cytoHubba and MCC (Maximal Clique Centrality).

Quantitative real-time PCR (qRT-PCR)

Total RNA from each sample was reverse-transcribed to first-strand cDNA using the Evo M-MLV RT Mix Kit with gDNA Clean for qPCR (AGbio, China) according to the manufacturer’s instruction. The specific primers of the selected genes were designed using the online site Primer3Plus (https://www.primer3plus.com/) for qRT-PCR (Table 1). The qRT-PCR was conducted using a CFX384 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA). The 10 µL PCR reaction system contained 5 µL 2 × SYBR Green Pro Taq HS Premix (AGbio, China), 1 µL diluted cDNA, and 0.2 µL each primer (10 µM), and 3.6 µL ddH₂O. PCR conditions were as follows: 95°C for 30 s, followed by 40 cycles of 95°C for 5 s and 60°C for 30 s. Gene expression levels were determined by the 2^−∆∆Ct method with the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene used as an internal control. The experiments were conducted with four replicates for each sample. The data were expressed as mean ± standard error (SE) (n = 3). The qRT-PCR results were visualized using the software GraphPad prism (Version9.0.0).

Table 1.

Primer sequences for qRT-PCR

Gene	Forward primer (5ʹ-3ʹ)	Reverse primer (5ʹ-3ʹ)
GAPDH 1	GAGGGTAGTGAAGGCTGCTG	CACAACACGGTTGCTGTATC
POMC	AGAGCAGCAAATGCCAGGAC	TTCCAGCGAAAGTGGCTCAT
CPLX2	CAAGGACATGGGGAAGATGC	CCGCCTCCTTCTCCTCCTT
NPB	CTCTACTCGGTGGGCAGAGC	TCACGTCTGCTTTGCACTGG
HAPLN2	CAACTACCACGAGGCCAAGC	CGGGAGTTGATGATGGGGTA
NTS	CTCTACTCGGTGGGCAGAGC	TCACGTCTGCTTTGCACTGG
EGR4	CCTTAGCCGAGCCCAAAAAG	ATGTGGGTGGTGAGGTGGTC
TOX3	TGTCCACATGACGGATGCTC	ATGGGGAAGGAGTGGCTGAT
MSH4	CCAGTGGCCAACTTCCTTCA	AGCAGCATGTCCAGCATTGA

¹Gene GAPDH was used as the reference gene.

Results

Semen quality comparison and animal grouping

The detection of semen quality indicated that sperm motility was significantly different among sexually sensitive individuals. The individuals with sperm motility ≥7 were divided into a high-quality group (DH), and individuals with sperm motility ≤3 into a low-quality group (DL), and the remaining individuals (3 < sperm motility < 7) into medium-quality group (DM). The semen quality data of all three groups were shown in Table 2. The sperm motility, sperm viability, and sperm deformity rate of the DH group were significantly higher than those of the DL group (P < 0.001). Additionally, the testicular morphology observation under microscope showed that the number of sperm in the testicular lumen of the DH group was larger than that of the DL group, and the spermatozoa of DH group were closely arranged (Figure 1A and B). The testicular lumen of the DN group was mostly closed (Figure 1C), which was consistent with the results of semen quality test. Further, we selected three individuals from DH, DL, and DN group, respectively, and extracted the RNA from their hypothalamus and pituitary gland for DEGs identification.

Table 2.

Semen quality data of male ducks among groups

Semen parameters	High-semen-quality group (DH) (n = 17)	Medium-semen-quality group (DM) (n = 9)	Low-semen-quality group (DN) (n = 15)
Semen volume, μL	692.93 ± 260.61	597.78 ± 291.66	660.46 ± 243.94
Sperm concentration, ×109/ mL	6.44 ± 2.89	5.76 ± 1.59	6.12 ± 2.50
Sperm motility	7.20 ± 0.63A	5.32 ± 1.29BA	1.82 ± 0.86B
Sperm viability, %	85.2 ± 7.76aA	77.27 ± 10.05bA	43.58 ± 20.74B
Sperm deformity rate, %	13.37 ± 5.63aA	19.24 ± 7.74b	20.04 ± 6.69B

Note

P ^{A, B} < 0.001

P ^{a, b} < 0.05.

Figure 1.

The H&E staining of testis tissue of male ducks in different reproductive capacity groups. (A) High-semen-quality group, (B) low-semen-quality group, and (C) non-response group. H&E, hematoxylin, and eosin. Bar scale = 100 μm.

Identification of differentially expressed genes (DEGs) among groups

A total of 118.21 Gb of clean data were obtained from 18 hypothalamus and pituitary tissue samples by mRNA sequencing. The PCA results showed that hypothalamus samples of DhH, DhL, and DhN groups were separated from each other (Figure 2A), indicating significant differences in mRNA expression among these three groups. The pituitary samples of DpN group were significantly separated from those of DpH and DpL groups, and an intersection of pituitary samples was observed between DpH group and DpL group (Figure 2B). A total of 1,858 DEGs were identified from the hypothalamus tissue, including 371 DEGs (220 up-regulated, 151 down-regulated) in the comparison of DhH vs. DhL, 1,011 DEGs (916 up-regulated, 95 down-regulated) in DhH vs. DhN, and 476 DEGs (411 up-regulated, 65 down-regulated) in DhL vs. DhN (Figure 2C, Figure S1 and S2, and Supplementary Table S1). A total of 122 DEGs were identified from the pituitary tissue, of which 20 DEGs (20 up-regulated) were identified in the comparison of DpH vs. DpL, 82 DEGs (41 up-regulated, 41 down-regulated) in DpH vs. DpN, and 20 DEGs (5 up-regulated, 15 down-regulated) in DpL vs. DpN (Figure 2D, Supplementary Figures S1 and S2, and Supplementary Table S1).

Figure 2.

Expression of mRNAs. Principal component analysis (PCA) of 9 different samples from 3 reproductive capacity groups in the hypothalamus (A) and pituitary (B), respectively. Histograms of the number of up- and down-regulated DEGs in the hypothalamus (C) and pituitary (D) in the three comparisons of DH vs. DL, DH vs. DN, and DL vs. DN. Clustering heatmap of DEGs between hypothalamus (E) and pituitary (F) groups.

Hierarchical clustering analysis results of DEGs showed that the hypothalamus samples of different groups (DhH, DhL, and DhN) were distributed in different sub-clusters, but three biological replicates of each group were essentially distributed in the same sub-clusters, indicating that three biological replicates were satisfactory, and that pituitary samples of DpH and DpL groups were clustered together, which was consistent with the PCA results (Figure 2E and F). In addition, clustering analysis also showed that there were significantly fewer DEGs in the pituitary than in the hypothalamus. According to relative expression level (>50), the top 10 DEGs in each group were screened (Table 3). The above results demonstrated the reliability of screened DEGs.

Table 3.

The list of the top 10 DEGs between each group

Hypothalamus			Pituitary
DhH vs. DhL	DhH vs. DhN	DhL vs. DhN	DpH vs. DpL	DpH vs. DpN	DpL vs. DpN
POMC ↓	SERF2 ↑	SEBOX ↑	NTS ↑	SLC23A1 ↓	SLC6A19 ↓
CGA ↓	CHRNA2 ↑	UBE2L6 ↑	EGR4 ↑	MSH4 ↓	SLC39A10 ↑
ALDH1A1 ↓	CPLX2 ↑	STBD1 ↑	ABCB5 ↑	RXFP3 ↑	SLC23A1 ↓
CHRNA2 ↑	WIF1 ↓	CGA ↑	RXFP3 ↑	SLC22A31 ↓	LBX2 ↓
MARCO ↓	UBE2L6 ↑	RPUSD3 ↑	PDE6H ↓	HOPX ↑	STC2 ↑
MATN2 ↓	RPUSD3 ↑	RASGRF2 ↓	EGR3 ↑	NOX1 ↓	ABCG4 ↓
WIF1 ↓	NPB ↑	HAPLN2 ↑	TOX3 ↑	TRPM1 ↑	TRIM54 ↓
RAB27B ↑	CKS1B ↑	CKS1B ↑	EFHC1 ↓	ADAMTS12 ↑	QRICH2 ↓
STX1A ↑	LRFN1 ↑	GRIN2B ↓	MARCO ↓	ELF3 ↓	CHRNA10 ↓
EFHC1 ↓	TMEM132A ↑	KIF12 ↑	COL11A1 ↑	SMARCD3 ↓	CALB2 ↓

Note: ↑ means up-regulation, ↓ means down-regulation.

Functional enrichment analysis of DEGs

The GO and KEGG analyses were conducted to predict functions of DEGs (Supplementary Tables S2 and S3). The results showed that the 371 DEGs in hypothalamus in the comparison of DhH vs. DhL were mostly assigned to GO terms such as signal release, neurotransmitter transport, synapse formation, and exocytosis. Additionally, KEGG analysis showed that these 371 DEGs were mainly enriched in the pathways such as neuroactive ligand–receptor interaction, cAMP signaling, and synaptic vesicle cycle (Figure 3A). The 1,011 DEGs in the comparison of DhH vs. DhN were annotated to neurogenesis-related GO terms such as glial regeneration and differentiation, and mitochondrial inner membrane formation, their KEGG analysis showed that these 1,011 DEGs were mainly enriched in energy metabolism-related pathways such as amino acid metabolism and glucose metabolism (Figure 3B). The 476 DEGs in DhL vs. DhN were assigned to GO terms such as ribosome formation, cytoplasmic translation, and synapse ­plasticity, and their KEGG analysis demonstrated that these 476 DEGs were mainly enriched in nerve-related pathways such as synaptic vesicle cycle and energy metabolism pathways such as cholesterol metabolism and oxidative phosphorylation (Figure 3C).

Figure 3.

Top 30 GO terms in which DEGs were assigned and top 20 KEGG pathways significantly enriched with DEGs in 3 comparisons in the hypothalamus: (A) DhH vs. DhL, (B) DhH vs. DhN, and (C) DhL vs. DhN.

For the analysis of the pituitary samples, the number of DEGs annotated to GO terms and enriched in KEGG pathways was too small to be statistically significant due to the very limited number of DEGs identified from the comparison of three groups (Figure 4). However, it was worth noting that in comparison of DpH vs. DpN, relatively more DEGs were identified, and their KEGG analysis showed that these DEGs were mainly enriched in the neuroactive ligand–receptor interaction pathway, which was also enriched with DEGs in the hypothalamus sample analysis.

Figure 4.

Top 30 GO terms in which DEGs were assigned to and top 20 KEGG pathways significantly enriched with DEGs in 3 comparisons in the pituitary: (A) DpH vs. DpL, (B) DpH vs. DpN, and (C) DpL vs. DpN.

Interaction network construction of DEGs

To reveal the relationship of gene regulation between the hypothalamus and pituitary tissues, the highly expressed DEGs from two tissues in the comparison between different groups (DH, DL, and DN) were subjected to PPI network analysis, and the connection between the Top 10 key hub genes were presented in Figure 5. In DH vs. DL, genes including complexin 1 (CPLX1), member RAS oncogene family (RAB3A), syntaxin 1A (STX1A), synaptic vesicle glycoprotein 2A (SV2A), and complexin 2 (CPLX2) were aggregated into a highly concentrated network (Figure 5A), most of which were specifically expressed only in the brain, involved in synaptic vesicle synthesis, exocytosis, transport, and other biological processes. The sub-networks of DEGs identified in the comparison of DH vs. DN and DL vs. DN exhibited some shared DEGs such as ribosomal protein L23 (RPL23), ribosomal protein S28 (RPS28), ubiquitin A-52 residue ribosomal protein fusion product 1 (UBA52), and ribosomal protein S10 (RPS10), and these shared DEGs were identified as hub genes in two comparisons (Figure 5B and C). These hub genes were widely expressed in the body and were involved in encoding ribosomal and subiquitin proteins.

Figure 5.

PPI network and corresponding hub genes of DEGs in three comparisons: (A) DH vs. DL, (B) DH vs. DN, and (C) DL vs. DN. On the left is the mRNA–mRNA interaction networks constructed by analyzing the DEGs. The red and blue nodes represent up-regulated and down-regulated DEGs, respectively. On the right is the key sub-network of PPI network. The darker the red color, the more important the gene.

The GO and KEGG results showed that the neuroactive ligand–receptor interaction pathway was enriched with DEGs from almost all comparisons between groups. To further explore the regulatory relationships between the hypothalamus and pituitary DEGs, DEGs enriched in this pathway were extracted for PPI network analysis. In the comparison of DH vs. DL, 9 DEGs in the hypothalamus regulated 5 DEGs in the pituitary, which in turn affected semen quality (Figure 6). Similarly, in the comparison of DH vs. DN and DL vs. DN, a total of 22 DEGs in the hypothalamus regulated 21 pituitary DEGs, thus affecting the sexual response of male breeding ducks (Figure 6). Additionally, LPAR3 (lysophosphatidic acid receptor 3), DRD2 (dopamine receptor D2), and EGR4 were identified only in the comparison of DH vs. DL, indicating that these three genes were semen quality-specific. Similarly, FSHB (follicle stimulating hormone subunit beta), NPB (neuropeptide B), GABRB3 (gamma-aminobutyric acid type A receptor subunit beta3), PTGER4 (prostaglandin E receptor 4), and other genes were identified only in the comparison of DH vs. DN and DL vs. DN, indicating that these genes were sexual response-specific. Genes POMC (proopiomelanocortin), CGA (glycoprotein hormones, alpha polypeptide), TOX3 (TOX high mobility group box family member 3), and NTS (neurotensin) were identified in all three comparisons, suggesting that they might regulate both semen quality and sexual response.

Figure 6.

PPI network of DEGs enriched in neuroactive ligand–receptor interaction pathways by KEGG enrichment analysis.

Verification of candidate DEGs

Based on our bioinformatics analysis data and previous reports, 8 DEGs including POMC, CPLX2, NPB, HAPLN2 (hyaluronan and proteoglycan link protein 2), NTS, EGR4, TOX3, and MSH4 (muts homolog 4) were selected as candidate DEGs regulating the reproductive performances. The expression levels of the selected 8 DEGs were investigated by qRT-PCR, and qRT-PCR results were consistent with RNA-seq data, confirming the reliability of the candidate DEGs (Figure 7).

Figure 7.

Validation of candidate DEGs expression pattern by qPCR analysis. RNA-seq of genes in DH, DL, and DN groups in the hypothalamus (A) and pituitary (C), respectively. qRT-PCR results of DEGs in DH, DL, and DN groups in the hypothalamus (B) and pituitary (D), respectively. The GAPDH gene was used as reference gene. Data are shown as means ± SE (n = 3). *P < 0.05; **P < 0.01; ***P < 0.001, and ****P < 0.0001.

In the hypothalamus, the expression level of POMC gene in the DhH group was significantly lower than that in the DhL group and the DhN group (P < 0.001), whereas the expression level of CPLX2 gene was significantly higher in the DhH group than in the DhL and the DhN group (P < 0.01), indicating that POMC and CPLX2 genes were related to semen quality. The expression level of HAPLN2 gene was significantly lower in the DhN group than in the response group (DhH, DhL) (P < 0.01), suggesting that HAPLN2 gene might be related to sexual response. In pituitary, the expression of TOX3 and EGR4 genes was significantly higher in the DpH group than in other groups (P < 0.01), and the expression of MSH4 in the DpN group was significantly higher than that in other groups (P < 0.01).

Discussion

The semen quality is an important trait for poultry production, and it determines the economic benefits and the breeding progression. There are significant differences in semen quality among different individuals and breeds. In this study, a large number of semen sample data were collected. These male ducks were divided into high-quality (DH) and low-quality (DL) groups in terms of semen quality, as well as non-response (DN) group in terms of sexual behavior. The HPG axis exhibits important neuro-hormone regulatory functions, and it is involved in biological processes related to avian reproduction. In this study, the mRNA transcriptomes in the hypothalamus and pituitary tissues have been investigated, based on which, DEGs were identified in the hypothalamus and pituitary tissues in the three comparisons (DH vs. DL, DH vs. DN, and DL vs. DN), respectively.

Notably, a total of 1,858 DEGs were identified from hypothalamus samples in the 3 comparisons, while only 122 DEGs were identified from pituitary samples. Hypothalamus is a coordinating center that regulates important physiological functions such as endocrine activity, appetite, and energy balance. The hypothalamus delivers precise signals from some specific neurons to the pituitary gland that further releases hormones to the most tissues of endocrine system. In return, various hormones produced by the pituitary can feedback regulate the hypothalamus and affect its functional activities. The hypothalamus–pituitary system is important for controlling gonad development and sex hormone secretion, and thus the transcriptome of the poultry HPG axis has been extensively studied to explore its regulatory effects on reproductive traits such as egg production and broodiness. In geese, the transcriptome comparison of the hypothalamus and pituitary tissues in individuals with high and low egg production capacities revealed more DEGs in the hypothalamus than in pituitary tissue, which was consistent with our results (Wu et al., 2020). However, another study of the transcriptome differences in HPG tissues between laying and initial nesting geese showed the number of DEGs in the pituitary was significantly larger than that in the hypothalamus (Liu et al., 2018).

Our PCA and clustering heat map results of DEGs showed high consistency in biological replicates of the hypothalamus samples and significant differences in gene expression among three sample groups (DhH, DhL, and DhN). In the pituitary tissue, the significant differences in gene expression between the non-response group (DpN) and the response group (DpH and DpL) were observed, while the differences between the DpH group and the DpL group were not significant. These results indicated that hypothalamus might be the main upstream regulators to modulate duck reproductive performances including semen quality and sexual behavior, which might explain why fewer DEGs were identified from the pituitary tissue. Our identified DEGs would contribute to our understanding of genetic mechanism underlying reproductive performance and sexual behavior in male ducks.

Our GO and KEGG enrichment analyses found that most of DEGs were enriched in biological processes related to nerves and synapses, mitochondrial inner membrane formation pathway, and ribosome structure pathway. Consistently, our PPI network analysis also showed that the hub genes in DH vs. DL were involved in synaptic vesicle synthesis, exocytosis, and transport, and hub DEGs in DH vs. DN and DL vs. DN were involved in coding ribosomes and ubiquitin proteins. The above results demonstrated that the biological processes such as neuromodulation, energy metabolism, protein expression, and modification were very important for the molecular regulation of reproductive performance in male ducks.

Notably, the neuroactive ligand–receptor interaction pathway was significantly enriched with DEGs in all three comparisons in the duck hypothalamus. Transcriptome analyses of pigs (Xu et al., 2015), goats (Su et al., 2018), zebrafish (Chen et al., 2019), and poultry (Tao et al., 2017; Zhang et al., 2019) have revealed the important role of this pathway in controlling reproductive performance. Our PPI analysis showed that the genes affecting semen quality such as NTS, TOX3, and EGR4 in the pituitarium were mainly regulated by POMC, CGA, and LPAR3 in the hypothalamus. In the comparison of response groups vs. non-response group, the genes affecting sexual behavior such as NTS, NPB, GABRB3, PRLR (prolactin receptor), POMC, and CGA in the hypothalamus regulated the genes such as FSHB, PTGER4, GABRA3 (gamma-aminobutyric acid type A receptor subunit alpha3), and TOX3 in the pituitarium.

Most of the above-mentioned genes have been reported to be related to animal reproductive performance. POMC gene is closely related to the feeding behavior during the reproductive processes in parenting female Zebra Finch and nesting geese (Toda et al., 2017; Liu et al., 2018; Kumari et al., 2022). Some chicken POMC SNPs are significantly associated with fertilization rate and hatching rate of eggs (Liu et al., 2020). Several studies have reported that TOX3 and LPAR3 play a role in the biological processes of progesterone and estrogen synthesis (Diao et al., 2015; Man et al., 2020). The well-studied FSHB gene plays an important role in spermatogenesis (Oduwole et al., 2018). GABRB3 and GABRA3 genes encode neurotransmitter receptors (Tang et al., 2021). PTGER4 is a prostaglandin receptor, and it plays a role in follicle growth (El-Nefiawy et al., 2005). EGR4 plays an important role in the regulation of spermatogenesis in male mammals, such as spermatogenesis impairment (Sung et al., 2017), oligozoospermia (Tourtellotte et al., 1999), and cryptorchidism (Hadziselimovic et al., 2009). The enrichment of above-mentioned genes in the neuroactive ligand–receptor interaction pathway suggested that this pathway was one of the important regulatory pathways in the avian HPG axis, and that this pathway might directly or indirectly regulate semen quality and the initiation of sexual behavior through the neuroendocrine regulation of sex hormones.

In this study, MSH4 had the similar expression pattern to POMC, namely, the expression level in DpH group was significantly lower than that in other two groups (DpL and DpN) (P < 0.001). The function of MSH4 has been reported to be related to male infertility and gonadal failure in mammals (Tang et al., 2020; Akbari et al., 2021; Wyrwoll et al., 2021). Based on these findings, we speculated that MSH4 might regulate the semen quality and sexual behavior of male poultry. CPLX2 is one of four members of the complexin family encoding presynaptic regulatory proteins, and CPLX2 regulates synaptic neurotransmitter release, thereby influencing synaptic signaling, synaptic plasticity, and neuronal network function (Reim et al., 2005). The current research on CPLX2 gene is mostly related to psychiatric diseases such as Parkinson’s disease, Alzheimer’s disease, and schizophrenia (Hass et al., 2015), and no evidence supports that this gene directly regulates reproduction-related traits. Our study found that the expression of CPLX2 was significantly up-regulated in DhH, but down-regulated in DhL and DhN groups, which might be related to the regulation of spermatogenesis and sexual behavior by neuromodulation. Our study identified DEG HAPLN2, and this gene has been reported to be a contributor to the pathological processes in neurological disorders (Wang et al., 2019). HAPLN2, also known as brain-derived link protein1, is vital for neuronal conductivity and extracellular matrix formation, and it is mainly expressed in the brain. Similar to that of CPLX2, the expression level of HAPLN2 was significantly lower in the DhN group than in the other two groups (P < 0.01), indicating that it may play a role in the biological process related to the sexual behavior of breeding ducks. However, the functions of these genes related to duck reproductive performance remain to be further investigated and confirmed.

Conclusion

In conclusion, a large number of DEGs putatively related to semen quality and sexual behavior were identified in the hypothalamus and pituitary gland of male ducks, and most of these genes were enriched in synapse-related pathways. Comprehensive analysis showed that the neuroactive ligand–receptor interaction pathway and six candidate DEGs, including POMC, CPLX2, HAPLN2, EGR4, TOX3, and MSH4, were critical for semen quality and sexual behavior in ducks. Our identified genes can be used as candidates for further exploring selectable markers in ducks to improve their reproductive performance. Our findings provide new insights into the molecular mechanisms underlying neuroendocrine regulation in male ducks with different reproductive capacity.

Glossary

Abbreviations

AI

artificial insemination

CCNF

cyclin F

CGA

glycoprotein hormones: alpha polypeptide

CPLX1

complexin 1

CPLX2

complexin 2

COX7B

cytochrome c oxidase subunit 7B

DEG

differentially expressed gene

DRD2

dopamine receptor D2

E2

17β-estradiol

EGR4

early growth response 4

FPKM

fragments per kilobase of exon per million fragments mapped

FSH

follicle-stimulating hormone

FSHB

follicle-stimulating hormone subunit beta

GABRA3

gamma-aminobutyric acid type A receptor subunit alpha3

GABRB3

gamma-aminobutyric acid type A receptor subunit beta3

GAPDH

glyceraldehyde-3-phosphate dehydrogenase

GnH

gonadotropins

GnRH

gonadotropin-releasing hormone

HAPLN2

hyaluronan and proteoglycan link protein 2

HPG

hypothalamus–pituitarium–gonadal

HPGDS

hematopoietic prostaglandin D synthase

LH

luteinizing hormone

LPAR3

lysophosphatidic acid receptor 3

MCC

maximal clique centrality

MSH4

muts homolog 4

NPB

neuropeptide B

NTS

neurotensin

P4

progesterone

PCA

principal component analysis

POMC

proopiomelanocortin

PPI

protein–protein interaction

PRLR

prolactin receptor

PTGER4

prostaglandin E receptor 4

PTGDS

prostaglandin D2 synthase

RAB3A

member RAS oncogene family

RPL23

ribosomal protein L23

RPS10

ribosomal protein S10

RPS28

ribosomal protein S28

SE

standard error

STX1A

syntaxin 1A

SV2A

synaptic vesicle glycoprotein 2A

T

testosterone

TOX3

tox high mobility group box family member 3

UBA52

ubiquitin A-52 residue ribosomal protein fusion product 1

WNT2

wnt family member 2

Conflicts of Interest Statement

The authors declare no conflicts of interest.

Data Availability Statement

All data generated or analyzed during this study are included in this published article.