Abstract

Background

   Incubation behaviour, an instinct for natural breeding in poultry, is
   strictly controlled by the central nervous system and multiple
   neuroendocrine hormones and neurotransmitters, and is closely
   associated with the cessation of egg laying. Therefore, it is essential
   for the commercial poultry industry to clarify the molecular regulation
   mechanism of incubation behaviour. Here, we used high-throughput
   sequencing technology to examine the pituitary transcriptome of
   Changshun green-shell laying hen, a local breed from Guizhou province,
   China, with strong broodiness, in two reproductive stages, including
   egg-laying phase (LP) and incubation phase (BP). We also analyze the
   differences in gene expression during the transition from egg-laying to
   incubation, and identify critical pathways and candidate genes involved
   in controlling the incubation behaviour in the pituitary.

Results

   In this study, we demonstrated that a total of 2089 differently
   expressed genes (DEGs) were identified in the pituitary, including 842
   up-regulated and 1247 down-regulated genes. Kyoto Encyclopedia of Genes
   and Genomes (KEGG) analysis revealed that steroid biosynthesis pathway
   and neuroactive ligand-receptor interaction were significantly enriched
   based on DEGs commonly identified in pituitary. Further analysis
   revealed that SRC, ITGB4, ITGB3, PIK3R3 and DRD2 may play crucial roles
   in the regulation of incubation behaviour.

Conclusions

   We identified 2089 DEGs and the key signaling pathways which may be
   closely correlated with incubation in Changshun green-shell laying
   hens, and clarified the molecular regulation mechanism of incubation
   behaviour. Our results indicate the complexity and variety of
   differences in reproductive behaviour of different chicken breeds.

Supplementary Information

   The online version contains supplementary material available at
   10.1186/s12864-024-10233-1.

   Keywords: Changshun green-shell laying hens, Pituitary, Transcriptome
   analysis, Laying period, Incubation period

Introduction

   Incubation behaviour, also colloquially known as broodiness, is a
   behaviour commonly occurring in domestic poultry. In domestic chickens,
   incubation behaviour consists of persistent nesting, turning and
   retrieval of eggs, characteristic clucking, and defense of the nest
   [[41]1, [42]2]. It can cause atrophy of the fallopian tubes and
   ovaries, and the subsequent cessation of egg laying, leading to
   economic losses for the domestic poultry industry [[43]1]. Although the
   mechanism of incubation behaviour has been extensively investigated in
   domestic chickens, the exact regulatory mechanisms remain elusive.
   Usually, incubation behaviour is known to be strictly controlled by the
   central nervous system and is closely associated with multiple
   neuroendocrine hormones and neurotransmitters.

   During the transition from egg-laying to incubation in domestic
   chickens, the levels of hormones change markedly, and prolactin (PRL)
   is considered to be a main factor induced during incubation. The onset
   and maintenance of incubation behaviour in chickens are demonstrated to
   be closely correlated with a characteristic increase in plasma PRL
   level [[44]3–[45]7]. Meanwhile, the concentrations of luteinizing
   hormone (LH) and steroid hormones in plasma decrease dramatically
   during incubation, leading to regression of the ovary [[46]5]. Once the
   young chicks have hatched, the plasma PRL level subsequently declines
   dramatically, and the concentrations of LH and estradiol increase
   gradually [[47]5, [48]8–[49]10]. Increased PRL and decreased LH
   secretion in incubating chickens are found to be correlated with
   increased hypothalamic neuropeptide vasoactive intestinal polypeptide
   (VIP) and decreased hypothalamic gonadotropin-releasing hormone (GnRH),
   respectively. The hypothalamic VIP is a physiological PRL-releasing
   hormone in chickens. It can not only increase the gene expression of
   PRL in pituitary gland, but also induce the release of PRL by acting
   directly on the the lactotrophs of pituitary gland [[50]11–[51]14]. In
   addition, the neurotransmitters are also thought to be involved in the
   regulation of avian incubation behaviour. The neurotransmitter dopamine
   (DA) has both an inhibitory and stimulatory effect on the secretion of
   avian PRL, depending on the subtype of DA receptor [[52]15, [53]16].
   However, 5-hydroxytryptophan (5-HTP) and dynorphin are reported to
   stimulate VIP and PRL release, respectively [[54]17, [55]18].

   In addition to neuroendocrine control, many investigations on the
   genetic control of incubation behaviour have been reported in the past
   decades. However, these investigations appear to have led to
   conflicting observations. Previous studies have extensively reported
   that incubation behaviour is a polygenic trait, and is controlled by
   more than one independent autosomal gene [[56]19, [57]20]. While,
   subsequent investigations indicated that incubation behaviour might be
   controlled, at least to a large extent, by Z-linked genes, which seems
   to be contradictory to previous findings [[58]21, [59]22].
   Interestingly, the quantitative trait loci (QTL) analysis indicated
   that genetic loci affecting incubation behaviour are not located on the
   Z chromosome, but on chromosomes 1, 5, 8, 13, 18 and 19 and linkage
   group E22C19W28 [[60]23]. Overall, it is now generally accepted that
   more than one locus controls the incubation behaviour in domestic
   chickens.

   Changshun green-shell laying hen is an indigenous breed of chicken in
   Guizhou province, China. It is known to be a dual purpose egg- and
   meat-type chicken, and their eggs are considered to be superior in
   local markets for their better appearance, higher contents of protein,
   better amino acid composition, and lower fat and cholesterol content
   [[61]24]. Although its strong tendency for broodiness significantly
   reduces egg production, and has restricted development of the Changshun
   chicken industry, the stable laying-incubation cycles and easily
   identifiable characteristics of broodiness make it an ideal model to
   study incubation behaviour and the molecular regulatory mechanism of
   poultry broodiness.

   In recent years, RNA sequencing (RNA-Seq) has been widely used to
   investigate the dynamic gene expression profiles of key genes in the
   hypothalamic-pituitary-gonadal (HPG) axis of poultry between the laying
   period and incubation period [[62]25–[63]27]. A total of 110
   differentially expressed genes (DEGs) were screened in the pituitary
   between laying and nesting geese, and the PRL, oxytocin-neurophysin
   (OXT), chordin-like protein 1 (CHRDL1) and growth hormone (GH),
   expressed in the pituitary gland, are considered as new candidate
   molecules involved in incubation in geese [[64]25]. In ducks, 398 DEGs
   were identified in pituitary during the transition from egg-laying to
   incubation, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and
   Genomes (KEGG) analysis further indicated that neuroactive
   ligand-receptor interaction pathway, calcium signaling pathway, and
   response to steroid hormones biological process are critical for the
   transition from egg-laying to incubation phase of ducks [[65]27].
   Considering the complexity and variety of differences in reproductive
   behaviour of different poultry breeds, it is essential to understand
   the genetic basis of the transition from egg-laying to incubation phase
   of chickens at transcriptome level.

   A large number of studies have focused on the identification of
   potential genes and gene regulatory networks associated with high rates
   of egg production in chicken pituitary [[66]28, [67]29]. However, there
   are few studies on pituitary transcriptome analysis of incubation
   behaviour in chickens. Here, we systematically examined the pituitary
   transcriptome of Changshun green-shell laying hens at LP and BP through
   high-throughput sequencing and concentrated on the analysis of the
   differences in gene expression during the transition from LP to BP.
   Candidate genes involved in incubation were screened out, and related
   pathways were evaluated through KEGG enrichment and GO enrichment
   analysis. This study further reveals the complexity and variety of
   differences in reproductive behaviour of different chicken breeds and
   provide new insights into poultry reproductive behaviour.

Materials and methods

Ethics statement

   The animal experiments were performed under the guidelines published in
   the Guide for the Care and Use of Laboratory Animals of the Ministry of
   Science and Technology of the People’s Republic of China. All methods
   strictly follow ARRIVE (Animal Research: Reporting of In Vivo
   Experiments) guidelines 2.0. The experimental procedures in this study
   were approved by the Animal Ethics Committee of the College of
   Biological Science and Agriculture, Qiannan Normal University for
   Nationalities, Duyun, China (AEC No. QNUN2020018).

Animals and sample preparation

   The experimental Changshun green-shell laying hens were obtained from
   Changshun Sanyuan Agricultural Development Co., Ltd., China and were
   then raised in the poultry breeding farm of Qiannan Normal University
   for Nationalities according to the standard procedure. 100 females and
   15 males were reared on the floor with litter and artificial nest and
   exposed to natural temperature conditions. An infrared video camera was
   set up above laying nest. The incubation behaviours were then recorded
   daily in the annual reproductive cycle. The individuals with high
   fertility were deemed to be LP birds. The hens which sat in the nest
   while exhibiting characteristic clucking, aggressive or defensive
   behaviour for at least one week were allocated as the BP group. Six
   hens from LP and BP stages were randomly chosen according to their
   behaviours and the ovary and anterior and posterior pituitary gland
   samples were quickly collected after euthanization by exsanguination
   under sodium pentobarbital anesthesia (60 mg/kg), respectively. The
   morphologic characteristics of the ovaries in the study were mainly
   used to further evaluate individual physiological stage of chickens
   (Fig. [68]1). All the anterior and posterior pituitary samples were
   immediately frozen in liquid nitrogen and then stored at -80 °C until
   total RNA extraction for transcriptome sequencing. Three pituitary
   samples from each of the LP and BP groups were randomly chosen for
   transcriptome sequencing according to the morphologic characteristics
   of the ovary.

Fig. 1.

   Fig. 1
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   Ovary morphology of hens at LP (A) and BP (B)

RNA extraction, cDNA library construction, and mRNA sequencing

   Total RNA from three individuals in each of the LP and BP groups was
   extracted from the pituitary samples using the Trizol reagent (Life
   technologies, California, USA), according to the manufacturer’s
   instruction manual. The concentration and quality of the total RNA were
   determined using NanoDrop 2000 (Thermo Fisher Scientific, Wilmington,
   DE). RNA integrity was evaluated using the RNA Nano 6000 Assay Kit of
   the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA).
   Sequencing libraries were generated using Hieff NGS Ultima Dual-mode
   mRNA Library Prep Kit for Illumina (Yeasen Biotechnology (Shanghai)
   Co., Ltd.) following manufacturer’s recommendations. The libraries were
   sequenced on an Illumina NovaSeq6000 platform using 150 bp paired-end
   reads, according to the manufacturer’s instructions.

Data analysis

   The raw reads were further processed with a bioinformatics
   pipelinetool, BMKCloud ([70]www.biocloud.net) online platform. Raw
   sequences were firstly transformed into clean reads through removing
   adaptor sequences and discarding low-quality sequences. Hisat2 tools
   software (v2.2.1, -dta -p 6 -max-intronlen 5,000,000) was then used to
   map against the chicken reference genome
   (Gallus_gallus.GRCg6a_release106.genome.fa) [[71]30]. Both known and
   novel transcripts from Hisat2 alignment results were detected by the
   method of the StringTie Reference Annotation Based Transcript (v2.2.1,
   -merge -F 0.1 -T 0.1) [[72]31]. The KEGG
   ([73]http://www.genome.jp/kegg/), National Center for Biotechnology
   Information for non-redundant proteins
   ([74]ftp://ftp.ncbi.nih.gov/blast/db/), Eukaryotic Orthologous Groups
   of proteins ([75]http://www.ncbi.nlm.nih.gov/KOG/), Protein family
   ([76]http://pfam.xfam.org/), GO ([77]http://www.geneontology.org/) and
   Swiss-Prot ([78]http://www.uniprot.org/) databases were used to
   annotate the gene function. The principal component analysis (PCA) was
   applied to assess the similarity between the samples.

Identification of DEGs

   Differential expression analysis of between the two groups was
   performed using DESeq2 (v1.30.1) [[79]32]. DESeq2 provides statistical
   routines for determining differential expression in digital gene
   expression data using a model based on the negative binomial
   distribution. The resulting P values were adjusted using the Benjamini
   and Hochberg’s approach for controlling the false discovery rate. Genes
   with an adjusted P-value < 0.05 & Fold Change ≥ 1.5 found by DESeq2
   were assigned as differentially expressed.

KEGG pathway, GO enrichment analysis and protein-protein interaction

   GO enrichment analysis of DEGs was implemented by the clusterProfiler
   package (v3.16.1) based Wallenius non-central hyper-geometric
   distribution, and KOBAS software (v3.0) was used to test the
   statistical enrichment of DEGs in KEGG pathways [[80]33, [81]34]. The
   protein-protein interaction (PPI) analysis was performed using
   Cytoscape [[82]35].

Gene expression analysis by quantitative real-time PCR

   Using total RNA, 1 µg was reverse transcribed to cDNA using the Prime
   Script RT reagent Kit (Takara, Dalian, China). Four genes of interest,
   including PRL, BMP5, DHCR7 and SC5D, were selected to validate the
   RNA-seq results. Beta-actin was chosen as an internal reference gene
   for the normalization of expression levels. The primers used in the
   quantitative real-time PCR (qRT-PCR) were designed using Primer 5
   (Table [83]S1). Each assay was carried out in triplicate. The relative
   mRNA expression levels were calculated by the 2^−ΔΔCt method [[84]36].

Statistical analysis

   Statistical analysis was performed using the R software (v4.0.2, R
   Development Core Team 2019). Data were analyzed using the Student’s
   t-test after testing for the homogeneity of variance with Levene’s
   test. All data are presented as the mean ± SD, and statistical
   significance is shown as *P < 0.05.

Results

RNA sequencing quality assessment

   A total of six libraries were constructed from the pituitary and
   sequenced using Illumina NovaSeq platform. The reads of transcriptome
   data and their statistical analysis are shown in Table [85]1. The clean
   reads from each library averaged more than 19 million. The base
   percentage of the Q20 (base sequencing error probability < 1%) and Q30
   (base sequencing error probability < 0.1%) ranged from 98.15 to 98.46%
   and 94.72–95.42%, respectively, indicating high accuracy of the
   sequencing. The GC content of all pituitary samples ranged from 47.98
   to 49.54%. The above results showed that the sequencing data was
   suitable for subsequent data analysis.

Table 1.

   Quality metrics of transcripts in pituitary
   Sample Clean reads Clean bases Q20 (%) Q30 (%) GC content (%) Total
   mapped Multiple mapped Uniquely mapped
   LP1 21,699,331 6,489,415,358 98.20 94.77 49.15 20,291,871 (93.51%)
   398,685 (1.84%) 19,893,187 (91.68%)
   LP2 23,189,413 6,932,116,122 98.33 95.10 49.24 21,704,639 (93.60%)
   461,979 (1.99%) 21,242,661 (91.60%)
   LP3 19,290,472 5,767,577,132 98.15 94.72 49.54 17,998,306 (93.30%)
   440,208 (2.28%) 17,558,098 (91.02%)
   BP1 24,468,336 7,318,629,774 98.43 95.27 47.98 23,215,360 (94.88%)
   741,255 (3.03%) 22,474,105 (91.85%)
   BP2 20,122,438 6,020,221,454 98.46 95.42 48.47 18,942,509 (94.14%)
   671,406 (3.34%) 18,271,104 (90.80%)
   BP3 21,727,047 6,500,165,130 98.40 95.27 48.79 20,484,626 (94.28%)
   650,102 (2.99%) 19,834,525 (91.29%)
   [86]Open in a new tab

   Note: Q20 and Q30 represent error rate less than 1% and 0.1%,
   respectively. GC content presents the percentage of G and C bases in
   clean data

Transcriptome alignment

   As shown in Table [87]1, the mapping rate of the clean reads from six
   pituitary samples ranged from 93.30 to 94.88%. The percentage of
   multiple and uniquely mapped reads in clean reads ranged from 1.84 to
   3.34% and 90.80–92.22%, respectively. The results indicated that the
   transcriptome data were reliable and suitable for subsequent analysis.

Differentially expressed genes

   Principal component analysis (PCA) was first conducted to analyze the
   six samples in LP and BP. As shown in Fig. [88]2A, the pituitary
   samples in LP and BP groups were separated from each other, indicating
   an obvious difference in mRNA expression between these two groups. The
   correlation coefficient of gene expression levels between samples
   demonstrated good sample repeatability in each group (Fig. [89]2B). A
   comparison of the gene expression showed that a total of 2089 DEGs were
   identified in the pituitary, including 842 up-regulated and 1247
   down-regulated genes in the BP group (Fig. [90]3 and Table [91]S2).
   Subsequently, hierarchical clustering analysis was carried out on the
   DEGs. We observed that the pituitary samples of LP and BP groups were
   distributed in different sub-clusters, but the three biological
   replicates from each group all clustered closely together. The heatmap
   visually reflected the expression patterns of the genes in the samples,
   indicating the reliability of screened DEGs (Figure [92]S1).

Fig. 2.

   [93]Fig. 2
   [94]Open in a new tab

   Features of sequencing data. (A) PCA score plot of pituitary
   transcriptomes. Blue and red nodes represent individuals from
   egg-laying phase (LP) and incubation phase (BP), respectively. (B)
   Pearson correlation analysis of LP and BP samples. LP1, LP2 and LP3 are
   pituitary samples of egg-laying phase hens, and BP1, BP2 and BP3 are
   pituitary samples of incubation metaphase hens

Fig. 3.

   [95]Fig. 3
   [96]Open in a new tab

   Volcano map of all expressed genes. The longitudinal and horizontal
   coordinates represent the statistical significance of the changes in
   gene expression and the fold changes of genes between the LP and BP
   groups, respectively. Each dot in the map represents one gene. Red and
   blue represent significantly up- and down-regulated genes,
   respectively. Gray represents genes showing no significant difference

KEGG pathway and GO enrichment analysis

   The KEGG pathway enrichment analysis was performed to further identify
   the major biochemical, metabolic, and signal transduction pathways of
   the DEGs. The top 20 significantly enriched KEGG pathways are listed in
   Fig. [97]4. The ten most enriched pathways were steroid biosynthesis,
   focal adhesion, MAPK signaling pathway, neuroactive ligand-receptor
   interaction, vascular smooth muscle contraction, regulation of actin
   cytoskeleton, TGF-beta signaling pathway, glutathione metabolism,
   ECM-receptor interaction and axon guidance. Notably, valine, leucine
   and isoleucine biosynthesis had the largest enrichment factor.
   Subsequently, the DEGs were annotated with Gene Ontology (GO) database
   into three main categories namely biological process (BP), cellular
   component (CC) and molecular function (MF) and 31 subcategories
   (Fig. [98]5 and Table [99]S3). Based on the GO classification, 22
   subcategories were assigned to the BP, 10 and 4 subcategories to the MF
   and CC, respectively (Table [100]S3).

Fig. 4.

   [101]Fig. 4
   [102]Open in a new tab

   KEGG pathway enrichment analysis of DEGs in pituitary. Bubble charts
   represent the top 20 significantly enriched KEGG pathways (with
   smallest q value). Each circle represents a KEGG pathway. Size of each
   circle represents the number of DEGs in each pathway and the colour
   represents the q value of each pathway. The enrichment factor is the
   ratio of the number of DEGs enriched in the pathway compared to the
   total number of all annotated genes enriched in this pathway

Fig. 5.

   [103]Fig. 5
   [104]Open in a new tab

   GO classification of DEGs identified in pituitary. The longitudinal
   coordinates represent the number of DEGs annotated to the term. The
   horizontal coordinates represent the GO terms and classifications

Interaction network construction of DEGs

   To further explore the biological characteristics of these DEGs from
   the pituitary, an analysis of protein-protein interaction (PPI)
   networks was performed (Fig. [105]6). The PPI networks were visualized
   by Cytoscape based on information gained up to 3 levels of functional
   analysis: fold change of genes, PPI and KEGG pathway. The PPI network
   of DEGs consisted of 209 nodes and 1252 edges. Functional analysis
   further indicated that the PPI network was mainly enriched into 10
   important pathways including steroid biosynthesis, neuroactive
   ligand-receptor interaction, MAPK signaling pathway, ECM-receptor
   interaction, glutathione metabolism, focal adhesion, regulation of
   actin cytoskeleton, vascular smooth muscle contraction, axon guidance
   and TGF-beta signaling pathway. The top five genes with the highest
   degree in the PPI network were SRC, ITGB4, ITGB3, PIK3R3 and DRD2,
   which may play important roles in mediating the transition from
   egg-laying to incubation in chickens.

Fig. 6.

   [106]Fig. 6
   [107]Open in a new tab

   PPI networks of DEGs in pituitary. Circle nodes for genes/proteins,
   rectangle nodes for KEGG pathway. In case of fold change analysis,
   genes/proteins were coloured in red (upregulation) and green
   (downregulation). Interactions are shown as solid lines between
   genes/proteins, and edges of KEGG pathway in dashed lines

Verification of DEGs by qRT-PCR

   To validate the RNA-seq results, four DEGs identified in pituitary,
   including PRL, BMP5, DHCR7 and SC5D, were selected for qRT-PCR
   analysis. While there are clear differences in the expression levels of
   these four genes, the expression trends determined by the qRT-PCR were
   consistent with the RNA-Seq results, indicating that the RNA-seq
   results were reliable (Fig. [108]7).

Fig. 7.

   Fig. 7
   [109]Open in a new tab

   qRT-PCR validation of DEGs. The relative mRNA expression levels were
   calculated by the 2^−ΔΔCt method. The results were expressed as
   mean ± SD. * for p < 0.05

Discussion

   Broodiness is known to be a complicated maternal behaviour which
   consists of cessation of egg laying, the incubation of eggs, and care
   of the young. It therefore becomes advantageous to clarify the
   molecular mechanism of the broodiness in poultry. The pituitary, as a
   key component of the HPG system, plays a critical role in chicken
   reproductive performance. In this study, an analysis of whole pituitary
   transcriptome differences was performed to explore the molecular
   mechanism underlying the transition from laying to incubation in
   chickens. The obtained pituitary RNA sequencing data will help us to
   further understand the biological processes that regulate these two
   reproductive behaviours.

   The incubation behaviour of poultry is mainly affected by the levels of
   PRL and steroid hormones in plasma. In 1930s, PRL as a hormone of the
   anterior pituitary was firstly identified to stimulate incubation
   behaviour in pigeons and chickens [[110]37, [111]38]. PRL has been
   known to play a fundamental role in the initiation and maintenance of
   incubation behaviour in poultry. As expected, transcriptome sequencing
   revealed that the PRL gene was one of the most active DEG in the
   pituitary, exhibiting an extremely high mRNA level in BP birds.
   Although it is well established that a characteristic increase of PRL
   in plasma is closely correlated with the initiation and maintenance of
   incubation behaviour in domestic and wild birds, the plasma PRL levels
   seem to be different between precocial and altricial birds. It was
   reported that the PRL levels in precocial chickens immediately
   decreased following the hatching of chicks and can be modified by
   removal of newly hatched chicks from hens [[112]39]. Interestingly,
   plasma PRL levels in altricial birds seemed to decline more slowly
   after hatching, presumably because brooding by the parent is imperative
   for young chicks to survive to fledging.

   In addition to PRL, we observed that the expression levels of other
   hormone-related genes in pituitary such as PGR, RLN3, NR5A1 and GRP
   sharply declined in incubating chickens. Similarly, these genes are
   involved in the initiation and maintenance of incubation behaviour in
   chickens. PGR as a member of the nuclear receptor superfamily is a
   specific steroid hormone receptor. The ligand progesterone, a key
   steroid hormone produced by granulosa cells, can activate PGR and then
   together they regulate reproductive cycles [[113]40]. RLN3 is the most
   recently identified member of the relaxin peptide family which may be
   involved in female reproduction in chickens [[114]41]. It is highly
   expressed in the pituitary with lower abundance in the hypothalamus,
   hindbrain, spinal cord and ovary [[115]41]. The expression of RLN3 is
   tightly regulated by GnRH, corticotropin-releasing hormone and sex
   steroid hormone (E2) [[116]41]. The relevant literature suggests that
   RLN3 might be involved in modulating a range of interrelated functions
   such as responses to stress, memory, circadian rhythm, sleep/wake
   states, food intake, metabolism, satiety and neuroendocrine function
   [[117]42–[118]47]. Meanwhile, preliminary studies have indicated a
   modulatory role for RLN3 as a pituitary hormone in reproduction and
   associated behaviour. Transcriptome analysis showed that RLN3 might be
   related to the transition from laying to brooding in chickens [[119]27,
   [120]48]. Both intracerebroventricular and intraparaventricular
   administration of RLN3 in animals significantly increased the plasma
   levels of LH, and this effect was blocked by administration of a
   peripheral GnRH antagonist [[121]49]. GRP is predominantly and
   abundantly expressed in the anterior pituitary gland and weakly
   expressed in other tissues in chickens, including the testes, ovary,
   proventriculus, telencephalon and hypothalamus [[122]50]. In accordance
   with their wide distribution, GRP is reported to be involved in many
   physiological processes in poultry, such as circadian clock, food
   intake, itch sensation, proventriculus acid secretion, gallbladder
   motility, crop-emptying rate, pancreatic secretion [[123]50–[124]57].
   Additionally, evidence has been accumulating to support the notion that
   pituitary GRP as a novel hormone in chickens may work together with GRP
   from other tissues to act in a coordinated fashion to regulate
   reproduction [[125]50]. NR5A1 is an enigmatic orphan receptor essential
   for the sexual development and reproduction in mammals [[126]58,
   [127]59]. Furthermore, it is known to be involved in the expression of
   pituitary gonadotropic hormone LH and follicle-stimulating hormone
   (FSH) and the synthesis of progesterone, androgens, estrogens and
   steroidogenesis [[128]60].

   Steroid biosynthesis plays an important role in the transition from the
   laying to incubation phases in chickens. It usually decreases sharply
   during the transition from laying to incubation in poultry.
   Consistently, the steroid biosynthesis pathway was observed to show the
   most significant changes during the transition from laying to
   incubation. In this study, eleven DEGs involved in steroid
   biosynthesis, including DHCR7, LIPA, DHCR24, SQLE, FDFT1, HSD17B7, LSS,
   CYP51A1, SC5D, NSDHL and MSMO1, are observed to decrease sharply during
   the transition from laying to incubation. Furthermore, the neuroactive
   ligand-receptor interaction pathway was reported to be important for
   hormonal regulation in reproductive cycle transitions of duck.
   Similarly, we found that the expression of DEGs associated with the
   neuroactive ligand-receptor interaction pathway were observed to be
   significantly different between LP and BP groups, indicating that
   neuroactive ligand-receptor interaction pathway might play an important
   role in reproductive cycle transitions of poultry. In this study, the
   DEGs involved in the TGF-β signaling pathway, including BMP5, BMP2,
   BMP4, INHBA, INHBB, GREM2, DCN, LTBP1, FMOD, ADAMTSL2, SMURF1 and
   SMAD6, were observed to decrease during the transition from laying to
   incubation. BMP5, a key member of the TGF-β signaling pathway, is
   reported to participate in fecundity of animals [[129]61]. Meanwhile,
   BMP5 could affect ovary development by TGF-β signaling pathway
   activation [[130]62]. BMP2 has been identified as a candidate gene
   within the hypothalamic-pituitary-gonadal axis responsible for
   different egg production performance of ducks [[131]63]. It might be
   involved in the egg-laying performance of poultry via regulating
   follicle development and ovulation [[132]63]. In addition, the MAPK
   pathways in the HPG axis are known to be an intracellular
   serine/threonine kinase signal transduction pathway that is widely
   distributed in various tissues including hypothalamus, pituitary
   glands, and ovaries and can be activated by a variety of stimuli. In
   chicks, there seem to be gender differences in MAPK pathways in HPG
   axis [[133]64]. It was reported that heat stress can trigger MAPK
   crosstalk to turn on the hyperosmotic response pathway [[134]65].
   During the early brooding stage, high temperature training was thought
   to aid chicks in surviving warm environments [[135]64].

   In conclusion, the pituitary transcriptome in laying and incubating
   Changshun hens was evaluated in the present study. We identified a
   total of 2089 DEGs and found that steroid biosynthesis pathway and
   neuroactive ligand-receptor interaction were significantly enriched.
   Five candidate genes, including SRC, ITGB4, ITGB3, PIK3R3 and DRD2, may
   play crucial roles in the regulation of incubation behaviour in
   chickens.

Electronic supplementary material

   Below is the link to the electronic supplementary material.
   [136]Supplementary Material 1^ (11.4KB, docx)
   [137]Supplementary Material 2^ (516.5KB, docx)
   [138]Supplementary Material 3^ (14KB, docx)
   [139]Supplementary Material 4^ (534.8KB, xlsx)

Acknowledgements