Integrated transcriptome and proteome revealed that the declined expression of cell cycle-related genes leads to follicular atresia in geese

doi:10.21203/rs.3.rs-1618265/v1

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Research Article

Integrated transcriptome and proteome revealed that the declined expression of cell cycle-related genes leads to follicular atresia in geese

https://doi.org/10.21203/rs.3.rs-1618265/v1

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Journal Publication

published 16 Jan, 2023

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Background

Geese exhibited relatively low reproductive performance, and follicular atresia is an important factor that restricts the egg production of geese. Systematic analysis of the regulation of follicle atresia in geese through transcriptome and proteome levels could provide meaningful information on clarifying the mechanism of follicle atresia in poultry.

Result

The granulosa cell layer was loose, disintegrated and showed apoptosis in atretic follicles and remained intact in normal follicles. The hormone levels of FSH and LH were significantly decreased in the atresia follicles compared to the normal follicles (P < 0.05). A total of 954 differentially expressed genes (DEGs, 315 increased and 639 decreased) and 161 differentially expressed proteins (DEPs, 61 increased and 100 decreased) were obtained in atresia follicles compared to normal follicles, of which, 15 genes were differentially expressed in both transcriptome and proteome. The DEGs were mainly enriched in sodium transmembrane transport, plasma membrane, and transmembrane transporter activity based on the GO enrichment analysis and in the cell cycle pathway based on the KEGG enrichment analysis. The DEPs were mainly enriched in localization, lysosome, and phospholipid-binding based on the GO enrichment analysis. Candidate genes Smad2–3, Smad4, Annexin A1 (ANXA1), Stromelysin-1 (MMP3), Serine/threonine-protein kinase (CHK1), DNA replication licensing factor (MCM3), Cyclin-A2 (CCNA2), mitotic spindle assembly checkpoint protein (MAD2), Cyclin-dependent kinase 1 (CDK1), fibroblast growth factor 12 (FGF12), and G1/S-specific cyclin-D1 (CCND1) were possibly responsible for the regulation of atresia.

Conclusion

The cell cycle is an important pathway for the regulation of follicular atresia. Sodium outflow and high expression of MMP3 and MMP9 were important causes of structural destruction and apoptosis of follicular cells.

Goose

atretic follicle

cell cycle

matrix metalloproteinase

Follicular atresia is an important factor restricting egg production performance. In birds, less than 5% of the follicles could complete their development process for mature and ovulation, while the remaining 95% become atretic follicles and eventually get degraded [1]. Goose, especially indigenous breeds, exhibits low egg production performance, which causes a relatively higher cost of goslings and lower marketing numbers as compared to broiler and meat ducks. The prevention of follicular atresia and promotion of the normal development of follicle could help increase egg production. Therefore, understanding the mechanism of follicular atresia in geese and screening key signal pathways or genes that regulate follicular atresia would be helpful for identifying molecular markers for selection of goose with high level of atresia.

Follicular atresia is a spontaneous process in which follicles naturally degenerate without an inflammatory response and is generally considered to be caused by defects in the oocyte itself or a lack of necessary growth factors and hormones [2]. The TGFβ superfamily plays an important role in follicular development, in which, BMP-4 and BMP-7 are positive regulators in the transition from the primordial to the primary follicle, while the anti-Mullerian hormone (AMH) is a negative regulator for this transition[3]. The bFGF inhibited follicular atresia through the PI3K-Akt pathway in chickens [4], while FGF10 promoted follicular atresia by decreasing the secretion of estradiol in cattle [5]. In bovine, FGF18 inhibited the secretion of estradiol and progesterone, alters the progression of the cell cycle, and promotes follicular atresia by accelerating the apoptosis of granulosa cells [6]. Hormones have been reported to be involved in follicular development, ovulation, atresia, and other processes. The follicle-stimulating hormone (FSH) could prevent follicular atresia through inhibiting the apoptosis of granulosa cells by inducing their differentiation, the formation of luteinizing hormone receptors, and the synthesis of follicular steroid hormones [7]. It has been proven that atretic follicles exhibited a reduced production of estradiol in granulose cells of porcine [8].

Atretic follicle showed shrunken surface with hemorrhagic spots. The histological and ultrastructural analysis of atretic follicles in two fish breeds revealed the degradation of mitochondria and marked phagocytic activity of digestive vacuoles, myelin, and lipofuscin granules [9]. Granulosa cells were considered to be the first sites to undergo apoptosis in the follicle, which then induced the overall apoptosis and atresia [10]. The atresia of monkey follicles was characterized by three consecutive stages including morphological alterations, cessation of the proliferation and apoptosis, and fragmentation of DNA in granulosa cells that occurred only in late atresia [11]. Furthermore, the follicular development or atresia is thought to be regulated by the survival and apoptosis of granulosa cells in mammals [12]. Unlike the granulosa cells of mammals, which are scattered in the follicular fluid, follicles of poultry have distinct layers of granulosa cells, which make them useful experimental models for the study of follicular atresia.

Many reports have stated the mechanism about follicular atresia in mammals, fish, however, still less was conducted on the systematic research of the molecular mechanism of follicular atresia in poultry. In this study, the histology and reproductive hormones level were compared in normal and atretic follicles. The site of apoptosis of the atretic follicle was first determined in goose. Granulose cells was used for integrated transcriptome and proteome analyses to interpret the molecular mechanism of follicular atresia in geese.

2.1. Histology and hormone levels of atretic and normal follicles

The granulosa cell layer was loose and disintegrated in atretic follicles and remained intact in normal follicles (Fig. 1a). Apoptosis was observed in all parts of atretic follicles and was mostly exhibited in the collapsed granulosa cell layer, which was not observed in the normal follicles (Fig. 1a). The hormone levels of FSH and LH were significantly decreased in the atresia follicles compared to the normal follicles (P < 0.05). The levels of hormones E2, P4, and PRL showed no significant differences between atretic and healthy follicles (Fig. 1b).

2.2. De novo transcriptome analysis

A total of 141,051,229 raw reads and 135,590,434 clean reads were obtained. The error rates of sequencing were < 0.03, the Q20 and Q30 were > 90%, and the mapped rate was > 80% (Supplementary Table S1). After de novo assembly, 140,475 transcripts and 70,318 unigenes with a mean length of 2007 bp and 1436 bp, respectively, were obtained (Supplementary Table S2). The complete single-copied genes accounted for 83.3% of all BUSCO groups, indicating a qualified transcriptome assembled (Fig. 2b). A total of 46,696 unigenes were annotated, and among the seven databases, the Nt database had the highest annotations as compared to other six databases (Supplementary Table S3). According to the results of gene annotation, Anser cygnoides showed the highest similarity with the geese among the annotated species (Fig. 2a). The expression levels of all genes were expressed by FPKM values (Supplementary Table S4) and were mainly distributed in the range of 3.57–15 (Fig. 2c).

A total of 954 DEGs were obtained, of which, 315 were upregulated, and 639 were downregulated in AF (Fig. 3a & b). A detailed analysis of the top 10 DEGs showed that collagenase 3-like (MMP3), which was responsible for proteolysis, was 11.68 times upregulated in AF (Table 1), and matrilysin (MMP7), a disintegrin, metalloproteinase with thrombospondin motifs 18 (ADAMTS18) were the matrix metalloproteinases all present in the top 10 upregulated genes (Supplementary Table S5). The downregulated gene with the highest change was the Kazal-type serine protease inhibitor domain-containing protein 1-like gene, which was responsible for the regulation of cell growth, was 6.19 times downregulated in AF (Table 1). In addition, growth factors fibroblast growth factor 12 (FGF12) and platelet-derived growth factor subunit A (PDGFA) were also downregulated in AF (Supplementary Table S5).

Table 1

The top 10 upregulated or downregulated mRNAs of goose AF vs NF follicles
Nt ID¹	Nt Description²	Symbol	UP/DOWN	log2FC³
XM_005010500	collagenase 3-like	MMP3	UP	11.68
XM_027450263	epiplakin	--	UP	6.05
XM_013191692	polyketide synthase-nonribosomal peptide synthetase-like	--	UP	5.98
XM_027460548	prosaposin-like isoform X1	PSAP	UP	5.89
XM_013178059	matrilysin	MMP7	UP	5.87
XM_013176668	A disintegrin and metalloproteinase with thrombospondin motifs 18 isoform X2	ADAMTS18	UP	5.85
XM_021275530	cysteine and glycine-rich protein 3 isoform X1	CSRP	UP	5.75
XM_013191692	highly reducing polyketide synthase PKS6-like	--	UP	4.77
XM_027459032	chloride anion exchanger	--	UP	4.77
XM_013183939	protein S100-G	S100G	UP	4.69
XM_013179956	solute carrier family 2, facilitated glucose transporter member 9	GLUT9	DOWN	-4.72
XM_013184728	uncharacterized protein LOC104832909	--	DOWN	-4.78
AC189035	transcription factor dp-1-hypothetical protein	--	DOWN	-4.83
XM_013194975	endothelin-converting enzyme-like 1	ECEL1	DOWN	-4.85
XM_021379024	sodium/iodide cotransporter, partial	SLC5A5	DOWN	-4.89
XM_013181091	glycerol kinase	--	DOWN	-5.39
XM_027678824	solute carrier family 23 member 3	IHH	DOWN	-5.63
XM_013187778	PREDICTED: hemogen	--	DOWN	-5.66
XR_001207760	ubiquitin carboxyl-terminal hydrolase 4	--	DOWN	-5.92
XM_013178393	kazal-type serine protease inhibitor domain-containing protein 1-like	--	DOWN	-6.19
¹The ID number of a gene, as annotated in the NT database. ² Description of genes in the NT database. ³ Relative change (x-fold) in atretic follicles compared to normal follicles

The GO enrichment analysis was used for the functional analysis of DEGs. Among biological processes, DEGs were significantly enriched in the sodium ion transmembrane transport (GO: 0035725), pyruvate metabolic process (GO: 0006090), and arginine metabolic process (GO: 0006525). Regarding cellular components, DEGs were significantly enriched in the plasma membrane (GO: 0005886), cell periphery (GO: 0071944), andprotein-N(PI)-phosphohistidine-sugar phosphotransferase complex (GO: 0009357). In terms of the molecular functions, DEGs were significantly enriched in transmembrane transporter activity (GO: 0022857), oxaloacetate decarboxylase activity (GO: 0008948), and peptidase activity (GO: 0008233) (Supplementary Table S6, Fig. 4a). Based on the KEGG enrichment analysis, the DEGs were significantly enriched only in the cell cycle pathway (P < 0.05) in which Smad 2–3, Smad 4, cyclin D, cyclin A, cyclin-dependent kinase (CDK1, CDK2), mini-chromosome maintenance (MCM3), Myelin transcription factor 1 (Myt1), Serine/threonine-protein kinase (chk1, chk 2), mitotic spindle assembly checkpoint protein (mad2), and Securin (PTTG) were downregulated, while the ECREB-binding protein (p300) and cyclin-dependent kinase inhibitor (Ink4b) were upregulated (Supplementary Table S7, Fig. 4b&c).

The Protein − Protein Interaction Network (PPI) analysis was performed to further explore the key genes that regulate the occurrence of follicular atresia (Fig. 5a). In the PPI of the transcriptome, cyclic AMP-responsive element-binding protein 3-like protein 1 (CR3L1), Myb-related protein A (MYBA), Set1/Ash2 histone methyltransferase complex subunit (ASH2L), histone-lysine N-methyltransferase 2D (KMT2D), zinc finger protein 830 (ZN830), and retinoic acid receptor RXR-alpha (RXRA), which were involved in the regulation of cell cycle, upregulated the EP300 in atretic follicles. The downregulated expression of lutropin-choriogonadotropin hormone receptor (LSHR), estradiol 17-beta-dehydrogenase 1 (DHB1), aromatase (CP19A), androgen receptor (ANDR), N-acetylglucosamine-6-sulfatase (GNS), endophilin-A1 (SH3G2), protein-patched homolog 1 (PTC1), sodium channel protein type 5 subunit alpha (SCN5A), and FGF12, and the upregulated expression of low-density lipoprotein receptor-related protein 2 (LRP2) inhibited cell cycle in atretic follicles (Fig. 5b).

2.3. Proteome analysis

Mass spectrometry produced a total of 604048 spectra, 61114 matched spectra, 30333 peptides, 4657 identified proteins, and 4654 quantifiable proteins (Supplementary Table S8). The length of most peptides varied between eight and 16 amino acids, the percentage of proteins with more than 10% peptides was 54.07%, and the protein mass was mainly distributed in the range of 20 kDa–30 kDa and > 100 kDa (Supplementary Figure S1). The principal component analysis (PCA) indicated a significant difference in protein expression between AF and NF groups (Fig. 6a). A low coefficient variance (CV) of 0.2 indicated good repeatability of each proteomic library (Fig. 6b).

A total of 161 DEPs was obtained, among which 61 were upregulated and 100 were downregulated in AF (Fig. 6c & d, Supplementary Table S9). The upregulated protein of MMP3, which was involved in proteolysis, showed a maximum change of 2.66 times in AF than that of NF. The downregulated nicastrin (NICA), which was responsible for protein processing, was 0.58 times in AF than that of NF (Table 2). The three proteins of vitellogenin-2-like (down), transgelin isoform X1 (up), and low-density lipoprotein receptor-related protein 1 isoform X1 (down) showed the highest expression change in DEPs (Supplementary Table S9).

Table 2

The top 10 upregulated or downregulated proteins of goose AF vs NF
Protein ID¹	Gene	Description	C (%)²	Peptides³	FC⁴	UP/DOWN
Cluster-9112.25489;orf2	MMP3	stromelysin-1-like	9	1	2.66	up
Cluster-9112.14658;orf2	HBAD	hemoglobin alpha D subunit	10	1	1.69	up
Cluster-9112.10601;orf1	HSPB7	heat shock protein beta-7	10	1	1.62	up
Cluster-9112.22775;orf1	MLEC	myosin light chain 3	16	2	1.55	up
Cluster-9112.23899;orf1	PRC2A	Protein PRRC2A	9	1	1.54	up
Cluster-9112.6017;orf1	LYG	lysozyme g	25	4	1.53	up
Cluster-9112.9523;orf1	TPPC1	trafficking protein particle complex subunit 1	13	2	1.49	up
Cluster-9112.12518;orf1	IF2B	Eukaryotic translation initiation factor 2 subunit 2, partial	14	1	1.49	up
Cluster-9112.19651;orf1	IKBE	NF-kappa-B inhibitor epsilon	3	1	1.46	up
Cluster-9112.13569;orf1	MMP9	matrix metalloproteinase-9-like, partial	38	19	1.46	up
Cluster-9112.10020;orf1	UTP18	U3 small nucleolar RNA-associated protein 18 homolog	2	1	0.41	down
Cluster-9112.11244;	AAKB1	minus strand	1	1	0.41	down
Cluster-9112.4474;orf1	GNMT	glycine N-methyltransferase	3	1	0.46	down
Cluster-9112.11859;orf1	CCNT1	cyclin-T1	2	1	0.51	down
Cluster-9112.13068;orf1	PLIN3	perilipin-3 isoform X1	21	5	0.54	down
Cluster-9112.12939;orf1	NOP53	hypothetical protein AV530_003974	3	1	0.56	down
Cluster-9112.8116;orf1	A1AT	alpha-1-antitrypsin-like	3	1	0.56	down
Cluster-9112.15043;orf2	POL	hypothetical protein DUI87_09561	18	13	0.57	down
Cluster-9112.13866;orf1	IPO13	Importin-13, partial	4	3	0.57	down
Cluster-9112.11626;orf1	NICA	nicastrin	8	4	0.58	down
¹ The number of proteins obtained by mass spectrometry and annotation corresponding to the transcript database. ² Sequence coverage (%), the ratio of the number of amino acids in every peptide that matches with the mass spectrum in the total number of amino acids in the protein sequence. ³ Number of matching peptides. ⁴ Relative change (x-fold) in atretic follicles compared to normal follicles

Among the biological processes, DEPs were significantly enriched in localization (GO: 0051179), transport (GO: 0006810), and transport of organic substances (GO: 0071702). Regarding the cellular components, DEPs were significantly enriched in the lysosome (GO: 0005764), retromer complex (GO: 0030904), and the extracellular region (GO: 0044421). In the case of molecular function, the DEPs were mostly enriched in phospholipid binding (GO: 0005543), lipid binding (GO: 0008289), and transporter activity (GO: 0005215, Fig. 7A, Supplementary Table S10). The subcellular localization of DEPs was in the nuclear protein (22.33%), extracellular protein (19.42%), and cytoplasm protein (15.53%, Fig. 7b).

The PPI of proteome exhibited that the upregulation of MMP3, MMP9, CD44, annexin A1 (ANXA1), ANXA5, and the downregulation of low-density lipoprotein receptor-related protein 1 (LRP1), disabled homolog 2 (DAB2), alpha-2-macroglobulin (A2M), vitronectin (VTN), and AMBP were involved in proteolysis (Fig. 8a & b). The upregulation of MMP3 and MMP9, along with the downregulation of A disintegrin and metalloproteinase with thrombospondin motifs 13 (ADAMTS13), sulfhydryl oxidase 1 (QSOX1), VTN, and LRP1 were involved in the disassembly of the extracellular matrix. The upregulated expression of MMP3 and MMP9 played a key role in regulating follicle atresia through the promotion of protein degradation (Fig. 8c). To test the accuracy of mass spectrometry, western blotting of MMP3 and MMP9 was performed, and the results showed high expression of MMP3 and MMP9 in atretic follicles (Fig. 8d & e)

2.4. Integrated analysis of transcriptome and proteome

A weak correlation between the mRNA and protein expression was observed according to Pearson’s correlation coefficient (0.107) (Fig. 9a, Supplementary Table S11). While fifteen DEGs were identified in proteome and transcriptome (DEG∩P) and eight showed the same expression trend (Fig. 9b, Table 3), in which MMP3, neuron navigator 1 (NAV1), and ANXA1 were upregulated, adrenodoxin-like (ADX), glucosamine (N-acetyl)-6-sulfatase (GNS), carboxypeptidase N subunit 1 (CPN1), C-factor (CSGA), and VTN were downregulated. The GO analysis indicated that the DEG∩P was mainly enriched in the binding and extracellular region (Fig. 9c).

Table 3

Differentially expressed genes at both mRNA and protein levels in the atretic and normal follicles in geese
Unigenes ID¹	Symbol	description	mRNA² (AFVSNF)	Proetin³ (AFVSNF)
Cluster-9112.25489	MMP3	Anser cygnoides domesticus stromelysin-1-like (LOC106034136)	UP	UP
Cluster-9112.17124	NAV1	PREDICTED: Anas platyrhynchos neuron navigator 1	UP	UP
Cluster-9112.8189	ANXA1	Anser cygnoides domesticus annexin A1	UP	UP
Cluster-9112.11671	ADX	adrenodoxin-like (LOC106045559), transcript variant X3	DOWN	DOWN
Cluster-9112.15071	GNS	Anser cygnoides domesticus glucosamine (N-acetyl)-6-sulfatase	DOWN	DOWN
Cluster-9112.8001	CPN1	Anas platyrhynchos carboxypeptidase N subunit 1, transcript variant X2,	DOWN	DOWN
Cluster-9112.15106	CSGA	Anas platyrhynchos C-factor (LOC101798590)	DOWN	DOWN
Cluster-9112.18299	VTN	Anas platyrhynchos vitronectin (VTN), transcript variant X1	DOWN	DOWN
Cluster-9112.27886	MLEX	Anser cygnoides domesticus myosin, light chain 4, alkali; atrial, embryonic (MYL4), transcript variant X3	DOWN	UP
Cluster-9112.14822	TAGLN	Gallus gallus transgelin (TAGLN), mRNA && M83105.1 Chicken SM22 mRNA, complete cds	DOWN	UP
Cluster-9112.16594	COL11A1	Anas platyrhynchos collagen type XI alpha 1 chain (COL11A1), transcript variant X2	DOWN	UP
Cluster-9112.16021	FMOD	Anas platyrhynchos fibromodulin (FMOD)	DOWN	UP
Cluster-9112.15059	RCN1	Anser cygnoides domesticus reticulocalbin 1, EF-hand calcium binding domain (RCN1)	DOWN	UP
Cluster-9112.22106	FCGBP	Nipponia nippon IgGFc-binding protein-like (LOC104012603),	UP	DOWN
Cluster-9112.21861	-	Anser cygnoides domesticus uncharacterized (LOC106034650),	UP	DOWN
¹ The unique identification number of a gene in the sequencing file. ² The upregulation or downregulation of genes in transcriptome of atretic follicles compared to normal follicles. ³ The upregulation or downregulation of genes in the proteome of atretic follicles compared to normal follicles.

2.5 qRT-PCR analysis of the candidate genes

According to the PPI of transcrptome, genes CHK1, MCM3, CCNA2, MAD2, CDK1, and CCND1 at the nodes and played important role in cell cycle pathway, Smad2–3 and Smad4 at the transcriptional factors of TGFβ for transmembrane signal transduction, ANXA1 and MMP3 that differentially expressed in both transcriptome and proteome were screened out for quantitative determination. The expression level of the selected genes followed a similar trend in the relative expression levels between the RNA-Seq and qRT-PCR, indicating the reliability of our transcriptome data (Fig. 10).

Reproductive hormones play an important regulatory role in follicular development. The FSH and LH promoted follicular maturation and ovulation thus inhibited the apoptosis of granulosa cells in human [13]. Ilha et al. suggested that the activation of LIF-STAT3 in follicular granulosa cells resulted in the decreased level of FSH and caused follicle atresia in cattle [14]. The FSH and LH are glycoproteins that are secreted by the pituitary gland and need to be transported through the blood to the target site to be effective. A study by Feranil et al. demonstrated that the distribution of peripheral vasculature in atretic follicles was lesser than that of normal follicles in swamp buffalo [15], which possibly leads to lower levels of FSH and LH. The in vitro inhibition of angiogenesis could directly induce the apoptosis of granulosa cells and thus cause follicular atresia in rats [16]. A lower levels of FSH and LH were also detected in AF of Yangzhou geese in this study, which demonstrated that steroid hormones regulate follicle atresia. The PDGFA, which regulates cell growth and angiogenesis by binding to its receptor, could increase follicle size and enhance the transition from primordial to primary follicles in the in vitro culture of sheep follicles [17], was downregulated in AF in this study suggested that decreased angiogenesis is positively associated with follicle atresia.

Granulosa cells represent the first site of the initiation of apoptosis in rat atretic follicles [9], and follicular growth or atresia were regulated by the survival or death of granulosa cells in mammalian ovaries [12]. All sodium ion transmembrane transport-related genes were downregulated in the atretic follicles, especially in the plasma membrane, which caused the efflux of sodium ions and thus initiated the activation and shrinkage of apoptotic cells [18, 19], which explained the obvious collapse in the atretic follicles of Yangzhou geese in this study. The caspase recruitment domain (CARD) 9 and 11 showed a significant increase in atretic follicles at the transcriptional level [20], while the mitochondrial proteins SmaC and HtrA2, which promoted cytochrome C-dependent caspase activation by the inhibition of IAP, were upregulated in atretic follicles [21]. These studies demonstrated that apoptotic related genes were highly expressed to initiate the apoptosis of atretic follicles, which also need transcriptome and proteome levels changes to complete granulose cell apoptosis and follicle atresia.

Huet et al. believed that changes in ECM components could affect the apoptosis of follicular cells in sheep [22]. Matrix metalloproteinase (MMP) participates in the disassembly of the extracellular matrix, and the upregulated proteins MMP3 and MMP9 could be involved in the collapse of the granulosa cell layer. As a secretion protein, MMP acts on growth factors and growth factor-binding proteins to affect various cellular functions, including cell invasion, cell migration, apoptosis, and angiogenesis [23]. In the follicles of humans and rats, MMP promotes ovulation by destroying the follicle wall and the surrounding matrix through the stimulation of LH and HCG [24]. After ovulation, the ovaries of poultry leave postovulatory follicles, which gradually degenerate and disappeared. Hrabia et al. reported that the increased activity of MMP2 and MMP9 promoted follicle regression in chicken postovulatory follicles [25]. Zhu et al. also reported that the protein expression of MMP9 increased with the degree of regression of postovulatory follicles [26]. The upregulation of MMP3 and MMP9 in the atretic follicles of Yangzhou geese also suggested that these two genes were involved in the apoptosis of granulosa cells in AF, and might also promote the degeneration and disappearance of atretic follicles.

A complex composed of CDK and cyclin synergistically promoted the cell cycle, while the downregulation of CDK (CDK1 and CDK2) and cyclin (CyCD and CyCA) caused a decrease in the cell division activity in atretic follicles. The downregulation of Smad 2–3 and Smad 4 resulted in the interruption of cell growth or transformation. Tomic et al. demonstrated that the decreased expression of Smad3 resulted in the downregulation of CyCD2 and increased follicular atresia in mice [27]. On the other hand, the increased expression of p300 and Ink4b caused a high level of acetylation of p53 and resulted in the increased expression of p21, which then bound to CDK and CyC to interrupt the cell cycle process. Regan et al. demonstrated that high LSHR was associated with a higher number of follicles, reduced apoptosis, and a higher ovulation rate in merino sheep [28]. A study reported the reduction of ANDR in atretic porcine follicles [29], while another reported that the mRNA of ANDR could be regulated by FSH [30]. FGF12, a growth factor, could promote the migration of endothelial cells, proliferation of smooth muscle cells, formation of new blood vessels, and the repair of damaged endothelial cells, which could act intracellularly to inhibit apoptosis [31, 32]. The downregulation of FGF12 could also be involved in the apoptosis of atretic follicles. The bFGF, FGF10, and FGF18 have been reported to be closely associated with follicular development and atresia [4–6]. In this experiment, the PPI of DEPs suggested that the downregulated genes LSHR, DHB1, CP19A, ANDR, and FGF12, which participated in the cell cycle pathway, could be the key factors regulating follicular atresia in Yangzhou geese.

The DEG∩Ps mainly enriched in binding and extracellular region signal pathways. ANXA1 is a calcium-dependent phospholipid-binding protein that is expressed in almost all organs [33]. It participates in membrane transport, exocytosis, the fusion of some membranes, signal transduction, cell proliferation, apoptosis, and ion channel formation [34]. Zhu et al. reported that ANXA2 was involved in the angiogenesis of chicken ovarian follicles [35]. The downregulation of VTN, an extracellular matrix protein, is the intermediate cause of decreased adhesion between granulosa cell layers. VTN could be degraded by MMPs [36], and its content was positively correlated with the follicular size in bovine [37]. The decreased expression of VTN further suggested that it might be degraded in atretic follicle and caused the collapse of cell layer.

In this experiment, normal and atretic follicles from geese were compared using integrated transcriptome and proteome analysis. The cell cycle is an important pathway for the regulation of follicular atresia. Sodium outflow and high expression of MMP3 and MMP9 were important causes of structural destruction and apoptosis of follicular cells. The genes Smad2–3, Smad4, ANXA1, MMP3, ANXA1, CHK1, MCM3, CCNA2, MAD2, CDK1, FGF12, and CCND1, which were involved in the cell cycle, could be candidate genes for the regulation of follicular atresia.

5.1. Experimental Animals and Sample Collection

Six egg-laying geese (provided by Jiangsu Changzhou Four Seasons Poultry Industry Co., Ltd.) were selected for slaughter after being anesthetized with sodium pentobarbital. The follicles displayed typical morphology of atresia were separated as the atretic follicles (AF). Normal developing follicles (NF) with similar size of AF were separated and set as the control. One part of the separated follicles was immediately frozen in liquid nitrogen and then stored at -80°C for hormone determination. The second part, follicle granulose cell layer was immediately separated and stored for, RNA and protein extraction. The remaining part was stored in a tissue-fixative solution (4% paraformaldehyde) for the paraffin section.

5.2. ELISA and TUNEL assays

After 24 h of immersion in the tissue-fixative solution, the AF and NF were embedded in paraffin and sectioned into slices of 5 µm thickness. These slices were then subjected to TUNEL staining using the TUNEL Apoptosis Detection Kit (yeasen, China) according to the manufacturer's instructions. The hormones FSH, LH, PRL, E2, and P4 were measured using the Enzyme-Linked Immunosorbent assay (ELISA) according to the manufacturer's instructions of ELISA kits (Keshun tech, Shanghai, China). The microtiter plate was read at 450 nm using microtiter plater reader (SUNRISE, Astria)。 Twelve follicles in each of the NF and AF groups were used for the measurement of hormone levels. .

5.3. De novo transcriptome analysis

The total RNA was extracted from each follicle. Three extracted samples were equally mixed to form one replicate, and three replicates represented nine samples that were submitted for analysis. Total RNA was extracted using the Animal Tissue RNA Extraction Kit (Ambion, USA). The purity of the extracted RNA was assessed using the RNA Nano 6000 Assay Kit (Bioanalyzer 2100 system, Agilent Technologies, CA, USA). Purification and reverse transcription of the mRNA were performed using the NEBNext Ultra RNA Library Prep Kit (Illumina, NEB, USA). The cDNA fragment of 370 ~ 420 bp was selected for PCR and repurified using the AMPure XP system (Beckman Coulter, Beverly, USA). The quality of the final library was assessed using the Qubit 2.0 Fluorometer (Agilent Bioanalyzer 2100 system, USA).

The mRNA library was sequenced using Illumina NovaSeq 6000. Clean reads were obtained from raw data by removing reads containing the adapter, ploy-N, and low-quality. De novo assembly was performed using the software Trinity [38], and each cluster was defined as a “Transcript”. The “UniGenes” were obtained by removing redundant data from the “Transcripts” using the software Corset [39]. The completeness of transcript assembly was assessed by the Benchmarking Universal Single-Copy Orthologs (BUSCO, http://busco.ezlab.org/).

Functional annotation of the UniGenes was performed using seven databases: NCBI non-redundant protein sequences (Nr), NCBI nucleotide sequences (Nt), Protein family (PFAM), eukaryotic ortholog groups (KOG), SwissProt, Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene ontology (GO). To calculate the expression of the UniGenes, clean reads were mapped to the assembled database using the software RSEM [40]. The gene expression was quantified and normalized by the calculation method of “Reads per kilobase per million mapped reads” (RPKM) [41], and RPKM values > 0.3 represented the gene expressed.

5.4. Proteomic analysis

The total protein from each follicle was extracted using the acetone precipitation method, three samles were equallty mixed to form one replicate and three replicates represent nine samples were submitted for proteome analysis. The concentration and quality of the extracted protein were measured using the Bradford Protein Quantitative Kit (P0006, Beyotime, Shanghai, China) and 12% SDS-PAGE electrophoresis, respectively. After the protein samples were analyzed, digested, and desalinated, they were labeled using isobaric tags (iTRAQ) for relative and absolute quantification [42]. Briefly, the protein samples were digested with trypsin overnight, and the pH was adjusted to around 3.0 using formic acid (FA). The samples were then centrifuged at 12000 g for 5 min and the supernatant was collected for desalting through the C18 desalting column with elution buffer of 0.1% FA and 70% acetonitrile. Each sample was reconstituted with 20 µL of 1M TEAB buffer (Sigma, USA) and iTRAQ Reagent-8 plex kit (Sigma, USA) for 2 h, following which, 100 µL of 50 mM Tris-HCl (pH = 8) was added to terminate the reaction. The labeled samples were fractionated using a C18 Nano-Trap column (4.6 × 250 mm, 5 µm, Thermo Fisher, USA) on a Rigol L3000 UHPLC system and monitored at 214 nm. Finally, all fractions were dried under vacuum and reconstituted in 0.1% FA.

The liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed on an EASY-nLCTM 1200 UHPLC system (Thermo Fisher, Massachusetts, USA) coupled to a Q Exactive^™ HF-X mass spectrometer (Thermo Fisher, Massachusetts, USA) and operated in a data-dependent acquisition mode [43]. The fraction sample was injected into a C18 Nano-Trap column (4.5 cm × 75 µm, 3 µm, Thermo Fisher, USA). The peptides were separated on an analytical column (15 cm × 150 µm, 1.9 µm) using linear gradient elution for 60 min with a flow rate of 600 nL/min. The gradient elution included mobile phase A (0.1% FA) varying from 94–0% and mobile phase B (0.1% FA, 80% acetonitrile) varying from 6% to100%. A full MS scan ranging from 407 to 1500 m/z was conducted at 60,000 resolution with a target automatic gain control (AGC) value of 3 × 106 ions and a maximum ion injection time (IT) of 20 ms. The top 40 precursors of the highest abundance in the full scan were selected and fragmented using high-energy collisional dissociation (HCD). All MS/MS spectra were scanned using the following parameters: resolution = 15000, AGC = 5 × 104 ions, maximum IT = 45 ms, dynamic exclusion duration = 20 s, normalized collision energy = 32%, and intensity threshold = 2.2 × 104.

The coding sequence (CDS) of the UniGenes from transcriptome was searched in the Nr and SwissProt protein databases. Once the CDS could not match to these two protein database, it would be predicted by the software ESTSCAN version 3.0.3. This combined database was the reference for the proteomic analysis. All spectra were searched against the transcriptome CDS prediction database using the software Proteome Discoverer 2.2 (PD 2.2, Thermo). The search parameters are set as follows: mass tolerance for precursor ion was 10 ppm, mass tolerance for production was 0.02 Da, the fixed modification was carbamidomethyl, dynamic modifications were Oxidation of methionine (M) and iTRAQ plex, N-Terminal modifications were acetylation and iTRAQ plex, and a maximum of two missed cleavage sites was allowed. To ensure the accuracy of the MS data analysis, the Peptide Spectrum Matches (PSMs), protein confidences, and false discovery rates (FDR) were set to > 99%, > 1.0, and ≤ 1.0%, respectively. The proteins were annotated using the program InterProscan against the GO, KEGG, IPR, and COG databases [44].

5.5. qRT-PCR validation

Total RNA was extracted from the goose follicle and reverse transcribed for cDNA synthesis using a reverse transcription kit (Yeasen, China) according to the manufacturer’s protocol. Real-time quantitative PCR (qRT-PCR) was performed using the TransStart Green qPCR SuperMix Kit (TAKARA, China) on an ABI 7500 system (Thermo, USA). The primers were designed using the online platform Primer 3.0 (https://bioinfo.ut.ee/primer3–0.4.0/) (Table 4). The conditions of qRT-PCR were: 95°C for 5 min, followed by 40 cycles of denaturation at 95°C for 10s and annealing and extension at 60°C for 20 s. The expression level was calculated by using the 2^–ΔΔCT method.

Table 4

Primers used for the qRT-PCR validation
Gene	FORWARD PRIMER	RIGHT PRIMER	SIZE (BP)
smad2–3	GCTGGATGAGCTTGAGAAGG	ACGTGCGGTAATCCTTTACG	207
smad4	CAGATGCAGCAGCAAGCA	AGCCCTTGACGAAGCTGAG	290
ANX12	TATTGAGAACTGCCTGACTGCC	TGTCCCAAGGCCCTTCATTG	97
CHK1	GCTGGTGAAGAGGATGACGC	CTCCTGTCCGTGGTGGAGAT	144
MCM³	CCGCGACCAAGAAGACCATC	CCTTGTAGACCGAGAGGCCA	138
CCNA2	GCTGAGCTTGCACCTCTTCA	CCTCCCCAGTGAGAAGGATGT	134
MAD2	TTCACCCGCGTCCAGAAGTA	GCACTTGCAGAGCCACTCTT	111
CDK1	ATGGCCTGATGTGGAGTCCC	AAAGCAGATCAAGCCCATCTTCAT	113
CCND1	CGAGCCTGCCAAGAACAGAT	GTGTGCAGGAAAGGTCTGCT	118
FGF12	AGCTCGGATGTTTTCACACC	GCGTCCTTGTTTTTCTCCAA	258
MMP3	GGACATGATGAAGCAGCCCA	TCGTCCACATCAGCTTGCAG	144
PDGFA	GCCTTTGGCTTTATTGTGGA	GGTTTCCTTCACATCGGAGA	199

5.6. Western blot analysis

Gel electrophoresis was performed using 12% SDS-PAGE, after which, the proteins were transferred onto PVDF membrane. The PVDF membranes were then incubated in 5% skim milk (diluted in TBST) for 2 h and then incubated in the diluted primary antibodies (rabbit anti-MMP3 with 1:1000 dilution, YT4465, ImmunoWay, Jiangsu, China; anti-MMP9 with 1:1000 dilution, YT1892, ImmunoWay, Jiangsu, China; anti-β-actin with 1:10000 dilution, ABclonal, Wuhan, China) at 4°C overnight. After washing by tris-buffered saline three times, the PVDF membrane was incubated with HRP-labeled goat anti-rabbit IgG (1:30,000, BIOMIKY, Shanghai, China) for 60 min. Protein blots were visualized using chemiluminescence imaging system 398 (UVitec, Cambridge, United Kingdom) and quantified using Image J software.

5.7. Statistical and Bioinformatics Analysis

The differences in expression of genes and proteins between AF and NF were analyzed using the T-test in the statistical program R. The genes with p_adj of ≤ 0.05 and fold change (FC) of > 2 were considered differentially expressed (DEGs). The proteins with p_adj of ≤ 0.05 and FC of > 1.3 were defined as differentially expressed proteins (DEPs). The relative mRNA expression through qRT-PCR was analyzed using student T-test in the statistical software SPSS version 22.0 (SPSS, Chicago, IL, USA). The histogram was made using the software GraphPad Prism version 8.0 (GraphPad, San Diego, USA). The GO and KEGG enrichment analyses were performed using the software GOseq and KOBAS [45]. Protein-protein interaction (PPI) was predicted and visually edited using the software STRING DB and Cytoscape, respectively, with String database (http://string-db.org/) as the reference [46].

Normal follicles, NF; atretic follicles, AF; enzyme linked immunosorbent assay, ELISA; TDT-mediated dUTP Nick-end Labeling, Tunel; quantitative real-time PCR, qRT-PCR; RNA-sequencing, RNA-seq; isobaric tags for relative and absolute quantification, iTRAQ; Principal component analysis, PCA; Protein−Protein Interaction Network, PPI.

Acknowledgement

Not applicable

Ethics approval and consent to participate

The animal experiment was reviewed and approved by the Institutional Animal Care and Use Committee of the Anhui Agricultural University (no.SYDW-P20200600601). The experiments were performed according to the Regulations for the Administration of Affairs Concerning Experimental Animals and the Standards for the Administration of Experimental Practices, as well as the ARRIVE guidelines version 2.0.

Consent for publication

Not applicable

Availability of data and materials

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD028836. ‍The transcriptomics datasets generated during the current study are available in NCBI SRA (PRJNA767219, https://www.ncbi.nlm.nih.gov/bioproject/PRJNA767219).

Competing interests

The authors declare that they have no competing interests.

Founding

This work was financially supported by Graduate Innovation Fund of Anhui Agricultural University (2021yjs-19), National Innovative Training Program for College Student (XJDC2020576) and Science and Technology Program of Xinjiang Uygur Autonomous Region(2020B01004-2-1).

Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. All authors contribute equally.

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No competing interests reported.

FigureS1.pdf
The quality control of mass spectrometry, including peptide length (a), precursor ion tolerance (b), protein mass distribution (c), and protein coverage (d).
FigureS2.pdf
The complete gels image of western blot
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Integrated transcriptome and proteome revealed that the declined expression of cell cycle-related genes leads to follicular atresia in geese

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Abstract

Background

Result

Conclusion

Figures

1. Introduction

2. Results

2.1. Histology and hormone levels of atretic and normal follicles

2.2. De novo transcriptome analysis

2.3. Proteome analysis

2.4. Integrated analysis of transcriptome and proteome

2.5 qRT-PCR analysis of the candidate genes

3. Discussion

4. Conclusions

5. Materials And Methods

5.1. Experimental Animals and Sample Collection

5.2. ELISA and TUNEL assays

5.3. De novo transcriptome analysis

5.4. Proteomic analysis

5.5. qRT-PCR validation

5.6. Western blot analysis

5.7. Statistical and Bioinformatics Analysis

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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