Resolving Candidate Genes for Chicken Ovarian Transplantation through RNA-seq and WGCNA

ABSTRACT

This study aimed to identify candidate genes regulating ovarian development following ovarian tissue transplantation through transcriptome sequencing (RNA-seq) and weighted gene co-expression network analysis (WGCNA). Ovarian tissues were collected from 10 thirty-day-old donor chickens, and each ovary was divided equally into three parts of similar size, and transplanted in situ into two-day-old recipient chickens. Samples were collected on days 0 (untransplanted), 6, and 12 after transplantation. RNA was extracted from ovarian samples for construction of a transcriptome library, and then sequenced on the IlluminaHiSeqTM2000 sequencing platform. The sequencing results were evaluated to determine differentially expressed genes (DEGs) through GO function and the KEGG pathway analyses, Gene Set Enrichment Analysis (GSEA), WGCNA, and PPI networks. Some candidate genes were further validated through real-time fluorescence quantitative PCR (RT-qPCR). RNA-seq analysis identified 2242 up-regulated and 3095 down-regulated DEGs at 6 days after ovarian transplantation, and 2181 up-regulated and 2129 down-regulated DEGs were identified at 12 days after transplantation. Enrichment analysis showed that the genes were enriched in multiple inflammatory pathways, pathways involved in signal transduction and cellular processes. Through the WGCNA, five modules were constructed, from which 4 and 5 candidate genes were mined at 6 and 12 days after ovarian transplantation, respectively. Transcription factor prediction showed that GTF3A was the most important transcription factor. The results of RT-qPCR verification confirmed that the expression profile of 8 candidate genes was consistent with that of the sequencing results. In summary, this study presents the candidate genes involved in ovarian vascular remodeling and ovarian proliferation after ovarian transplantation.

Keywords:
Ovary; Transplantation; RNA-seq; WGCNA; Chicken

INTRODUCTION

The decline in the number of wild bird species, coupled with the threat of extinction due to habitat destruction, air pollution, and increasing predator density, underscore the urgent need for research and development aimed at preserving poultry genetic resources. Currently, three primary preservation methods exist: in vivo, cell, and gene preservation. While in vivo preservation remains common, its high cost, genetic instability, and susceptibility to extinction through epidemic diseases necessitate the exploration of alternative preservation techniques. Cell and gene preservation techniques have seen continuous progress with the development of biotechnology. However, gene preservation is susceptible to mutation and recombination during gene library amplification, whereas cell preservation technology offers the advantage of preserving the entire genome without the disadvantages of in vivo preservation. Both domestic and international efforts in cell preservation mainly focus on germ cells such as semen, primordial germ cells, blastocyst cells, gonadal germ cells, testicular tissue, and ovarian tissue. The sex chromosome is ZW in females and ZZ in males, with spermatozoa lacking the W chromosome. Therefore, preserving and transplanting female sex tissue cells is important for protecting genetic resources. The first successful bird ovarian tissue transplantation was performed in 2006 by Song et al. (2006), who developed a surgical procedure for ovarian tissue transplantation in chicks. This involved transferring ovarian tissue from Barred Plymouth Rock chickens to the left ovarian position of newly emerged White Leghorn chickens. Subsequently, donor-derived offspring were successfully obtained using this transplantation technique in chickens (Song & Silversides, 2007), quails (Song & Silversides, 2008), and ducks (Song et al., 2012).

In recent years, while ovarian transplantation has seen significant progress in clinical practice, numerous challenges persist in the revascularization of ovarian tissue, mechanisms of immune tolerance, maintenance of follicular development potential, and the recovery of endocrine function post-transplantation. As a core component of systems biology, transcriptome sequencing (RNA-seq) presents an unprecedented opportunity for an in-depth analysis of the physiological, pathological, and immune response mechanisms involved in ovarian transplantation. Weighted gene co-expression network analysis (WGCNA) has emerged as a widely used tool for mining core genes associated with important traits, and disease occurrence and development (Nangraj et al., 2020; Fan et al., 2023; Guo et al., 2023; Yu et al., 2023). Unlike traditional algorithms, the WGCNA algorithm represents a systemic biological approach for constructing gene co-expression networks, offering a more biologically meaningful means of gene analysis. This methodology involves weighing the genes through soft threshold calculation to establish a scale-free network and generating an expression matrix to classify genes exhibiting similar expression patterns. By identifying genes that play pivotal roles within these modules, WGCNA facilitates the efficient and accurate identification of target genes for further biological research (Panahi & Hejazi, 2021). Therefore, through transcriptome analysis of ovarian tissue pre- and post-transplantation, detailed insights into dynamic cellular and molecular level changes can be obtained, enabling the identification of key functional genes and regulatory networks. Such analysis not only helps to reveal the specific factors influencing transplantation success rates and ovarian function recovery, but also offers theoretical guidance for advancing the science and application of ovarian transplantation in rare bird species.

MATERIALS AND METHODS

Sample collection

Experimental chickens and breeding eggs were provided by a specific chicken farm in Zhumadian. Additionally, 10 one-month-old sanhuang chickens served as donors. Ovaries were collected and rinsed with PBS to remove excess tissues. Each ovary was partitioned into three comparable-sized sections. The control group comprised one piece of fresh ovarian tissue randomly obtained from 9 donor chickens. Furthermore, 30 two-day-old recipient chickens (hatched using the same incubator) were included. Receiver hens were anesthetized with 0.001 mg/g of Sumianxin II (mainly composed of Fluphenazine hydrochloride, Promethazine, and Dihydroetorphine). The hen was positioned on its left side for lateral fixation. The area was shaved, sterilized, and an incision was made below the left last rib to create a 1 cm opening. The intestines near the ovary were moved aside to expose the left ovary. The ovarian membrane was carefully torn open, and the left ovary was removed using microsurgical forceps. Subsequently, the left ovary was excised, and a piece of ovarian tissue was transplanted in situ. The muscle and skin layers were then sutured. On days 6 and 12 post-transplantation, transplanted ovarian tissues were retrieved, washed with PBS, and temporarily stored in liquid nitrogen in freezing tubes for subsequent RNA-seq and fluorescence quantitative Reverse Transcription Polymerase Chain Reaction (RT-qPCR) validation. Ovaries were divided into three groups based on sampling time (N=9 samples/group): NC (fresh untransplanted), OT6 (sampled 6 d post-transplantation), and OT12 (sampled 12 d post-transplantation). Chickens were primarily fed a flat diet comprising appropriate commercial feeds selected according to the nutritional standards for chicken feeding, with free access to water. They were housed in a clean, dry, and well-ventilated environment. The immunosuppressant Mycophenolate mofetil (0.15 mg/day) was administered on the first day of transplantation and continued for seven days.

Library construction and sequencing

Following sampling, the sequencing library was constructed by Shanghai Origin Gene Bio-pharm Technology Co. Ltd, using the Illumina TruseqTM RNA sample prep Kit method. Subsequently, the library was sequenced on the Illumina Novaseq 6000 platform to generate double-terminal sequencing data.

Quality Control

The original data were filtered using the Fastqc software to obtain clean data, with calculations performed for Q20, Q30, and GC contents. Subsequently, the clean data was compared to the reference genome (GRCg7b) using Hisat2 software (v2.1.0, https://daehwankimlab.github.io/hisat2/). The reference genome and annotation files for chickens were downloaded from the NCBI website (https://www.ncbi.nlm.nih.gov/genome/111?genome_assembly_id=1543395). The read segments were compared using Feature Counts (subread v2.0.2, https://subread.sourceforge.net/featureCounts.html). FPKM value was used to quantify gene expression.

Enrichment analysis of differentially expressed genes

Differentially expressed genes (DEGs) were analyzed using the DESeq2 (v1.30.0) software. DEGs were defined by a p value < 0.05 and |fold-change| > 2. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway significance enrichment analysis were performed on the differentially expressed genes.

GSEA enrichment analysis

The whole genome expression level data were filtered using GSEAV4.0.1 software (http://software.broadinstitute.org/gsea/index.jsp) with a threshold of p<0.05 and Q < 0.25. The enrichment of different functional gene sets in the sequenced gene list was then calculated.

WGCNA and Hub gene screening

Gene expression data were analyzed using online WGCNA (Shen et al., 2022). The weighted gene co-expression network of all differentially expressed mRNAs was constructed and divided into different modules based on expression patterns. The correlation between different modules was analyzed, and modules were clustered accordingly. After module division, the correlation between the eigenvalue analysis module and sample groups was calculated. The module with high correlation with different groups was selected as the target module. Using the online tool String (https://cn.string-db.org), a confidence level exceeding 0.80 was established. Subsequently, a Protein-Protein Interaction (PPI) network was constructed based on the differentially expressed genes within the module. Unrelated genes were filtered out, and the core gene interaction network was constructed using Cytoscape. Finally, the top 10 nodes were selected using the MCC algorithm within the cytoHubba plug-in to identify the hub genes.

Transcription factor enrichment analysis

Using the key genes identified through Cytoscape for input genes, perform transcription factor enrichment analysis was conducted using the online web tool ChEA3 (https://maayanlab.cloud/chea3/). The Select the ‘Mean Rank’ method was used, choosing the top 10 ranked transcription factors, and the gene regulatory list was subsequently exported.

Fluorescence quantitative RT-PCR validation

RT-qPCR was used to verify that the analysis materials were selected from all six samples in each group and to validate the candidate genes identified through WGCNA and PPI network analysis. Ovarian total RNA was extracted using the Trizol method, and RNA concentration in the tissue was determined using an ultra-micro ultraviolet spectrophotometer. Subsequently, mRNA was reverse transcribed into cDNA following the instructions of the reverse transcription kit, and stored at -20 ºC. The internal reference gen was β-actin. Primers were designed using the NCBI online Primer-BLAST tool, and their sequences are provided in Table 1. The primers were synthesized by GENEWIZ Co., Ltd. RT-qPCR was performed using the 2×SYBR Green qPCR Master Mix (Low ROX) kit according to the manufacturer’s instructions. The reagents amounted to a 20 μL reaction system: 10 μL of 2 × SYBR Green qPCR Master Mix, 1 μL each of forward and reverse primers, 1 μL cDNA, and 7 μL ddH2O. The reaction conditions were as follows: 95 ºC for 30 s, 95 ºC for 15 s, 60 ºC for 30 s, 72 ºC for 30 s, 40 cycles.

Table 1
Primer pairs for qRT-PCR.

Target	Accession number	Primer sequence	Size (bp)
β-actin	NM_205518.2	Forward: 5’-GCCAACAGAGAGAAGATGACAC-3’	140
		Reverse: 5’-GTAACACCATCACCAGAGTCCA-3’
ACAP1	XM_040695071.2	Forward: 5’-GCGCCCCGTAGAATCCTTTA-3’	141
		Reverse: 5’-CCTGAACCAGGGTGAATCCC-3’
CCNL1	XM_040679446.2	Forward: 5’-ACATGAATGACAGCCTGCGA-3’	132
		Reverse: 5’-AGAGCAAGAACCAGTGTGGG-3’
CDCA7L	NM_001305283.2	Forward: 5’-TGTGCGCAAAAGGAGGAGAT-3’	184
		Reverse: 5’-CTGGTTGCGACAGATGGTCT-3’
COX1	KC847967.1	Forward: 5’-ACGTAGTCCGAGAAAGCACC-3’	139
		Reverse: 5’-AGGCTTGAGTGGAAGAAGGC-3’
ATP5B	NM_001031391.3	Forward: 5’-AAGGCTCCATCACTTCGGTG-3’	183
		Reverse: 5’-TGTTGGGGTCCATGATTCGG-3’
NDUFS8	XM_001232779.7	Forward: 5’-CACAGGCCATCACCATCGAG-3’	150
		Reverse: 5’-ACTCAAAGTTGGGACCCTCC-3’
SDHB	NM_001080875.3	Forward: 5’-TGGACGGACTCTATGAGTGCATCC-3’	167
		Reverse: 5’-TTGAAGTTGTGCCAGGCGTTCC-3’
UQCRFS1	NM_001005843.2	Forward: 5’-GTGTCGGGAGTCGATGAGC-3’	129
		Reverse: 5’-CGACGGTAGGCAGAGAAGTC-3’

• ACAP1: ArfGAP with Coiled-Coil, Ankyrin Repeat And PH Domains 1. CCNL1: cyclin L1. CDCA7L: Cell division cycle associated 7 like. COX1: Cytochrome C oxidase subunit 1.
• ATP5B: ATP synthase subunit beta. NDUFS8: NADH:ubiquinone oxidoreductase core subunit S8. SDHB: Succinate Dehydrogenase Complex Iron Sulfur Subunit B.
• UQCRFS1: ubiquinol-cytochrome C reductase iron-sulfur subunit 1.

Statistical analysis

Using the 2^-ΔΔct method, we calculated each gene’s relative expression. Statistical significance was assessed and plotted using GraphPad Prism (v8.0.1, SanDiego, California USA) software t-tests. The significant level was set to p<0.05.

RESULTS

Ovarian transplantation

Ovarian tissues that developed normally after transplantation were also grayish-white with a glossy surface (Figures 1A, 1B, arrows).

Figure 1
Ovarian tissue after transplantation. Black arrow: (A) Ovarian tissue at 6 days a fter transplantation. (B) Ovarian tissue 12 days after transplantation.

Sequencing data quality assessment

A total of 9 ovarian tissue samples from 3 fresh untransplanted ovaries at 0 days, 6 days, and 12 days after transplantation were sequenced by RNA-seq. The data are controlled, compared, and counted, and each sample produces sequence data after quality control that exceeds 5Gb, of which the Q30 data are more than 90%, indicating that the sequencing data meet the requirements of subsequent analysis (Table 2).

Table 2
RNA-Seq data quality assessment.

Sample	Clean reads	Q20/%	Q30/%	GC%
NC-1	39316286	98.02	94.82	48.62
NC-2	38221066	97.79	94.26	48.63
NC-3	43704846	97.94	94.64	48.62
OT6-1	42233130	97.84	94.37	49.01
OT6-2	46201052	97.94	94.58	48.87
OT6-3	43615264	98.01	94.81	49.06
OT12-1	43701080	98.05	94.88	49.29
OT12-2	44154302	97.91	94.49	49.13
OT12-3	48862980	98.01	94.77	49.18

• Clean reads: The number of sequencing data after quality control. Q20’Q30: Calculation of the percentage of bases with Phred values greater than 20 and 30 to the total bases respectively. GC content: Calculation of the percentage of the total number of bases G and C in relation to the total number of bases.

Screening of differentially expressed genes

To evaluate transcript expression levels, the FPKM values of each sample’s expression matrix were normalized. The expression results are shown in Figure 2A. Principal component analysis (PCA) revealed that the three samples within each group clustered together, indicating high similarity; while the distance between the three groups was significant, suggesting significant differences among them (Figure 2B). A threshold of p-value < 0.05, and | logFC | ≥ 1 was used to screen differentially expressed mRNA. The control group comprised samples collected at 0 days post-ovarian transplantation, while the experimental groups included samples collected at 6 days and 12 days post-transplantation. In the OT6 group, 2242 DEGs were up-regulated and 3095 DEGs were down-regulated (Figure 2C); while in the OT12 group, 2181 DEGs were up-regulated and 2129 were down-regulated (Figure 2D). Correlation analysis indicated that the gene-level correlation coefficients of each set of three replicates were close to 1, indicating similar gene expression patterns among replicate samples, thus meeting experimental conditions (Figure 2E).

Figure 2
The expression profile, principal component analysis, screening and clustering of differential genes in ovaries at different transplantation periods. (A) Box diagram showing the expression of individual genes. (B) principal component analysis (PCA). Different colors represent different groups. (C-D) Volcanic map of differentially expressed genes. The horizontal axis of the volcano plot represents the log2FC value, and the vertical axis represents the -1og10 (P-Value) value. Each point represents a gene, with red and blue dots indicating genes with significantly different expression levels, and gray dots indicating genes with no significant difference. Red dots indicate upregulated genes, while blue dots represent downregulated genes. (E) Correlation analysis between samples.

Enrichment analysis of DEGs by GO and KEGG

The DEGs were analyzed for GO and KEGG functional enrichment, and the results of GO enrichment are shown in Figure 3. Comparisons and analyses were conducted between the group OT6 and NC, as well as between the group OT12 and NC. The five entries with the highest number of differentially enriched genes were selected for presentation. Biological processes (BP) terms were consistently associated with cellular processes, biological regulation, response to stimulus, metabolic processes, and multicellular organismal processes. Cellular components (CC) terms were all related to cell, cell part, organelle, membrane, and membrane part. Molecular functions (MF) terms were all related to binding, catalytic activity, molecular function regulation, molecular transducer activity, and transporter activity.

Figure 3
GO enrichment analysis of differential genes. (A) OT6 group VS. NC group. (B) OT12 group VS .NC group

KEGG analysis revealed significant enrichment of 98 signal pathways. Figure 4A and Figure 4B present the first 30 significantly enriched pathways for OT6 VS NC and OT12 VS NC, respectively. Notably, the enrichment significance of Cytokine-cytokine receptor interaction and cell adhesion molecules (CAMs) was highest during both transplant periods.

Figure 4
KEGG enrichment analysis of differential genes. (A) OT6 group VS. NC group. (B) OT12 group VS .NC group

Gene Set Enrichment Analysis (GSEA)

In addition to GO and KEGG enrichment analyses, we also conducted GSEA (http://software.broadinstitute.org/gsea/) to comprehensively analyze the expression patterns of all genes post-transplantation and provide a more thorough review of the metabolic pathways related to the hypoxia response following ovarian transplantation. Based on the enrichment analysis results, respectively 69 and 87 pathways were significantly enriched in the OT6 (p<0.05, FDR < 0.25) and OT12 groups after ovarian transplantation (p<0.05, FDR < 0.25). Furthermore, among the top 10 pathways enriched by GSEA-KEGG, the PI3K-Akt and Calcium signaling pathways were identified as the most significant pathways during both transplantation times (Table 3).

Table 3
partial GSEA enrichment after ovarian transplantation.

Signaling pathway	No. of enriched genes	Enrichment score	NES	FDR
OT6 VS. NC
PI3K-Akt signaling pathway	279	0.3704	1.5340	0.0189
Calcium signaling pathway	154	0.4153	1.6023	0.0110
cAMP signaling pathway	154	-0.315	-1.2292	0.2475
Cytokine-cytokine receptor interaction	153	0.6787	2.6109	0.0111
Hippo signaling pathway	138	-0.4269	-1.6435	0.0181
Phagosome	133	0.5899	2.2402	0.0111
Phospholipase D signaling pathway	132	0.5156	1.9535	0.0111
Chemokine signaling pathway	127	0.6541	2.4593	0.0111
Ubiquitin mediated proteolysis	127	0.3720	1.3987	0.0874
Apoptosis	121	0.4919	1.8452	0.0111
OT12 VS. NC
PI3K-Akt signaling pathway	285	0.4659	1.5909	0.0093
Calcium signaling pathway	159	0.5404	1.7536	0.0093
Cytokine-cytokine receptor interaction	153	0.6940	2.2428	0.0093
Phagosome	139	0.6278	2.0089	0.0093
Phospholipase D signaling pathway	137	0.5944	1.9011	0.0093
Chemokine signaling pathway	128	0.6541	2.0822	0.0093
NOD-like receptor signaling pathway	116	0.6194	1.9556	0.0093
Jak-STAT signaling pathway	106	0.6385	2.0002	0.0093
T cell receptor signaling pathway	93	0.7008	2.1539	0.0093
Osteoclast differentiation	96	0.5933	1.8270	0.0093

Screening candidate module genes by WGCNA

We used RNA-Seq data from two-time points after ovarian transplantation to filter genes with the top 10,908 MAD values from the expression matrix to construct a co-expression network using WGCNA. To ensure a scale-free distribution of the interaction strengths between genes, we selected a soft threshold value of βpower=28 (Figures 5A and 5B) and constructed the co-expression matrix. Subsequently, we performed module merging, yielding 5 modules (Figure 5C).

Figure 5
Analysis of WGCNA at different times of chicken ovarian tissue transplantation. (A and B) When R2 reaches 0.87 (★), the soft threshold tends to be stable. (C) WGCNA module merge diagram. Genes are divided into various modules through hierarchical clustering, with different colors indicating different modules. Grey represents genes that cannot be classified into any module. (D) WGCNA module-trait correlation heatmap. In the figure, red represents positive correlation, green denotes negative correlation, and the depth of the color indicates the degree of correlation.

Modules were screened at different days post-transplantation as traits, using the criteria of correlation |cor|>0.5 and p<0.05 to identify the genes highly related to ovarian revascularization and follicular loss after ovarian transplantation. Among the 5 co-expression modules, two modules, including cyan brown (r = 0.89, P = 1.5e-3), and midnightblue (r = -0.83, P = 5.4e-3), met the screening requirements on the 6th-day post-transplantation, while two other modules, including green yellow (r = 0.94, P = 1.3e-4), midnight blue (r = 0.90, P = 1.1e-3) met the screening requirements on the 12th-day post-transplantation (Figure 5D). Based on these results, the genes in the 4 small modules were selected for further analysis.

To further screen the related core genes, PPI network analysis was performed using the STRING database, and the top 20 key genes were extracted using the Cytoscape software CytoHubba plug-in (Figure 6). Furthermore, Maximal Clique Centrality (MCC), Maximum Neighborhood Component (MNC), Edge Percolated Component (EPC), degree, and closeness were used to identify key genes before extracting common genes. According to the analysis, ACAP1, CCNL1, CDCA7L, and COX1 were identified as the key genes at 6 days after ovarian transplantation (Table S1). Conversely, ATP5B, NDUFS3, NDUFS8, SDHB, and UQCRFS1 were identified as the key genes at 12 days after ovarian transplantation (Table S2).

Figure 6
Top 20 in network OT6 (A), and OT12 (B) ranked by MCC method.

TF prediction

We used the online ChEA3 database to predict the common TFs associated with the extracted key genes. The results revealed that GTF3A, ZNF511, ZNF101, ZNF276, THAP8, SNAI3, ZNF589, FOXP3, E2F8, and SCML4 were the top ten TFs closely associated with DEGs among ovarian tissues transplanted on days 6 and 12, as well as normal control tissues. Table 4 provides detailed information regarding the genes regulated by these TFs. Notably, the results show that GTF3A is the predominant transcription factor in ovarian transplantation, regulating the expression of genes such as NDUFS8, ACAP1, and NDUFS3, among others.

Table 4
Top 10 TF enrichment analysis of DEGs in different days following ovarian transplantation.

Rank	TF	Mean Rank	No. of regulated genes	Name of regulated gene
1	GTF3A	10.67	5	NDUFS8, ACAP1, NDUFS3, UQCRFS1, SDHB
2	ZNF511	12	3	NDUFS8, UQCRFS1, SDHB
3	ZNF101	44.33	1	ACAP1
4	ZNF276	58	1	ACAP1
5	THAP8	61.33	2	NDUFS8, CCNL1
6	SNAI3	67.67	1	ACAP1
7	ZNF589	78.75	3	ACAP1, CDCA7L, CCNL1
8	FOXP3	88.75	2	ACAP1, NDUFS3
9	E2F8	91	2	CDCA7L, UQCRFS1
10	SCML4	92.67	1	ACAP1

RT-qPCR validation

To ensure the reliability and accuracy of the ovarian tissue RNA-seq data, four genes from each of the two groups of key genes were randomly selected, and their expression was validated using the RT-qPCR approach. Figure 7 shows the validation results, indicating a consistent expression pattern of the genes with the RNA-Seq data. These findings confirm the reliability of the RNA-Seq results presented herein, suggesting their suitability for future research.

Figure 7
RT-qPCR validation of transcriptome data from chicken ovarian transplantation. *, p<0.05; **, ***, p<0.01.

DISCUSSION

In ovarian tissue transplantation without vascular anastomosis, the graft undergoes a period of hypoxia and local ischemia before vascular reconstruction is completed. Progressive vascular remodeling can typically be observed within 3 to 5 days, with functional vessels becoming apparent by the 7th day (Israely et al., 2004). Aerobic metabolic reconstruction follows, usually starting around the 10th day, providing sufficient oxygen and nutrition to the graft (Roness & Meirow, 2019). However, after neovascularization, grafts may experience ischemia-reperfusion injury (IRI). This process generates a significant amount of reactive oxygen species, leading to cellular damage and mitochondrial dysfunction, ultimately resulting in the death of most primordial follicles (Kolusari et al., 2018). Nevertheless, the key genes and mechanisms during this period remain unclear.

In this study, compared to fresh ovaries, we identified 2242 up-regulated and 3095 down-regulated DEGs at 6 days after ovarian transplantation, and 2181 up-regulated and 2129 down-regulated DEGs at 12 days after transplantation.

KEGG analysis was conducted to gain further insight into the biological roles of these DEGs. Our results identified two pathways, cytokine-cytokine receptor interaction and cell adhesion molecules (CAMs), which exhibited the most significant differences between the two-time points of ovarian transplantation.

Cytokines are small proteins crucial for cell signaling and intercellular communication. They exert their effects through specific binding to cytokine receptors on the surface of target cells. The regulation of ovarian follicle development, from primordial to mature stages, involves multiple cytokines and growth factors (Silva et al., 2020). For instance, interleukins (ILs), leukemia inhibitory factor (LIF), and granulocyte-macrophage colony-stimulating factor (GM-CSF) actively participate in the recruitment, proliferation, and differentiation of granulosa cells, which in turn support oocyte maturation within the follicle. In ovarian transplantation, cytokines such as IL-1 and tumor necrosis factor-alpha (TNF-α) are pro-inflammatory cytokines, capable of promoting the production of additional cytokines and chemokines, recruiting immune cells to injury sites, and stimulating tissue repair (Silva et al., 2020). IL-6, a pleiotropic cytokine, promotes cell proliferation and differentiation, regulates metabolism, and plays a role in immune responses (Luo et al., 2017; Stassi et al., 2017). IL-8 is a chemokine that attracts neutrophils and macrophages to injury sites and promotes their activation (Luo et al., 2017). Transforming growth factor beta (TGF-β), a multifunctional cytokine, regulates cell growth, differentiation, and immune responses, and plays a role in tissue repair and fibrosis (Demir et al., 2021; Ji et al., 2023). Revascularization of transplanted ovarian tissue is critical for its survival and function. Vascular endothelial growth factor (VEGF) and other angiogenic cytokines interact with endothelial cell receptors, promoting new blood vessel formation to support the nutritional needs of the grafted ovarian tissue (Olesen et al., 2021; Qin et al., 2023). In summary, cytokine-cytokine receptor interaction signaling pathways play a critical role in the physiological function of ovarian transplantation by regulating tissue repair, immune responses, and ovarian function. Understanding these signaling pathways can facilitate the development of new therapeutic strategies to enhance ovarian transplantation outcomes.

Cell adhesion molecules (CAMs) are integral to ovarian physiology. Throughout ovarian follicle development, CAMs such as integrins, cadherins, and selectins ensure proper cellular communication and organization. They facilitate interactions between granulosa cells and the oocyte, allowing for coordinated growth and differentiation (Baracat et al., 2015). Following ovarian tissue transplantation, rapid re-establishment of blood vessels is essential for graft survival and function. CAMs, including integrins, selectins, and cadherins, participate in the attachment, migration, and proliferation of endothelial cells, promoting angiogenesis to nourish the transplanted ovarian tissue (Brooks 1996). In summary, CAMs are fundamental components of signaling pathways involved in vascularization, and functional recovery. Understanding and manipulating these pathways could lead to improvements in transplant techniques and outcomes.

The results of the GSEA analysis revealed that the PI3K-Akt signaling pathway and the Calcium signaling pathway enriched the most genes among the first 10 common pathways of ovarian enrichment at the two transplantation time points. The PI3K-Akt signaling pathway is responsible for numerous cellular behaviors, including survival, growth, and proliferation. Studies have demonstrated its widespread involvement in the proliferation, differentiation, and migration of ovarian granulosa cells, typically expressed in oocytes and granulosa cells. This pathway not only regulates the growth, development, and maturation of follicles but also regulates cell proliferation, division, and apoptosis control (Cao et al., 2019; Elzaiat et al., 2019). Up-regulation of PI3K-AKT expression can inhibit ovarian tissue damage and restore ovarian function (Jiao et al., 2022). Granulosa cell apoptosis is a factor in follicular atresia, directly affecting follicular quantity and quality. Excessive apoptosis of granulosa cells leads to stalled follicular development, thereby affecting ovarian function (Silva et al., 2020; Qin et al., 2022). Research indicates that the PI3K/AKT/mTOR signaling pathway is closely associated with ovarian granulosa cell apoptosis. Regulating the expression of this pathway can reduce ovarian granulosa cell apoptosis, promote follicular development, and improve ovarian function (Wang et al., 2019). Additionally, the PI3K-AKT signaling pathway is closely related to the activity of estrogen receptor α (ER α). ER α initiates the PI3K-AKT pathway by binding to the PI3K regulatory subunit P85. Moreover, ER α enhances the transcription of the anti-apoptosis protein Bcl2 gene promoter by activating protein kinase B, ultimately increasing Bcl2 protein levels and promoting ovarian cell proliferation and differentiation (Cao et al., 2019; Zhang et al., 2019). The PI3K-Akt pathway is crucial for angiogenesis formation and essential for the revascularization and perfusion of transplanted ovarian tissue. Akt activation stimulates endothelial cell proliferation, migration, and tube formation by upregulating factors like VEGF (Samakova et al., 2019). During the ischemic period before reperfusion, the PI3K-Akt pathway helps cells cope with metabolic stress and maintain redox homeostasis (Samakova et al., 2019). Akt activation enhances antioxidant defenses and reduces reactive oxygen species (ROS) production, mitigating oxidative stress-related damage to ovarian tissue (Chen et al., 2022). In summary, the PI3K-Akt signaling pathway emerges as an essential mediator of ovarian tissue survival, regeneration, and function following transplantation. By targeting this pathway, researchers aim to improve the success rates of ovarian transplantation procedures and restore fertility.

Calcium signaling pathways are fundamental for cell survival, proliferation, and differentiation in various cell types, including ovarian cells (Berridge, 2016). Calcium ions (Ca²⁺) serve as versatile second messengers regulating a wide range of cellular processes in response to extracellular stimuli (Skupin & Thurley, 2012). Following transplantation, revascularization of ovarian tissue is crucial for its survival and function. Calcium ions play pivotal roles as second messengers in endothelial cell proliferation and migration, key steps in angiogenesis (Patton et al., 2003). The influx of Ca²⁺ can trigger the release of angiogenic factors and facilitate the formation of new blood vessels to supply nutrients to the transplanted tissue (Patton et al., 2003). Calcium dysregulation may lead to increased intracellular calcium levels, potentially triggering apoptosis or necrosis in the transplanted ovarian cells (Berridge, 2016). Therefore, maintaining proper calcium homeostasis is essential to minimize cell death and maximize tissue viability. However, oxygen and nutrient deprivation during ischemia cause energy failure, impairing ion pumps responsible for maintaining intracellular calcium homeostasis (Seta et al., 2004). When blood flow returns during reperfusion, calcium channels open, and ATP-dependent pumps fail to clear excess calcium, leading to elevated cytosolic calcium levels (Chang et al., 2010). This calcium overload can trigger a cascade of detrimental events. For instance, excessive cytoplasmic calcium entry into mitochondria disrupts the electron transport chain, resulting in reduced ATP production (Chang et al., 2010). In summary, further research is needed to determine whether the calcium signaling pathway plays a beneficial or damaging role in ovarian transplantation. A better understanding of calcium signaling in ovarian transplantation could provide insights into strategies to enhance tissue survival and function post-transplantation.

WGCNA divides a large number of genes into several modules using gene co-expression data. By associating modules with traits, key modules containing core genes can be identified, and the core genes can be screened out (Cheng et al., 2020). WGCNA clusters genes with similar expression patterns and analyzes the relationship between genes and traits (Langfelder & Horvath, 2008). WGCNA identified candidate genes at days 6 and 12 post-transplantation. Although direct research on the role of these genes in ovarian transplantation is limited, their potential involvement can be inferred based on their known functions.

At 6 days post-ovarian transplantation, key genes identified included ACAP1, CCNL1, CDCA7L, and COX1. ACAP1 (Arf-GAP with coiled-coil, ankyrin repeat, and PH domains 1) is a protein involved in intracellular vesicle trafficking and cytoskeletal organization. It regulates vesicle trafficking pathways, such as endocytosis and exocytosis, which are crucial for transporting essential molecules like hormones, growth factors, and signaling proteins within and between cells in the transplanted ovary (Dai et al., 2004; Li & Hsu, 2015). Disruption of vesicle trafficking processes could affect cellular communication, hormone production, and overall ovarian function (Roberts & Kurre, 2013). Additionally, ACAP1 contributes to cytoskeletal organization by interacting with actin filaments and microtubules (Jackson et al., 2000). The cytoskeleton is essential for maintaining cell structure, supporting cell motility, and facilitating intracellular transport (Fletcher & Mullins, 2010; Hohmann & Dehghani, 2019). In various cellular contexts, ACAP1 has been implicated in regulating cell adhesion and migration (Hohmann & Dehghani, 2019). In ovarian transplantation, cell adhesion and migration are critical for the integration of transplanted ovarian tissue into the host environment, as well as for angiogenesis and immune cell recruitment. ACAP1 may also modulate signaling pathways involved in cell proliferation, differentiation, and survival, thereby influencing tissue function and viability (Vitali et al., 2019). While these hypotheses suggest potential roles for ACAP1 in ovarian transplantation, experimental validation is necessary to confirm its exact function and significance in this context. As the field of ovarian transplantation research evolves, further investigation may uncover novel roles for proteins like ACAP1 in the future.

Both CCNL1 and CDCA7L belong to the cyclin family. CCNL1 recruits and stabilizes transcription factors and cofactors at target gene promoters, thereby modulating the expression of genes involved in cell proliferation, differentiation, and response to stressors (Liu et al., 2017; Gong et al., 2023). This regulation can have implications for cellular processes such as development and immune response. CCNL1 is also implicated in the DNA damage response, as it can localize to DNA damage sites upon genotoxic stress, potentially coordinating the transcriptional response to DNA damage, including cell cycle checkpoint activation, DNA repair, and apoptosis (Wood & Endicott, 2018). Thus, CCNL1 may be involved in the transcriptional control of genes necessary for tissue repair, cell survival, and immune responses in transplanted ovarian tissue. On the other hand, CDCA7L likely participates in regulating cell cycle progression, particularly during the G1/S or G2/M transition phases, affecting cell proliferation (Rivera-Gonzalez et al., 2012; Xiao et al., 2023). As ovarian tissue contains germ cells that differentiate into viable oocytes and somatic cells supporting follicle development, CDCA7L may play a role in these differentiation processes, ensuring the recovery of ovarian function post-transplantation.

COX1, also known as cytochrome c oxidase subunit 1, is a key component of the mitochondrial electron transport chain (ETC) Complex IV. It plays a crucial role in cellular respiration and ATP production by accepting electrons from cytochrome c and reducing molecular oxygen to water (Dennerlein et al., 2023). Healthy mitochondria are essential for cell survival and tissue regeneration. Impaired COX1 function could lead to mitochondrial dysfunction, resulting in decreased energy output, oxidative stress, and increased cell death (Dennerlein et al., 2023), negatively impacting the success of ovarian transplantation. Rapid cell division and differentiation processes occurring during ovarian tissue regeneration post-transplantation demand high energy levels. COX1 and the ETC are critical for providing the energy required for these processes. The process of ovarian transplantation can induce periods of ischemia followed by reperfusion, leading to oxidative damage to the transplanted tissue (Rodrigues et al., 2023). COX1, located at one of the final sites in the ETC, plays a crucial role in interacting electrons with oxygen, and aiding in mitigating the harmful effects of ROS generated during reperfusion (Tu et al., 2019). In summary, COX1 is essential for responding to ischemia-reperfusion injury by maintaining mitochondrial function, limiting ROS production, and sustaining cellular energy levels. However, the specific role of COX1 in ovarian ischemia-reperfusion injury requires further elucidation through dedicated research in this area.

Our results revealed a significant decrease in COX1 expression at 6 days post-ovarian transplantation, suggesting impairment of the respiratory chain function in the mitochondrial intima at this time. This impairment likely led to the accumulation of ROS, abnormal opening of the mitochondrial membrane permeability transition pore, decreased mitochondrial membrane potential, damaged the respiratory chain complex, and disrupted calcium homeostasis, ultimately resulting in mitochondrial damage and dysfunction (Ávila et al., 2016). This also explains the significant enrichment of Calcium signaling pathways in the results of GSEA analysis. Additionally, the significant increase in the expression of ACAP1, CCNL1, and CDCA7L suggests active graft repair processes aimed at mitigating cell damage.

The key genes identified by the WGCNA analysis method at 12 days post-ovarian transplantation were ATP5B, NADH: ubiquinone oxidoreductase core subunit S3 (NDUFS3), NDUFS8, SDHB, and UQCRFS1. These genes are all components of the ETC within the mitochondria, which is a central part of cellular respiration and energy production. As a crucial component of ATP synthase, ATP5B plays a pivotal role in converting the energy stored in the proton gradient across the inner mitochondrial membrane into ATP molecules (Fliedner et al., 2015). NDUFS3 and NDUFS8 serve as subunits of Complex I (NADH: ubiquinone oxidoreductase) in the ETC. Their proper functioning is vital for initiating the electron transport chain and maintaining the mitochondrial membrane potential, which drives ATP synthesis (Pagniez-Mammeri et al., 2009). SDHB is a component of Complex II (Succinate dehydrogenase), connecting the citric acid cycle to the ETC (Zhang et al., 2022). SDHB’s function is critical for ATP production and for maintaining redox balance (Goncalves et al., 2021; Zhang et al., 2022). UQCRFS1, also known as the Rieske iron-sulfur protein, is a component of Complex III (Cytochrome bc1 complex). This complex facilitates the transfer of electrons from ubiquinol to cytochrome c, generating a proton gradient (Fernandez-Vizarra & Zeviani, 2018). Efficient electron transport through UQCRFS1 is essential for ATP synthesis and minimizing the production of ROS that can damage cellular components (Sena et al., 2013). Overall, these proteins work synergistically to maintain the proper functioning of the electron transport chain, which is fundamental to aerobic respiration and energy metabolism in all cells, including those in the ovaries. Any disruption or mutation in these proteins can lead to mitochondrial dysfunction, potentially result in various diseases, including those affecting ovarian function and overall health.

The development, maturation, fertilization, and embryonic development of oocytes rely heavily on the normal functioning of mitochondria within the oocyte (May-Panloup et al., 2016). Mitochondria are important organelles in eukaryotic cells, generating adenosine triphosphate (ATP) through oxidative phosphorylation (OXPHOS) via the respiratory chain complex on the inner membrane. This process provides the energy required for normal physiological activities of the organism (Labarta et al., 2019). Moreover, mitochondria participate in various important physiological processes, including redox reactions, metabolite synthesis, regulation of calcium homeostasis, and lipid metabolism. They simultanesouly play key roles in cellular energy metabolism, differentiation, senescence, and apoptosis (Pickles et al., 2018). Following 12 days of ovarian transplantation, once the blood supply is completely established, the ovary undergoes rapid development, requiring a significant amount of energy. Our results demonstrate a significant increase in the expression levels of ATP5B, NDUFS3, NDUFS8, SDHB, and UQCRFS1, indicating enhanced functionality of the mitochondrial respiratory chain. This enhancement is beneficial to the acceleration of ovarian function recovery.

The prediction results of transcription factors highlight GTF3A as the most important transcription factor in ovarian transplantation. GTF3A, also known as General Transcription Factor 3A, is a subunit of the general transcription factor TFIIIB. Its main physiological function involves recognizing and binding to specific DNA sequences near the transcription start site of RNA polymerase III-dependent genes (Wang et al., 2022). Therefore, GTF3A plays an essential role in the transcription machinery, regulating the expression of numerous housekeeping genes essential for basic cellular functions (Arakawa et al., 1995; Wang et al., 2022). Transcription factor GTF3A may be involved in regulating the expression of cell cycle-related genes, affecting proliferation of ovarian cells, which is crucial for the survival and function maintenance of transplanted ovarian tissue (Anuraga et al., 2021). Ovarian function relies on hormone synthesis and secretion. GTF3A may influence ovarian hormone levels by regulating genes involved in hormone biosynthesis pathways (Rojo-Bartolomé et al., 2017). The transplantation process may induce oxidative stress and inflammatory responses. GTF3A may participate in regulating genes involved in cellular stress responses to help cells adapt to microenvironmental changes post-transplantation (Anuraga et al., 2021). While the direct association between GTF3A and ovarian transplantation may not be explicit, its normal functioning is indispensable for the overall health and vitality of any transplanted tissue, including ovarian tissue, due to its involvement in global RNA synthesis.

Recent studies have shown that molecular mechanisms of ovarian premature aging can be elucidated through gene knockout techniques, such as the knockout of nucleolar protein DCAF13, providing a new perspective for improving ovarian transplantation outcomes (Zhang et al., 2020). Additionally, the combination of materials engineering and reproductive medicine, such as using graphene oxide/poly-L-lactic acid (GO/PLLA) composite nanofiber scaffolds, can effectively promote neovascularization after ovarian tissue transplantation, thereby improving ovarian function (Yan et al., 2022). These studies suggest that precise manipulation of relevant genes or signaling pathways could significantly enhance the success and outcomes of ovarian transplantation. However, challenges in this field include identifying rational and effective target genes and efficient, low-toxicity gene carriers. Transcription factors like GTF3A typically participate in multiple biological processes, and their functional regulatory networks are complex and difficult to precisely control.

CONCLUSIONS

In summary, the identification of candidate genes involved in ovarian vascular remodeling and proliferation post-ovarian transplantation was achieved through RNA-Seq and WGCNA. At 6 days post-transplantation, key genes such as ACAP1, CCNL1, CDCA7L, COX1 were identified, while at 12 days post-ovarian transplantation, ATP5B, NDUFS3, NDUFS8, SDHB, UQCRFS1 were identified. These findings provide valuable insights into the molecular mechanism underlying improved ovarian development following ovarian tissue transplantation.

ACKNOWLEDGMENTS

We thank the central laboratory of Xinyang Agriculture and Forestry University for providing support with equipment.

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• Funding
  This work was supported by the Project of Science and Technology of Henan Province (grant no. 222102110192), Innovative Research Team of Poultry Germplasm Resources Application and Healthy Breeding of Dabie Mountain Area in Xinyang Agriculture and Forestry University (grant no. XNKJTD-013), and Project of Training Programme for Young Backbone Teachers in Undergraduate Colleges and Universities of Henan Province (grant no. 2023GGJS178).
• Data availability statement
  Please contact the corresponding author if you would like access to the data supporting these findings.
• Ethics statement
  All animal procedures in this study were conducted according to the animal husbandry guideline of Xinyang Agriculture and Forestry University. The studies in animals were reviewed and approved by the Laboratory Animal Welfare and Ethical Reviewing Committee of Xinyang Agriculture and Forestry University (XAFU-2023-00068).

Edited by

• Section editor:
  Maria Fernanda Burbarelli

Data availability

Please contact the corresponding author if you would like access to the data supporting these findings.

Publication Dates

• Publication in this collection
  01 Nov 2024
• Date of issue
  2024

History

• Received
  07 May 2024
• Accepted
  18 Aug 2024