Identification of reproduction-related genes in the hypothalamus of the sheep (Ovis aries) using the ONT full-length transcriptome sequencing technology

Identification of reproduction-related genes in the hypothalamus of the sheep (Ovis aries) using the ONT full-length transcriptome sequencing technology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification of reproduction-related genes in the hypothalamus of the sheep (Ovis aries) using the ONT full-length transcriptome sequencing technology Tong Wang, Zhibin Ji, Xue Xiao, Dejie Zhu, Hengyi Li, Xinyu Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4847033/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The hypothalamus is the coordination center of the sheep ( Ovis aries ) endocrine system and plays an important role in the reproductive processes of the sheep. However, the specific mechanism by which the hypothalamus affects sheep reproductive performance remains unclear. In this study, the hypothalamus tissues of high-reproduction small-tailed Han sheep and low-reproduction Wadi sheep were collected, and full-length transcriptome sequencing by Oxford Nanopore Technologies (ONT) was performed to explore the key functional genes associated with sheep fecundity. The differentially expressed genes (DEGs) were screened and enriched using DESeq2 software through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Approximately 41.75 million clean reads were obtained from the hypothalamus tissues of high- and low-reproduction sheep; after quality control, 32,194,872 high-quality full-length sequences and 2,114 DEGs were obtained, including 1,247 upregulated genes and 867 downregulated genes ( P adjust 1). Some DEGs were enriched in oocyte meiosis, progesterone-mediated oocyte maturation, estrogen signaling pathway, GnRH signaling pathway and other development-related signaling pathways. The constructed protein-protein interaction (PPI) networks identified the reproduction-related genes, such as GSK3B , PPP2R1B , and PPP2CB . The results of this study will enrich and supplement the genomic information available for small-tailed Han sheep and Wadi sheep, as well as expand the understanding of the molecular mechanisms underlying the regulation of animal reproduction by the hypothalamus, and also provide reference data for further investigations on the mechanism of high reproduction in sheep. Biological sciences/Genetics/Animal breeding Biological sciences/Genetics/Development Biological sciences/Genetics/Functional genomics Biological sciences/Genetics/Gene expression Biological sciences/Genetics/Gene regulation Biological sciences/Genetics/Genome Biological sciences/Genetics/Genomics Biological sciences/Genetics/Sequencing sheep hypothalamus transcriptome reproduction differentially expressed gene Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Sheep ( Ovis aries ) is an important economic animal in China, its meat, milk and furs greatly enrich human life, and its reproductive performance is an important indicator of its economic traits 1 . Reproduction is a complex and important physiological process that depends on the coordination, synthesis and release of gonadal axis hormones 2 . The hypothalamus-pituitary-gonadal (HPG) axis underlies the endocrine control of reproduction in mammals, and together, they regulate the development and reproductive function in mammals 3 . As the starting organ of the HPG axis, the hypothalamus can generate a GnRH signal to regulate the secretion of downstream hormones, including follicle-stimulating hormone (FSH) and luteinizing hormone (LH) 4 . These two hormones are regulatory factors of the ovary, by maintaining the function of the corpus luteum, they drive the follicle to produce progesterone (P4) and estrogen (E2) 5 . GnRH (gonadotropin releasing hormone) secreted by the hypothalamus can serve as an initiator of reproductive activities and plays an important role in reproductive regulation, and changes in hypothalamus function affect reproductive activities, such as follicle development, ovulation and egg laying 6 . Therefore, the hypothalamus is involved in the regulation of reproductive activities in mammals, and the hypothalamus is a key region in the brain that initiates reproductive activities. With the development of high-throughput sequencing technology, breed selection has entered the era of molecular breeding. Candidate gene analysis can be used to identify individual genes responsible for economically important traits, and many candidate genes associated with animal fecundity have been continuously mined 7 . Currently, many signaling pathways related to sheep reproductive performance have been identified through molecular biology techniques 8 , but the genetic and physiological mechanisms involved remain to be further explored. Small-tailed Han sheep is a known native breed in Shandong Province, China. Small-tailed Han sheep can be in estrus for four seasons and can be bred all year round, the average annual lambing rate is approximately 267.1%, and small-tailed Han sheep are known for their high fecundity. The fecundity of Wadi sheep is lower than that of small-tailed Han sheep, Wadi sheep are often in estrus in spring and fall, and the lambing rate of primiparous ewes is only 194.7%. The differences in fertility between small-tailed Han sheep and Wadi sheep and the molecular mechanisms underlying the regulation of animal reproduction by the hypothalamus have not been studied. Therefore, in this study, we used hypothalamus tissues from high-reproduction small-tailed Han sheep ewes and low-reproduction Wadi sheep ewes as the research objects. Meanwhile, full-length transcriptome sequencing technology by Oxford Nanopore Technologies (ONT) 9 was used to screen and mine related genes involved in the reproduction regulation of sheep. This is of great significance for the comprehensive understanding of themechanism of the hypothalamus in regulating animal reproduction, and may provide a more theoretical basis for the future elucidation of the molecular mechanism of high reproduction in sheep. Methods Sample collection and ethics declarations Three one-year-old small-tailed Han sheep ewes with no significant differences in weight, height, body shape or body condition, and with the same age, from small-tailed Han sheep breeding farm in Jiaxiang County, Shandong Province, China (National-level Mutton Sheep Core Breeding Farm) were selected and named as High Fecundity Group (XH). Three Wadi sheep ewes, from Binzhou Animal Husbandry and Veterinary Research Institute of Shandong Province, China (Wadi Sheep Breeding Farm of Shandong Province), were also selected with the same selection criteria as small-tailed Han sheep, and named as Low Fecundity Group (XL). The animals were sacrificed without pain after general anesthesia by intramuscular injection of Xylazine Hydrochloride injection solution, and every effort was made to reduce the suffering of the animals in the experiments. The hypothalamus tissues were removed and placed into sterile cryopreservation tubes, numbered and quickly placed in the nitrogen tank for extraction of total RNA and subsequent experiments. All experimental protocols were approved by Experimental Animal Management Committee of Shandong Agricultural University (SDAUA-2023-237), all methods were carried out in accordance with relevant guidelines and regulations, all methods are reported in accordance with ARRIVE guidelines ( https://arriveguidelines.org ). Construction and sequencing of the ONT library Total RNA was extracted from the hypothalamus tissues of 6 experimental sheep using TRIzol reagent (Thermo Fisher Scientific, USA), the RNA purity (D260/D280 value) was determined using a NanoDrop ND-2000 (Thermo Science, USA) 10 , the RNA concentration was precisely quantified using a Qubit 3.0 fluorometer 11 , and the integrity of the RNA was examined by agarose gel electrophoresis. For the total RNA samples that passed the quality inspection, Oligo DT was used as a primer for reverse transcription, and complementary DNA (cDNA) was obtained by low-cycle polymerase chain reaction (PCR) and purified using AMPure beads. After adding sequencing adapters (including motor proteins), sequencing was performed using the FLO-PRO002 microarray. ONT library construction and all sequencing work were completed by Bena (Wuhan) Information Technology Co., Ltd. (Wuhan, China). Quality control and statistics of the sequencing data The obtained raw sequencing data were subjected to base calling using GUPPY (version: 5.0.16) software, converted to fastq format to remove low-quality sequences and adaptor sequences, and then processed using NanoFilt 50 (version: 2.8.0; parameters: -q 7 -l) software 12 to filter out sequences with a quality value less than 7 and a length less than 50 bp to obtain effective clean reads. The SeqKit (version: 0.12.0; parameter: default) software 13 was used to count the effective clean reads. The full-length sequences in the valid sequencing data were identified using Pychopper software (version: 2.4.0; parameters: -Q 7 -z 50, Pychopper: https://github.com/nanoporetech/pychopper ) and minimap2 (version: 2.17- r941; parameters: -ax splice -uf -k14) 14 , and the filtered full-length sequence was aligned with the sheep reference genome (ARS-UI_Ramb_v3.0). SAMtools software (version: 1.11; parameter: flagstat) 15 was used to analyze the genome alignment results. Analysis of DEGs between the high- and low- fecundity groups In this study, transcripts per kilobase million (TPM) was used to measure gene expression levels, and gene expression quantification was performed using Salmon software (version: 1.4.0) 16 . DESeq2 software (version: 1.26.0) 17 was used to analyze the read count data in each sample for differential expression analysis, and the screening thresholds were P adjust 1. For the calculation of TPM, for each gene, the read counts were divided by the length (in kilobases) of the gene to obtain the reads per kilobase (RPK), and all RPK values in a sample were calculated and divided by 1,000,000 to obtain the “per million” scaling factor. Then, the RPK value was divided by the “per million” scaling factor to obtain the TPM value 18 . GO and KEGG enrichment analysis of DEGs The Gene Ontology (GO) database ( http://www.geneontology.org/ ) 19 was used for the GO annotation of the DEGs, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database ( https://www.kegg.jp/ ) 20 was used to perform pathway analysis on the DEGs. GO analysis included mainly cellular component (CC), molecular function (MF) and biological process (BP). The GO items and KEGG pathways that were significantly enriched in DEGs were calculated using hypergeometric distribution, and the GO items and KEGG pathways with P ≤ 0.05 were considered significantly enriched. The enrichment analysis software used for DEGs was ClusterProfiler (version 3.14.3) 21 . Construction of the regulatory network Based on the GO and KEGG enrichment results of the DEGs, Cytoscape software (version 3.10.0, http://www.cytoscape.org/ ) 22 and the STRING database (version 11.5, https://cn.string-db.org ) 23 were used to construct a gene regulatory network. Validation of DEGs by quantitative real-time PCR (qRT-PCR) To verify the reliability of the sequencing results, eight DEGs were randomly selected from the transcriptome sequencing data. The GAPDH ( Ovis aries ) gene was used as an internal reference gene for the qRT-PCR. The primers were designed using Primer 5 software (Supplementary Table S1 ). Quantification was performed according to the instruction manual of the SYBR Green Premix Pro Taq HS qPCR Kit (Code: AG11701), and PCR amplification was performed on a LightCycler 480 system (Roche). The specific reaction conditions were as follows: denaturation at 95°C for 30 s, followed by for 40 cycles of amplification at 95°C for 5 s and 40 cycles of amplification at 60°C for 30 s. The relative expression levels of genes were calculated by the 2 −ΔΔCt method, and the significance standard was P < 0.05 (Livak et al., 2001). The results were visualized using GraphPad 8.0 (GraphPad Software, USA). Results Quality assessment and reference genome comparison of sequencing data In this study, the hypothalamus tissues of high-reproduction small-tailed Han sheep (XH) and low-reproduction Wadi sheep (XL) were selected for ONT sequencing. For the XH group (X1-X3), 9,530,456, 9,962,624, and 9,855,014 raw reads were obtained, of which 8,998,836, 9,496,791, and 9,415,631 were clean reads, accounting for 94.4%, 95.3% and 95.5% of the raw reads, respectively (Table 1 and Fig. 1 A). For the XL (X4-X6) group, 4,498,641, 4,965,510 and 4,958,213 raw reads were obtained, of which 4,313,447, 4,776,002 and 4,754,922 were clean reads, accounting for 95.9%, 96.2% and 95.9% of the raw reads, respectively (Table 1 and Fig. 1 A). For the XH group (X1-X3), there were 6,840,076, 7,329,826, and 7,397,089 full-length sequences, respectively, there were 3,291,592, 3,700,877, and 3,635,412 full-length sequences were also identified for the XL group (X4-X6), respectively. The full-length sequences were aligned to the reference genome, and the alignment rate was greater than 80% (Table 1 ). In addition, the correlation analysis of RNA-Seq data based on the gene expression levels showed that the Pearson correlation coefficient (R) between the two groups was greater than 0.96 (Fig. 1 B), indicating that the similarity in the expression patterns between the two groups of samples was high, which increased the reliability of the results. These results indicated that high-quality full-length transcriptome sequencing results were obtained. Table 1 Sequencing data information and alignment statistics with reference genome for six samples. Sample Raw reads Clean reads Total reads Mapped reads Map_rate X1 9,530,456 8,998,836 6,840,076 5,503,728 80.46% X2 9,962,624 9,496,791 7,329,826 5,935,547 80.98% X3 9,855,014 9,415,631 7,397,089 6,226,032 84.17% X4 4,498,641 4,313,447 3,291,592 3,209,994 97.52% X5 4,965,510 4,776,002 3,700,877 3,617,334 97.74% X6 4,958,213 4,754,922 3,635,412 3,573,424 98.29% Note: X1, X2 and X3 represent the different library in small-tailed Han sheep group (high-reproduction, XH); X4, X5 and X6 represent the different library in Wadi sheep group (low-reproduction, XL). Identification and expression analysis of DEGs The hypothalamus is an important organ of the hypothalamic-pituitary-ovarian (HPO) axis in female mammals, and the differential expression of genes can affect the fecundity of sheep. To further explore important regulatory factors in the hypothalamus associated with sheep reproductive ability, the DEGs between the XL and XH groups were screened according to the criteria of padj 1. A total of 25,246 genes, including 21,457 known genes and 7,482 novel genes (genes with new transcripts were counted as novel genes), were identified between the two groups (Fig. 1 C, Supplementary Table S2). A total of 2,114 DEGs were obtained in these two groups, with 1,247 upregulated genes and 867 downregulated genes (Fig. 1 D, Supplementary Table S3). The volcano map shows the basic distribution of the DEGs obtained by sequencing (Fig. 1 E). Hierarchical cluster analysis of DEGs in different samples revealed significant segregation between the XH group (X1-X3) and the XL group (X4-X6), indicating significant differences in gene expression between the two groups. In the hypothalamus, the three samples (X1-X3) of the XH group were clustered in the same cluster, and the three samples (X4-X6) of the XL group were clustered in the same cluster, indicating that the gene expression levels and patterns of the three samples within the same group were similar, and differences were also existed between different groups (Fig. 1 F). GO functional annotation and classification of DEGs Among the 2,114 DEGs obtained from the hypothalamus, 1,456 DEGs were enriched in 2,968 GO terms, including 1,717 in BP, 517 in CC and 734 in MF (Supplementary Table S4). In BP, DEGs were enriched mainly in vesicle-mediated transport, translation, and protein localization to the plasma membrane; in CC, DEGs were enriched mainly in Golgi apparatus, Ribosom, and Mitochondrial respiratory chain complex IV; and in MF, DEGs were enriched mainly in structural constituent of ribosome, GTPase activity, and ubiquitin protein ligase binding. Based on P ≤ 0.05, among the significant DEGs between the XH and XL groups, the top 20 GO terms with significant enrichment were screened, with 10 GO terms belonging to CC, 5 GO terms belonging to BP, and 5 GO terms belonging to MF (Fig. 2 A, B). Among the different GO categories, the top 20 GO terms in each of BP, CC, and MF are shown in Fig. 2 C. GO functional annotation and classification of the DEGs in small-tailed Han sheep (XH group) To more clearly explain the functional role of the enriched GO terms in the reproductive process, GO annotation analysis on the upregulated and downregulated DEGs in the hypothalamus of small-tailed Han sheep was performed (Supplementary Table S5). Among the 746 upregulated DEGs, 1,710 GO terms, including 937 in BP, 320 in CC and 453 in MF, were enriched, and the the GO terms related to reproductive and physiological processes in sheep, such as ovarian follicle development and in utero embryonic development were annotated and obtained. The top 20 GO terms in each of BP, CC, and MF are shown in Fig. 3 A. Based on P ≤ 0.05, the top 20 GO terms with significant enrichment were screened (Fig. 3 B), and the most enriched GO term was integral components of the membrane, with 178 enriched genes. The 710 downregulated DEGs were enriched in 1,920 GO terms, including 1059 in BP, 377 in CC, and 484 in MF, and the annotation obtained GO terms, such as positive regulation of growth hormone secretion and estrogen metabolic process. The 20 GO terms in BP, CC, and MF are shown in Fig. 3 C. The top 20 GO terms with significant enrichment based on P ≤ 0.05 are shown in Fig. 3 D. KEGG metabolic pathway analysis of DEGs To further clarify the contribution of specific signaling pathways to sheep fecundity, KEGG enrichment analysis was performed on the DEGs of sheep with high and low fecundity (Supplementary Table S6). A total of 826 DEGs were involved in 322 KEGG pathways (Fig. 4 A), and 19 signaling pathways were closely related to sheep reproduction (Fig. 4 B), involving oocyte meiosis (ko04114), progesterone-mediated oocyte maturation (ko04914), estrogen signaling pathway (ko04915), oxytocin signaling pathway (ko04921), and GnRH signaling pathway (ko04912). The pathways with the most enriched genes were the PI3K-Akt signaling pathway (ko04151), MAPK signaling pathway (ko04010) and mTOR signaling pathway (ko04150). The top 20 significantly enriched KEGG pathways were screened according to P ≤ 0.05 (Fig. 4 C, D). KEGG metabolic pathway analysis of DEGs in small-tailed Han sheep (XH group) Further KEGG enrichment analysis was performed on the upregulated and downregulated DEGs of small-tailed Han sheep. The results showed that 467 upregulated DEGs participated in 314 KEGG pathways; 18 reproduction-related signaling pathways were screened, the pathways with the most enriched genes being the PI3K-Akt signaling pathway (ko04151), MAPK signaling pathway (ko04010), and mTOR signaling pathway (ko04150) (Supplementary Table S7). The top 20 KEGG pathways that were significantly enriched were screened according to P ≤ 0.05 (Fig. 5 A, B), and many signaling pathways associated with tumorigenesis and cancer were also enriched, which may indicate that the hypothalamus regulates the cell activities. Overall, 359 downregulated DEGs participated in 304 KEGG pathways, and 18 reproduction-related signaling pathways, including the VEGF signaling pathway (ko04370), Hippo signaling pathway (ko04390), and GnRH signaling pathway (ko04912), were screened (Supplementary Table S7). These downregulated genes were also enriched in apoptosis-related signaling pathways. The top 20 KEGG pathways that were significantly enriched were screened according to P ≤ 0.05 (Fig. 5 C, D), in which the metabolism pathway was the class with the most enriched downregulated DEGs. Regulatory network construction Based on the GO and KEGG annotation results, 150 genes related to reproductive processes and body development were selected from the 2,114 DEGs, and a protein-protein interaction (PPI) regulatory network was constructed, including 124 nodes and 280 edges (Fig. 6 A, Supplementary Table S8). The Degree analysis method in the CytoHubba plugin was used to screen core genes from the top-ranking DEGs, and combined with GO annotation and KEGG pathway analysis results, a regulatory network of nine core genes ( GSK3B , PPP2R1B , PPP2CB , YWHAQ , YWHAZ , YWHAH , MAP2K1 , FOXO1 , and IRS1 ) was constructed (Fig. 6 B). These genes were involved in reproduction-related KEGG pathways, including the PI3K-Akt signaling pathway (ko04151), oocyte meiosis pathway (ko04114), Hippo signaling pathway (ko04390) and MAPK signaling pathway (ko04010). qRT-PCR validation of the DEGs To validate the RNA-seq results, 8 DEGs were randomly selected for expression level detection. The expression patterns of these genes were tested using qRT-PCR and compared with the RNA-seq results. The results showed that the RNA-seq and qRT-PCR results exhibited similar expression patterns (Fig. 7 ), indicating that the RNA-seq data were reliable. Discussion Early studies have shown that sheep fertility traits can be regulated by a single gene or polygenes, especially for high-fecundity breeds 24 . For example, the BMP15 gene is one of the main genes affecting sheep fecundity and is critical for the normal development of follicles, and it can reduce the mortality of cumulus cells, increase the maturation of oocytes, and thus promote early follicle development 25 . As a neuroendocrine center, the hypothalamus regulates the secretion of pituitary hormones and the activities of their target glands, including reproduction and growth 26 , but there are few relevant studies on the mechanism underlying the influence of the hypothalamus on the reproductive performance of sheep. In this study, six full-length transcriptome sequencing of the hypothalamus from small-tailed Han sheep (XH) and Wadi sheep (XL) were performed to screen DEGs, generate a regulatory network, and screen candidate genes that may be closely associated with sheep reproductive performance. Based on full-length transcriptome sequencing data, 2,114 DEGs were identified, and some signaling pathways closely associated with sheep reproduction, such as the oocyte meiosis, progesterone-mediated oocyte maturation, estrogen signaling pathway, ovarian steroidogenesis pathway, and VEGF signaling pathway, were obtained (Fig. 4 B, Supplementary Table S6). Insulin-like growth factor 1 receptor ( IGF-1R ) is the only gene enriched in the ovarian steroidogenesis pathway, and its expression in the hypothalamus of the XH group was upregulated (Supplementary Table S3). The ovarian steroidogenesis pathway is closely related to ovarian function and follicle development. IGF-1R is a receptor gene of insulin-like growth factor I (IGF-I) and plays an important role in IGF signal transduction. IGF synergizes with gonadotropins to promote ovarian cell growth and steroid production, thereby stimulating ovarian function 27 . In Bos taurus , IGF-I was found to promote embryonic development 28 . After the IGF-1R gene is knocked out in mice, mice suffer from severe intrauterine growth retardation, which is approximately 45% of the normal value 29 . In summary, we speculated that IGF-1R may play certain roles in the dynamic development of sheep follicles and the normal growth of the uterus through the ovarian steroidogenesis pathway. Angiogenesis and vasodilation are two key factors that control the increase in placental blood flow. Increased placental blood flow can enhance the function of the placenta, resulting in healthier offspring 30 . The vascular endothelial growth factor (VEGF) family comprises VEGF-A, VEGF-B, VEGF-C, VEGF-D, VEGF-E and placenta growth factor (PGF). VEGF usually refers to VEGF-A, which promotes neovasculature formation and increases vascular permeability. Previous studies have shown that the expression of fibroblast growth factor-2 (FGF-2) and VEGF is positively correlated with the growth of placental blood vessels during pregnancy 31 . This study showed that the expression level of VEGFA in the XH group was significantly higher than that in the XL group (Supplementary Table S3). VEGFA may increase the growth of placental blood vessels and blood flow to the placenta during gestation in sheep, thus enhancing placental function and allowing the embryo to receive more adequate nutrition, which may be partly responsible for the slightly higher fertility of small-tailed Han sheep compared to Wadi sheep. In this study, dopaminergic synaptic signaling pathways, as well as the valine, leucine and isoleucine degradation signaling pathways were enriched. Some studies have shown that differences in GnRH levels are one of the reasons for differences in the numbers of lambs born 32 , while dopamine can affect the secretion of GnRH to accelerate the metabolism of valine, leucine and isoleucine in high-production ewes, providing essential energy for ewes with multiple pregnancies 33 . One study revealed that injecting a mixture of the branched-chain amino acids, isoleucine and valine during the late luteal phase of the estrus cycle could increase the ovulation rate of ewes 34 , and amino acid biosynthesis signaling pathway was also enriched in the hypothalamus. These identified pathways and the DEGs in the pathways can directly or indirectly affect the reproductive process of sheep, but the specific mechanism involved remains to be further studied. This study identified nine hub genes through the PPI network, and the first was GSK3B , which was enriched in many reproduction-related pathways, such as the mTOR signaling pathway, Wnt signaling pathway, and prolactin signaling pathway. GSK3B can affect the activation of proteins responsible for the regulation of the cell cycle, thus indirectly participating in the regulation of breast cell proliferation 35 ; moreover, GSK3B can regulate protein synthesis in skeletal muscle and accelerate the regeneration of atrophic skeletal muscle 36 . In addition, the GSK3B gene is a signaling molecule of the WNT signaling pathway, and the WNT signaling pathway regulates the development of sheep embryos during the implantation period 37 . Therefore, we speculated that the GSK3B gene may indirectly affect the development of sheep embryos by regulating cell growth and proliferation. Additionally, many studies have reported the functions of other hub genes, for example MAP2K1 is involved in the regulation of steroidogenesis in mammalian ovaries, is ubiquitously expressed in the embryo and placenta, and plays a leading role in placental development. Loss of function of MAP2K1 in mice results in embryonic death 38 . The FOXO1 gene was significantly upregulated in the XH group (Supplementary Table S3), which indirectly regulates follicle development by regulating the synthesis of lipids and sterols and FOXO1 plays an important role in follicle development and corpus luteum formation in mice 39 In conclusion, these DEGs between the XH and XL groups may have an important impact on the fecundity of the ewes. The screened core genes can be used as key candidate genes to regulate sheep fecundity, but the specific mechanism involved in the regulation of sheep reproductive physiology needs further in-depth study. This study can provide reference data for further explorations of the mechanism of high reproductive performance in sheep and cultivating new sheep breeds with high fecundity. Declarations Competing interests All authors declared no competing interest. Funding This study was financially supported by grants from the National Key Research and Develop-ment Program of China (2021YFD1200901). Author Contribution Tong Wang: contributed to conception, design, acquisition, analysis, interpretation critically drafted manuscript and revised manuscript..Zhibin Ji: contributed to conception, design, acquisition, analysis, critically drafted manuscript and revised manuscript.Xue Xiao: contributed to analysis, interpretation and drafted the manuscript. Dejie Zhu: contributed to analysis, interpretation and critically revised the manuscript. Hengyi Li: contributed critically revised the manuscript. Xinyu Li: contributed to conception, design, interpretation and critically revised the manuscript.All authors approved the submitted version and agreed both to be personally accountable for the author’s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved and the resolutiondocumented in the literature. Acknowledgments The authors express their gratitude to their colleagues for the support in sample collection. We are also grateful to small-tailed Han Sheep Breeding Farm in Jiaxiang County of Shandong Province and Shandong Binzhou Animal Science & Veterinary Medicine Academy for providing raw materials. Data Availability The data that support the findings of this study are available from the tables and supplementary materials. The raw data of sequencing were deposited in GEO database (GSE275445) References Gholizadeh, M. & Esmaeili-Fard, S. M. Meta-analysis of genome-wide association studies for litter size in sheep. Theriogenology . 180 , 103–112. https://doi:10.1016/j.theriogenology (2022). Christensen, A. et al. Hormonal regulation of female reproduction. Horm. Metab. Res. 44 , 587–. 10.1055/s-0032-1306301 (2012). https:// 91. Miłosz, G., Obuchowicz, E. & Michał, Z. Neuropeptides as regulators of the hypothalamus-pituitary-gonadal (HPG) axis activity and their putative roles in stress-induced fertility disorders. Neuropeptides . 91 , 102216. http://doi:10.1016/j.npep.2021.102216 (2022). Moore, A. M., Novak, A. G. & Lehman, M. N. KNDy Neurons of the Hypothalamus and Their Role in GnRH Pulse Generation: an Update. Endocrinology . 165 , bqad194. https://doi:10.1210/endocr/bqad194 (2023). Boegl, M. et al. The LH:FSH Ratio in Functional Hypothalamic Amenorrhea: An Observational Study. J. Clin. Med. 13 , 1201. https://doi:10.3390/jcm13051201 (2024). Zhao, W. et al. The role of GnRH in Tibetan male sheep and goat reproduction. Reprod. Domest. Anim. 58 , 1179–1187. https://doi:10.1111/rda.14432 (2023). Zhou, A. et al. Whole-genome resequencing reveals new mutations in candidate genes for Beichuan-white goat prolificacya. Anim. Biotechnol. 35 , 2258166. https://doi:10.1080/10495398 (2024). Lv, X. et al. Whole-genome resequencing of Dorper and Hu sheep to reveal selection signatures associated with important traits. Anim. Biotechnol. 34 , 3016–3026. https://doi:10.1080/10495398.2022.2127409 (2023). Lin, B., Hui, J. & Mao, H. Nanopore Technology and Its Applications in Gene Sequencing. Biosens. (Basel) . 11 , 214. https://doi:10.3390/bios11070214 (2021). Das, P., Pandey, P., Harishankar, A., Chandy, M. & Bhattacharya, S. A high yield DNA extraction method for medically important Candida species: A comparison of manual versus QIAcube-based automated system. Indian J. Med. Microbiol. 34 , 533–535. https://doi:10.4103/0255-0857.195360 (2016). Sarnecka, A. K. et al. DNA extraction from FFPE tissue samples - a comparison of three procedures. Contemp. Oncol. (Pozn) . 23 , 52–58. https://doi:10.5114/wo.2019.83875 (2019). De Coster, W., D'Hert, S., Schultz, D. T., Cruts, M. & Van Broeckhoven, C. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics . 34 , 2666–2669. https://doi:10.1093/bioinformatics/bty149 (2018). Shen, W., Le, S., Li, Y., Hu, F. & SeqKit A cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS One . 11 , e0163962. https://doi:10.1371/journal.pone.0163962 (2016). Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics . 34 , 3094–3100. https://doi:10.1093/bioinformatics/bty191 (2018). Li, H. et al. 1000 genome project data processing subgroup. The sequence alignment/map format and samtools. Bioinformatics . 25 , 2078–2079. https://doi:10.1093/bioinformatics/btp352 (2009). Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods . 14 , 417–419. https://doi:10.1038/nmeth.4197 (2017). Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15 , 550. https://doi:10.1186/s13059-014-0550-8 (2014). Zhao, S., Ye, Z. & Stanton, R. Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols. RNA . 26 , 903–909. https://doi:10.1261/rna.074922.120 (2020). Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25 , 25–29. 10.1038/75556 (2000). https:// Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y. & Hattori, M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 32(Database issue):D277-80. (2004). https://doi:10.1093/nar/gkh063 Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS . 16 , 284–287. https://doi:10.1089/omi.2011.0118 (2012). Szklarczyk, D. et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47 (D1), D607–D613. https://doi:10.1093/nar/gky1131 (2019). Szklarczyk, D. et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51 (D1), D638–D646. https://doi:10.1093/nar/gkac1000 (2023). Davis, G. H. Major genes affecting ovulation rate in sheep. Genet. Sel. Evol. 37 (Suppl 1), S11–23. https://doi:10.1186/1297-9686-37-S1-S11 (2005). Paulini, F. & Melo, E. O. The role of oocyte-secreted factors GDF9 and BMP15 in follicular development and oogenesis. Reprod. Domest. Anim. 10.1111/j.1439-0531.2010.01739.x (2011). https://doi 46:354 – 61. Keenan, D. M. & Veldhuis, J. D. Pulsatility of Hypothalamo-Pituitary Hormones: A Challenge in Quantification. Physiol. (Bethesda) . 31 , 34–50. https://doi:10.1152/physiol.00027.2015 (2016). Spicer, L. J. & Echternkamp, S. E. The ovarian insulin and insulin-like growth factor system with an emphasis on domestic animals. Domest. Anim. Endocrinol. 12 , 223–245. https://doi:10.1016/0739-7240(95)00021-6 (1995). Tríbulo, P. et al. Effects of sex on response of the bovine preimplantation embryo to insulin-like growth factor 1, activin A, and WNT7A. BMC Dev. Biol. 18 , 16. https://doi:10.1186/s12861-018-0176-2 (2018). Liu, J. P., Baker, J., Perkins, A. S., Robertson, E. J. & Efstratiadis, A. Mice carrying null mutations of the genes encoding insulin-like growth factor I (Igf-1) and type 1 IGF receptor (Igf1r). Cell . 75 , 59–72. https://doi:10.1016/S0092-8674(05)80084-4 (1993). Reynolds, L. P. & Redmer, D. A. Utero-placental vascular development and placental function. J. Anim. Sci. 73 , 1839–1851. https://doi:10.2527/1995.7361839x (1995). Wang, K., Jiang, Y. Z., Chen, D. B. & Zheng, J. Hypoxia enhances FGF2- and VEGF-stimulated human placental artery endothelial cell proliferation: roles of MEK1/2/ERK1/2 and PI3K/AKT1 pathways. Placenta . 30 , 1045–1051. https://doi:10.1016/j.placenta.2009.10.007 (2009). Chandolia, R. K. et al. Ultrasonography of the developing reproductive tract in ram lambs: effects of a GnRH agonist. Theriogenology . 48 , 99–117. https://doi:10.1016/S0093-691X(97)00194-5 (1997). Goodman, R. L. et al. Evidence that dopamine acts via kisspeptin to hold GnRH pulse frequency in check in anestrous ewes. Endocrinology . 153 , 5918–5927. https://doi:10.1210/en.2012-1611 (2012). Downing, J. A., Joss, J. & Scaramuzzi, R. J. A mixture of the branched chain amino acids leucine, isoleucine and valine increases ovulation rate in ewes when infused during the late luteal phase of the oestrous cycle: an effect that may be mediated by insulin. J. Endocrinol. 145 , 315–323. https://doi:10.1677/joe.0.1450315 (1995). Musumeci, G. et al. Mammary gland: From embryogenesis to adult life. Acta Histochem. 117 , 379–385. 10.1016/j.acthis.2015.02.013 (2015). https://doi Pansters, N. A. et al. Muscle-specific GSK-3beta ablation accelerates regeneration of disuse-atrophied skeletal muscle. Biochim. Biophys. Acta . 1852 , 490–506. https://doi:10.1016/j.bbadis.2014.12.006 (2015). Hayashi, K., Burghardt, R. C., Bazer, F. W. & Spencer, T. E. WNTs in the ovine uterus: potential regulation of periimplantation ovine conceptus development. Endocrinology . 148 , 3496–3506. https://doi:10.1210/en.2007-0283 (2007). Nadeau, V. et al. Map2k1 and Map2k2 genes contribute to the normal development of syncytiotrophoblasts during placentation. Development . 136 , 1363–1374. https://doi:10.1242/dev.031872 (2009). Shi, F. & LaPolt, P. S. Relationship between FoxO1 protein levels and follicular development, atresia, and luteinization in the rat ovary. J. Endocrinol. 179 , 195–203. https://doi:10.1677/joe.0.1790195 (2003). Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.zip Cite Share Download PDF Status: Published Journal Publication published 13 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 11 Sep, 2024 Reviews received at journal 11 Sep, 2024 Reviews received at journal 09 Sep, 2024 Reviewers agreed at journal 01 Sep, 2024 Reviewers agreed at journal 31 Aug, 2024 Reviewers invited by journal 30 Aug, 2024 Editor assigned by journal 30 Aug, 2024 Editor invited by journal 28 Aug, 2024 Submission checks completed at journal 26 Aug, 2024 First submitted to journal 02 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4847033","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":352742809,"identity":"a141b91e-4121-4cb7-8041-ddb587c60796","order_by":0,"name":"Tong Wang","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Wang","suffix":""},{"id":352742810,"identity":"fcc8aaf4-f331-407d-ac5f-342079b1ed72","order_by":1,"name":"Zhibin Ji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDACCYaEAx8MbOQYGBJAXGbitBycUZBmTJIWBmaeD4cSG4jWIj+74eFhHoMD6fPbk59JMFRYJzawnz2AVwvjnAMJB+cY3MndcOaZmQTDmfTEBp68BLxamCUSEg68MXiWu0EiwUyCse1wYoMEjwFeLWwgLTwGh9PlZ6R/k2D8R4QWHqCWg0AtCQw3coC2NBChRUIG6JcZBmmGG868KbZIOJZu3MaTg1+L/Oye5A8f/tjIy7enb7zxocZatp/9DH4tQKclINggJhsB9UDAfoCwmlEwCkbBKBjZAACOpUqg9LDr2QAAAABJRU5ErkJggg==","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Zhibin","middleName":"","lastName":"Ji","suffix":""},{"id":352742811,"identity":"d35de8ce-171a-47d7-a625-e59ee5d33e40","order_by":2,"name":"Xue Xiao","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Xiao","suffix":""},{"id":352742812,"identity":"7f2f223d-acf6-46a4-8292-bf63a1d333b5","order_by":3,"name":"Dejie Zhu","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Dejie","middleName":"","lastName":"Zhu","suffix":""},{"id":352742813,"identity":"da7cbefa-56d1-4491-9ef3-dc374108de28","order_by":4,"name":"Hengyi Li","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Hengyi","middleName":"","lastName":"Li","suffix":""},{"id":352742814,"identity":"2757a272-00f3-4e57-8957-ee3d0d7e71c2","order_by":5,"name":"Xinyu Li","email":"","orcid":"","institution":"Shandong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-08-02 08:51:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4847033/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4847033/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-79140-5","type":"published","date":"2024-11-13T15:56:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65291445,"identity":"9b36ccff-94da-4b27-87fe-14da7c83bb3b","added_by":"auto","created_at":"2024-09-25 17:37:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1587655,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSequencing data statistics and differential gene identification analysis.\u003c/strong\u003e(A) Statistics of reads filtering information; (B) Pearson correlation between different samples; (C) Statistics of the gene numbers obtained from sequencing; (D) Number of differentially expressed genes between different groups; (E) Volcano plot of differentially expressed genes; (F) Clustering analysis heatmap of differentially expressed genes.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4847033/v1/1afb5ac8bbd4a3e53769ac37.png"},{"id":65290885,"identity":"13065bb3-5007-4870-a272-538a8919143f","added_by":"auto","created_at":"2024-09-25 17:21:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3489850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO Annotation and enrichment analysis of differentially expressed genes in the hypothalamus.\u003c/strong\u003e (A) Distribution of the top 20 GO terms in different GO Classes; (B) The top 20 significantly enriched GO terms for differentially expressed genes; (C) Distribution of the top 20 enriched GO terms for differentially expressed genes in different GO Classes.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4847033/v1/d50190a9428819e8c432570d.png"},{"id":65291313,"identity":"d1c161bc-a8ec-476f-b6d3-0e951b76093d","added_by":"auto","created_at":"2024-09-25 17:29:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3209928,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO annotation and enrichment analysis of upregulated and downregulated genes in the hypothalamus of Small-tailed Han Sheep.\u003c/strong\u003e (A) Distribution of the top 20 GO terms for upregulated genes in different GO Classes; (B) The top 20 significantly enriched GO terms for upregulated genes; (C) Distribution of the top 20 GO terms for downregulated genes in different GO Classes; (D) The top 20 significantly enriched GO terms for downregulated genes.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4847033/v1/9748eaaf093d9ef4b051bd30.png"},{"id":65290883,"identity":"0bd73c8b-6746-4f38-9e70-b70d6ec7cb86","added_by":"auto","created_at":"2024-09-25 17:21:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3818569,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG analysis of differentially expressed genes in the hypothalamus.\u003c/strong\u003e(A) Distribution of differentially expressed gene KEGG terms in different KEGG Classes; (B) Chord diagram of sheep reproduction-related pathways in the hypothalamus; (C) Distribution of the top 20 KEGG terms in different KEGG Classes; (D) The top 20 significantly enriched KEGG terms for differentially expressed genes.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4847033/v1/430cb4375ebacd85fbdd56fc.png"},{"id":65291310,"identity":"42e32f8e-4925-4322-938d-3af23e3c2aeb","added_by":"auto","created_at":"2024-09-25 17:29:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2304466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG analysis of upregulated and downregulated genes in the hypothalamus of Small-tailed Han Sheep.\u003c/strong\u003e (A) Distribution of the top 20 KEGG terms for upregulated genes in different KEGG Classes; (B) The top 20 significantly enriched KEGG terms for upregulated genes; (C) Distribution of the top 20 KEGG terms for downregulated genes in different KEGG Classes; (D) The top 20 significantly enriched KEGG terms for downregulated genes.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4847033/v1/735382f132c37ee00f09de04.png"},{"id":65290888,"identity":"3a707652-492f-430f-817f-f10c084af6f3","added_by":"auto","created_at":"2024-09-25 17:21:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5693760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of regulatory network construction. \u003c/strong\u003e(A) Genes interaction network of differentially expressed genes (DEGs); (B) Regulatory network constructed based on core genes.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4847033/v1/5115d78fc009d75a0a777647.png"},{"id":65291312,"identity":"58ef520d-2ead-4a56-a275-88978ae938f8","added_by":"auto","created_at":"2024-09-25 17:29:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":202361,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of RNA-seq data by RT-qPCR. \u003c/strong\u003e(A) RT-qPCR validation of selected differentially expressed genes (DEGs); (B) RNA-seq results of selected DEGs. RT-qPCR data are represented as relative gene expression levels, RNA-seq data are represented as TPM. ***p \u0026lt; 0.001, **p \u0026lt; 0.01, *p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4847033/v1/458313f5da5ebd2999df951a.png"},{"id":69274519,"identity":"5932a175-ce75-4e9c-8620-c56d3169c2c3","added_by":"auto","created_at":"2024-11-18 16:02:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":21504288,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4847033/v1/e3b9988b-7feb-41db-9430-6657eddee7d9.pdf"},{"id":65290891,"identity":"94268945-66b0-4d99-a247-39d339e8742d","added_by":"auto","created_at":"2024-09-25 17:21:53","extension":"zip","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":4868420,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.zip","url":"https://assets-eu.researchsquare.com/files/rs-4847033/v1/ea9eb0e49a3bda8ecd5fa14c.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of reproduction-related genes in the hypothalamus of the sheep (Ovis aries) using the ONT full-length transcriptome sequencing technology","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSheep (\u003cem\u003eOvis aries\u003c/em\u003e) is an important economic animal in China, its meat, milk and furs greatly enrich human life, and its reproductive performance is an important indicator of its economic traits\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Reproduction is a complex and important physiological process that depends on the coordination, synthesis and release of gonadal axis hormones\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The hypothalamus-pituitary-gonadal (HPG) axis underlies the endocrine control of reproduction in mammals, and together, they regulate the development and reproductive function in mammals\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. As the starting organ of the HPG axis, the hypothalamus can generate a GnRH signal to regulate the secretion of downstream hormones, including follicle-stimulating hormone (FSH) and luteinizing hormone (LH)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. These two hormones are regulatory factors of the ovary, by maintaining the function of the corpus luteum, they drive the follicle to produce progesterone (P4) and estrogen (E2)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. GnRH (gonadotropin releasing hormone) secreted by the hypothalamus can serve as an initiator of reproductive activities and plays an important role in reproductive regulation, and changes in hypothalamus function affect reproductive activities, such as follicle development, ovulation and egg laying\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Therefore, the hypothalamus is involved in the regulation of reproductive activities in mammals, and the hypothalamus is a key region in the brain that initiates reproductive activities.\u003c/p\u003e \u003cp\u003eWith the development of high-throughput sequencing technology, breed selection has entered the era of molecular breeding. Candidate gene analysis can be used to identify individual genes responsible for economically important traits, and many candidate genes associated with animal fecundity have been continuously mined\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Currently, many signaling pathways related to sheep reproductive performance have been identified through molecular biology techniques\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, but the genetic and physiological mechanisms involved remain to be further explored. Small-tailed Han sheep is a known native breed in Shandong Province, China. Small-tailed Han sheep can be in estrus for four seasons and can be bred all year round, the average annual lambing rate is approximately 267.1%, and small-tailed Han sheep are known for their high fecundity. The fecundity of Wadi sheep is lower than that of small-tailed Han sheep, Wadi sheep are often in estrus in spring and fall, and the lambing rate of primiparous ewes is only 194.7%. The differences in fertility between small-tailed Han sheep and Wadi sheep and the molecular mechanisms underlying the regulation of animal reproduction by the hypothalamus have not been studied.\u003c/p\u003e \u003cp\u003eTherefore, in this study, we used hypothalamus tissues from high-reproduction small-tailed Han sheep ewes and low-reproduction Wadi sheep ewes as the research objects. Meanwhile, full-length transcriptome sequencing technology by Oxford Nanopore Technologies (ONT)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e was used to screen and mine related genes involved in the reproduction regulation of sheep. This is of great significance for the comprehensive understanding of themechanism of the hypothalamus in regulating animal reproduction, and may provide a more theoretical basis for the future elucidation of the molecular mechanism of high reproduction in sheep.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and ethics declarations\u003c/h2\u003e \u003cp\u003eThree one-year-old small-tailed Han sheep ewes with no significant differences in weight, height, body shape or body condition, and with the same age, from small-tailed Han sheep breeding farm in Jiaxiang County, Shandong Province, China (National-level Mutton Sheep Core Breeding Farm) were selected and named as High Fecundity Group (XH). Three Wadi sheep ewes, from Binzhou Animal Husbandry and Veterinary Research Institute of Shandong Province, China (Wadi Sheep Breeding Farm of Shandong Province), were also selected with the same selection criteria as small-tailed Han sheep, and named as Low Fecundity Group (XL). The animals were sacrificed without pain after general anesthesia by intramuscular injection of Xylazine Hydrochloride injection solution, and every effort was made to reduce the suffering of the animals in the experiments. The hypothalamus tissues were removed and placed into sterile cryopreservation tubes, numbered and quickly placed in the nitrogen tank for extraction of total RNA and subsequent experiments. All experimental protocols were approved by Experimental Animal Management Committee of Shandong Agricultural University (SDAUA-2023-237), all methods were carried out in accordance with relevant guidelines and regulations, all methods are reported in accordance with ARRIVE guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arriveguidelines.org\u003c/span\u003e\u003cspan address=\"https://arriveguidelines.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and sequencing of the ONT library\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from the hypothalamus tissues of 6 experimental sheep using TRIzol reagent (Thermo Fisher Scientific, USA), the RNA purity (D260/D280 value) was determined using a NanoDrop ND-2000 (Thermo Science, USA)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, the RNA concentration was precisely quantified using a Qubit 3.0 fluorometer\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and the integrity of the RNA was examined by agarose gel electrophoresis. For the total RNA samples that passed the quality inspection, Oligo DT was used as a primer for reverse transcription, and complementary DNA (cDNA) was obtained by low-cycle polymerase chain reaction (PCR) and purified using AMPure beads. After adding sequencing adapters (including motor proteins), sequencing was performed using the FLO-PRO002 microarray. ONT library construction and all sequencing work were completed by Bena (Wuhan) Information Technology Co., Ltd. (Wuhan, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eQuality control and statistics of the sequencing data\u003c/h2\u003e \u003cp\u003eThe obtained raw sequencing data were subjected to base calling using GUPPY (version: 5.0.16) software, converted to fastq format to remove low-quality sequences and adaptor sequences, and then processed using NanoFilt 50 (version: 2.8.0; parameters: -q 7 -l) software\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e to filter out sequences with a quality value less than 7 and a length less than 50 bp to obtain effective clean reads. The SeqKit (version: 0.12.0; parameter: default) software\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e was used to count the effective clean reads. The full-length sequences in the valid sequencing data were identified using Pychopper software (version: 2.4.0; parameters: -Q 7 -z 50, Pychopper: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/nanoporetech/pychopper\u003c/span\u003e\u003cspan address=\"https://github.com/nanoporetech/pychopper\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and minimap2 (version: 2.17- r941; parameters: -ax splice -uf -k14)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and the filtered full-length sequence was aligned with the sheep reference genome (ARS-UI_Ramb_v3.0). SAMtools software (version: 1.11; parameter: flagstat)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e was used to analyze the genome alignment results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of DEGs between the high- and low- fecundity groups\u003c/h2\u003e \u003cp\u003eIn this study, transcripts per kilobase million (TPM) was used to measure gene expression levels, and gene expression quantification was performed using Salmon software (version: 1.4.0)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. DESeq2 software (version: 1.26.0)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e was used to analyze the read count data in each sample for differential expression analysis, and the screening thresholds were \u003cem\u003eP\u003c/em\u003e adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003eFoldChange| \u0026gt; 1. For the calculation of TPM, for each gene, the read counts were divided by the length (in kilobases) of the gene to obtain the reads per kilobase (RPK), and all RPK values in a sample were calculated and divided by 1,000,000 to obtain the \u0026ldquo;per million\u0026rdquo; scaling factor. Then, the RPK value was divided by the \u0026ldquo;per million\u0026rdquo; scaling factor to obtain the TPM value\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGO and KEGG enrichment analysis of DEGs\u003c/h2\u003e \u003cp\u003eThe Gene Ontology (GO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.geneontology.org/\u003c/span\u003e\u003cspan address=\"http://www.geneontology.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e19\u003c/sup\u003e was used for the GO annotation of the DEGs, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kegg.jp/\u003c/span\u003e\u003cspan address=\"https://www.kegg.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e20\u003c/sup\u003e was used to perform pathway analysis on the DEGs. GO analysis included mainly cellular component (CC), molecular function (MF) and biological process (BP). The GO items and KEGG pathways that were significantly enriched in DEGs were calculated using hypergeometric distribution, and the GO items and KEGG pathways with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered significantly enriched. The enrichment analysis software used for DEGs was ClusterProfiler (version 3.14.3) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of the regulatory network\u003c/h2\u003e \u003cp\u003eBased on the GO and KEGG enrichment results of the DEGs, Cytoscape software (version 3.10.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cytoscape.org/\u003c/span\u003e\u003cspan address=\"http://www.cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e22\u003c/sup\u003e and the STRING database (version 11.5, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e23\u003c/sup\u003e were used to construct a gene regulatory network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eValidation of DEGs by quantitative real-time PCR (qRT-PCR)\u003c/h2\u003e \u003cp\u003eTo verify the reliability of the sequencing results, eight DEGs were randomly selected from the transcriptome sequencing data. The \u003cem\u003eGAPDH\u003c/em\u003e (\u003cem\u003eOvis aries\u003c/em\u003e) gene was used as an internal reference gene for the qRT-PCR. The primers were designed using Primer 5 software (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Quantification was performed according to the instruction manual of the SYBR Green Premix Pro Taq HS qPCR Kit (Code: AG11701), and PCR amplification was performed on a LightCycler 480 system (Roche). The specific reaction conditions were as follows: denaturation at 95\u0026deg;C for 30 s, followed by for 40 cycles of amplification at 95\u0026deg;C for 5 s and 40 cycles of amplification at 60\u0026deg;C for 30 s. The relative expression levels of genes were calculated by the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method, and the significance standard was \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Livak et al., 2001). The results were visualized using GraphPad 8.0 (GraphPad Software, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eQuality assessment and reference genome comparison of sequencing data\u003c/h2\u003e \u003cp\u003eIn this study, the hypothalamus tissues of high-reproduction small-tailed Han sheep (XH) and low-reproduction Wadi sheep (XL) were selected for ONT sequencing. For the XH group (X1-X3), 9,530,456, 9,962,624, and 9,855,014 raw reads were obtained, of which 8,998,836, 9,496,791, and 9,415,631 were clean reads, accounting for 94.4%, 95.3% and 95.5% of the raw reads, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). For the XL (X4-X6) group, 4,498,641, 4,965,510 and 4,958,213 raw reads were obtained, of which 4,313,447, 4,776,002 and 4,754,922 were clean reads, accounting for 95.9%, 96.2% and 95.9% of the raw reads, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). For the XH group (X1-X3), there were 6,840,076, 7,329,826, and 7,397,089 full-length sequences, respectively, there were 3,291,592, 3,700,877, and 3,635,412 full-length sequences were also identified for the XL group (X4-X6), respectively. The full-length sequences were aligned to the reference genome, and the alignment rate was greater than 80% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, the correlation analysis of RNA-Seq data based on the gene expression levels showed that the Pearson correlation coefficient (R) between the two groups was greater than 0.96 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), indicating that the similarity in the expression patterns between the two groups of samples was high, which increased the reliability of the results. These results indicated that high-quality full-length transcriptome sequencing results were obtained.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSequencing data information and alignment statistics with reference genome for six samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClean reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMapped reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMap_rate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,530,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,998,836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,840,076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,503,728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.46%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,962,624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,496,791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,329,826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,935,547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.98%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,855,014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,415,631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,397,089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,226,032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e84.17%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,498,641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,313,447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,291,592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,209,994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.52%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,965,510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,776,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,700,877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,617,334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.74%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,958,213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,754,922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,635,412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,573,424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.29%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: X1, X2 and X3 represent the different library in small-tailed Han sheep group (high-reproduction, XH); X4, X5 and X6 represent the different library in Wadi sheep group (low-reproduction, XL).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and expression analysis of DEGs\u003c/h2\u003e \u003cp\u003eThe hypothalamus is an important organ of the hypothalamic-pituitary-ovarian (HPO) axis in female mammals, and the differential expression of genes can affect the fecundity of sheep. To further explore important regulatory factors in the hypothalamus associated with sheep reproductive ability, the DEGs between the XL and XH groups were screened according to the criteria of padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC|\u0026gt;1. A total of 25,246 genes, including 21,457 known genes and 7,482 novel genes (genes with new transcripts were counted as novel genes), were identified between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, Supplementary Table S2). A total of 2,114 DEGs were obtained in these two groups, with 1,247 upregulated genes and 867 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, Supplementary Table S3). The volcano map shows the basic distribution of the DEGs obtained by sequencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Hierarchical cluster analysis of DEGs in different samples revealed significant segregation between the XH group (X1-X3) and the XL group (X4-X6), indicating significant differences in gene expression between the two groups. In the hypothalamus, the three samples (X1-X3) of the XH group were clustered in the same cluster, and the three samples (X4-X6) of the XL group were clustered in the same cluster, indicating that the gene expression levels and patterns of the three samples within the same group were similar, and differences were also existed between different groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGO functional annotation and classification of DEGs\u003c/h2\u003e \u003cp\u003eAmong the 2,114 DEGs obtained from the hypothalamus, 1,456 DEGs were enriched in 2,968 GO terms, including 1,717 in BP, 517 in CC and 734 in MF (Supplementary Table S4). In BP, DEGs were enriched mainly in vesicle-mediated transport, translation, and protein localization to the plasma membrane; in CC, DEGs were enriched mainly in Golgi apparatus, Ribosom, and Mitochondrial respiratory chain complex IV; and in MF, DEGs were enriched mainly in structural constituent of ribosome, GTPase activity, and ubiquitin protein ligase binding. Based on \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05, among the significant DEGs between the XH and XL groups, the top 20 GO terms with significant enrichment were screened, with 10 GO terms belonging to CC, 5 GO terms belonging to BP, and 5 GO terms belonging to MF (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). Among the different GO categories, the top 20 GO terms in each of BP, CC, and MF are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGO functional annotation and classification of the DEGs in small-tailed Han sheep (XH group)\u003c/h2\u003e \u003cp\u003eTo more clearly explain the functional role of the enriched GO terms in the reproductive process, GO annotation analysis on the upregulated and downregulated DEGs in the hypothalamus of small-tailed Han sheep was performed (Supplementary Table S5). Among the 746 upregulated DEGs, 1,710 GO terms, including 937 in BP, 320 in CC and 453 in MF, were enriched, and the the GO terms related to reproductive and physiological processes in sheep, such as ovarian follicle development and in utero embryonic development were annotated and obtained. The top 20 GO terms in each of BP, CC, and MF are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. Based on \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05, the top 20 GO terms with significant enrichment were screened (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), and the most enriched GO term was integral components of the membrane, with 178 enriched genes. The 710 downregulated DEGs were enriched in 1,920 GO terms, including 1059 in BP, 377 in CC, and 484 in MF, and the annotation obtained GO terms, such as positive regulation of growth hormone secretion and estrogen metabolic process. The 20 GO terms in BP, CC, and MF are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC. The top 20 GO terms with significant enrichment based on \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eKEGG metabolic pathway analysis of DEGs\u003c/h2\u003e \u003cp\u003eTo further clarify the contribution of specific signaling pathways to sheep fecundity, KEGG enrichment analysis was performed on the DEGs of sheep with high and low fecundity (Supplementary Table S6). A total of 826 DEGs were involved in 322 KEGG pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), and 19 signaling pathways were closely related to sheep reproduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), involving oocyte meiosis (ko04114), progesterone-mediated oocyte maturation (ko04914), estrogen signaling pathway (ko04915), oxytocin signaling pathway (ko04921), and GnRH signaling pathway (ko04912). The pathways with the most enriched genes were the PI3K-Akt signaling pathway (ko04151), MAPK signaling pathway (ko04010) and mTOR signaling pathway (ko04150). The top 20 significantly enriched KEGG pathways were screened according to \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eKEGG metabolic pathway analysis of DEGs in small-tailed Han sheep (XH group)\u003c/h2\u003e \u003cp\u003eFurther KEGG enrichment analysis was performed on the upregulated and downregulated DEGs of small-tailed Han sheep. The results showed that 467 upregulated DEGs participated in 314 KEGG pathways; 18 reproduction-related signaling pathways were screened, the pathways with the most enriched genes being the PI3K-Akt signaling pathway (ko04151), MAPK signaling pathway (ko04010), and mTOR signaling pathway (ko04150) (Supplementary Table S7). The top 20 KEGG pathways that were significantly enriched were screened according to P\u0026thinsp;\u0026le;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B), and many signaling pathways associated with tumorigenesis and cancer were also enriched, which may indicate that the hypothalamus regulates the cell activities. Overall, 359 downregulated DEGs participated in 304 KEGG pathways, and 18 reproduction-related signaling pathways, including the VEGF signaling pathway (ko04370), Hippo signaling pathway (ko04390), and GnRH signaling pathway (ko04912), were screened (Supplementary Table S7). These downregulated genes were also enriched in apoptosis-related signaling pathways. The top 20 KEGG pathways that were significantly enriched were screened according to \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, D), in which the metabolism pathway was the class with the most enriched downregulated DEGs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRegulatory network construction\u003c/h2\u003e \u003cp\u003eBased on the GO and KEGG annotation results, 150 genes related to reproductive processes and body development were selected from the 2,114 DEGs, and a protein-protein interaction (PPI) regulatory network was constructed, including 124 nodes and 280 edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, Supplementary Table S8). The Degree analysis method in the CytoHubba plugin was used to screen core genes from the top-ranking DEGs, and combined with GO annotation and KEGG pathway analysis results, a regulatory network of nine core genes (\u003cem\u003eGSK3B\u003c/em\u003e, \u003cem\u003ePPP2R1B\u003c/em\u003e, \u003cem\u003ePPP2CB\u003c/em\u003e, \u003cem\u003eYWHAQ\u003c/em\u003e, \u003cem\u003eYWHAZ\u003c/em\u003e, \u003cem\u003eYWHAH\u003c/em\u003e, \u003cem\u003eMAP2K1\u003c/em\u003e, \u003cem\u003eFOXO1\u003c/em\u003e, and \u003cem\u003eIRS1\u003c/em\u003e) was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). These genes were involved in reproduction-related KEGG pathways, including the PI3K-Akt signaling pathway (ko04151), oocyte meiosis pathway (ko04114), Hippo signaling pathway (ko04390) and MAPK signaling pathway (ko04010).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eqRT-PCR validation of the DEGs\u003c/h2\u003e \u003cp\u003eTo validate the RNA-seq results, 8 DEGs were randomly selected for expression level detection. The expression patterns of these genes were tested using qRT-PCR and compared with the RNA-seq results. The results showed that the RNA-seq and qRT-PCR results exhibited similar expression patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), indicating that the RNA-seq data were reliable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eEarly studies have shown that sheep fertility traits can be regulated by a single gene or polygenes, especially for high-fecundity breeds\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. For example, the \u003cem\u003eBMP15\u003c/em\u003e gene is one of the main genes affecting sheep fecundity and is critical for the normal development of follicles, and it can reduce the mortality of cumulus cells, increase the maturation of oocytes, and thus promote early follicle development\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. As a neuroendocrine center, the hypothalamus regulates the secretion of pituitary hormones and the activities of their target glands, including reproduction and growth\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, but there are few relevant studies on the mechanism underlying the influence of the hypothalamus on the reproductive performance of sheep. In this study, six full-length transcriptome sequencing of the hypothalamus from small-tailed Han sheep (XH) and Wadi sheep (XL) were performed to screen DEGs, generate a regulatory network, and screen candidate genes that may be closely associated with sheep reproductive performance. Based on full-length transcriptome sequencing data, 2,114 DEGs were identified, and some signaling pathways closely associated with sheep reproduction, such as the oocyte meiosis, progesterone-mediated oocyte maturation, estrogen signaling pathway, ovarian steroidogenesis pathway, and VEGF signaling pathway, were obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Supplementary Table S6).\u003c/p\u003e \u003cp\u003eInsulin-like growth factor 1 receptor (\u003cem\u003eIGF-1R\u003c/em\u003e) is the only gene enriched in the ovarian steroidogenesis pathway, and its expression in the hypothalamus of the XH group was upregulated (Supplementary Table S3). The ovarian steroidogenesis pathway is closely related to ovarian function and follicle development. \u003cem\u003eIGF-1R\u003c/em\u003e is a receptor gene of insulin-like growth factor I (IGF-I) and plays an important role in IGF signal transduction. IGF synergizes with gonadotropins to promote ovarian cell growth and steroid production, thereby stimulating ovarian function\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In \u003cem\u003eBos taurus\u003c/em\u003e, IGF-I was found to promote embryonic development\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. After the \u003cem\u003eIGF-1R\u003c/em\u003e gene is knocked out in mice, mice suffer from severe intrauterine growth retardation, which is approximately 45% of the normal value\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In summary, we speculated that \u003cem\u003eIGF-1R\u003c/em\u003e may play certain roles in the dynamic development of sheep follicles and the normal growth of the uterus through the ovarian steroidogenesis pathway.\u003c/p\u003e \u003cp\u003eAngiogenesis and vasodilation are two key factors that control the increase in placental blood flow. Increased placental blood flow can enhance the function of the placenta, resulting in healthier offspring\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The vascular endothelial growth factor (VEGF) family comprises VEGF-A, VEGF-B, VEGF-C, VEGF-D, VEGF-E and placenta growth factor (PGF). VEGF usually refers to VEGF-A, which promotes neovasculature formation and increases vascular permeability. Previous studies have shown that the expression of fibroblast growth factor-2 (FGF-2) and VEGF is positively correlated with the growth of placental blood vessels during pregnancy\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. This study showed that the expression level of VEGFA in the XH group was significantly higher than that in the XL group (Supplementary Table S3). VEGFA may increase the growth of placental blood vessels and blood flow to the placenta during gestation in sheep, thus enhancing placental function and allowing the embryo to receive more adequate nutrition, which may be partly responsible for the slightly higher fertility of small-tailed Han sheep compared to Wadi sheep.\u003c/p\u003e \u003cp\u003eIn this study, dopaminergic synaptic signaling pathways, as well as the valine, leucine and isoleucine degradation signaling pathways were enriched. Some studies have shown that differences in GnRH levels are one of the reasons for differences in the numbers of lambs born\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, while dopamine can affect the secretion of GnRH to accelerate the metabolism of valine, leucine and isoleucine in high-production ewes, providing essential energy for ewes with multiple pregnancies\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. One study revealed that injecting a mixture of the branched-chain amino acids, isoleucine and valine during the late luteal phase of the estrus cycle could increase the ovulation rate of ewes\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and amino acid biosynthesis signaling pathway was also enriched in the hypothalamus. These identified pathways and the DEGs in the pathways can directly or indirectly affect the reproductive process of sheep, but the specific mechanism involved remains to be further studied.\u003c/p\u003e \u003cp\u003eThis study identified nine hub genes through the PPI network, and the first was \u003cem\u003eGSK3B\u003c/em\u003e, which was enriched in many reproduction-related pathways, such as the mTOR signaling pathway, Wnt signaling pathway, and prolactin signaling pathway. \u003cem\u003eGSK3B\u003c/em\u003e can affect the activation of proteins responsible for the regulation of the cell cycle, thus indirectly participating in the regulation of breast cell proliferation\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e; moreover, \u003cem\u003eGSK3B\u003c/em\u003e can regulate protein synthesis in skeletal muscle and accelerate the regeneration of atrophic skeletal muscle\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In addition, the \u003cem\u003eGSK3B\u003c/em\u003e gene is a signaling molecule of the WNT signaling pathway, and the WNT signaling pathway regulates the development of sheep embryos during the implantation period\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Therefore, we speculated that the \u003cem\u003eGSK3B\u003c/em\u003e gene may indirectly affect the development of sheep embryos by regulating cell growth and proliferation. Additionally, many studies have reported the functions of other hub genes, for example \u003cem\u003eMAP2K1\u003c/em\u003e is involved in the regulation of steroidogenesis in mammalian ovaries, is ubiquitously expressed in the embryo and placenta, and plays a leading role in placental development. Loss of function of \u003cem\u003eMAP2K1\u003c/em\u003e in mice results in embryonic death\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The \u003cem\u003eFOXO1\u003c/em\u003e gene was significantly upregulated in the XH group (Supplementary Table S3), which indirectly regulates follicle development by regulating the synthesis of lipids and sterols and \u003cem\u003eFOXO1\u003c/em\u003e plays an important role in follicle development and corpus luteum formation in mice\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e In conclusion, these DEGs between the XH and XL groups may have an important impact on the fecundity of the ewes. The screened core genes can be used as key candidate genes to regulate sheep fecundity, but the specific mechanism involved in the regulation of sheep reproductive physiology needs further in-depth study. This study can provide reference data for further explorations of the mechanism of high reproductive performance in sheep and cultivating new sheep breeds with high fecundity.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eAll authors declared no competing interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was financially supported by grants from the National Key Research and Develop-ment Program of China (2021YFD1200901).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTong Wang: contributed to conception, design, acquisition, analysis, interpretation critically drafted manuscript and revised manuscript..Zhibin Ji: contributed to conception, design, acquisition, analysis, critically drafted manuscript and revised manuscript.Xue Xiao: contributed to analysis, interpretation and drafted the manuscript. Dejie Zhu: contributed to analysis, interpretation and critically revised the manuscript. Hengyi Li: contributed critically revised the manuscript. Xinyu Li: contributed to conception, design, interpretation and critically revised the manuscript.All authors approved the submitted version and agreed both to be personally accountable for the author\u0026rsquo;s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved and the resolutiondocumented in the literature.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors express their gratitude to their colleagues for the support in sample collection. We are also grateful to small-tailed Han Sheep Breeding Farm in Jiaxiang County of Shandong Province and Shandong Binzhou Animal Science \u0026amp; Veterinary Medicine Academy for providing raw materials.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the tables and supplementary materials. The raw data of sequencing were deposited in GEO database (GSE275445)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGholizadeh, M. \u0026amp; Esmaeili-Fard, S. M. Meta-analysis of genome-wide association studies for litter size in sheep. \u003cem\u003eTheriogenology\u003c/em\u003e. \u003cb\u003e180\u003c/b\u003e, 103\u0026ndash;112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1016/j.theriogenology\u003c/span\u003e\u003cspan address=\"https://doi:10.1016/j.theriogenology\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristensen, A. et al. Hormonal regulation of female reproduction. \u003cem\u003eHorm. Metab. Res.\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e, 587\u0026ndash;. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1055/s-0032-1306301\u003c/span\u003e\u003cspan address=\"10.1055/s-0032-1306301\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://\u003c/span\u003e\u003cspan address=\"https://\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u0026thinsp;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiłosz, G., Obuchowicz, E. \u0026amp; Michał, Z. Neuropeptides as regulators of the hypothalamus-pituitary-gonadal (HPG) axis activity and their putative roles in stress-induced fertility disorders. \u003cem\u003eNeuropeptides\u003c/em\u003e. \u003cb\u003e91\u003c/b\u003e, 102216. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi:10.1016/j.npep.2021.102216\u003c/span\u003e\u003cspan address=\"http://doi:10.1016/j.npep.2021.102216\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore, A. M., Novak, A. G. \u0026amp; Lehman, M. N. KNDy Neurons of the Hypothalamus and Their Role in GnRH Pulse Generation: an Update. \u003cem\u003eEndocrinology\u003c/em\u003e. \u003cb\u003e165\u003c/b\u003e, bqad194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1210/endocr/bqad194\u003c/span\u003e\u003cspan address=\"https://doi:10.1210/endocr/bqad194\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoegl, M. et al. The LH:FSH Ratio in Functional Hypothalamic Amenorrhea: An Observational Study. \u003cem\u003eJ. Clin. Med.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 1201. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.3390/jcm13051201\u003c/span\u003e\u003cspan address=\"https://doi:10.3390/jcm13051201\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, W. et al. The role of GnRH in Tibetan male sheep and goat reproduction. \u003cem\u003eReprod. Domest. Anim.\u003c/em\u003e \u003cb\u003e58\u003c/b\u003e, 1179\u0026ndash;1187. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1111/rda.14432\u003c/span\u003e\u003cspan address=\"https://doi:10.1111/rda.14432\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, A. et al. Whole-genome resequencing reveals new mutations in candidate genes for Beichuan-white goat prolificacya. \u003cem\u003eAnim. Biotechnol.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e, 2258166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1080/10495398\u003c/span\u003e\u003cspan address=\"https://doi:10.1080/10495398\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv, X. et al. Whole-genome resequencing of Dorper and Hu sheep to reveal selection signatures associated with important traits. \u003cem\u003eAnim. Biotechnol.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e, 3016\u0026ndash;3026. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1080/10495398.2022.2127409\u003c/span\u003e\u003cspan address=\"https://doi:10.1080/10495398.2022.2127409\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin, B., Hui, J. \u0026amp; Mao, H. Nanopore Technology and Its Applications in Gene Sequencing. \u003cem\u003eBiosens. (Basel)\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, 214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.3390/bios11070214\u003c/span\u003e\u003cspan address=\"https://doi:10.3390/bios11070214\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDas, P., Pandey, P., Harishankar, A., Chandy, M. \u0026amp; Bhattacharya, S. A high yield DNA extraction method for medically important Candida species: A comparison of manual versus QIAcube-based automated system. \u003cem\u003eIndian J. Med. Microbiol.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e, 533\u0026ndash;535. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.4103/0255-0857.195360\u003c/span\u003e\u003cspan address=\"https://doi:10.4103/0255-0857.195360\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarnecka, A. K. et al. DNA extraction from FFPE tissue samples - a comparison of three procedures. \u003cem\u003eContemp. Oncol. (Pozn)\u003c/em\u003e. \u003cb\u003e23\u003c/b\u003e, 52\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.5114/wo.2019.83875\u003c/span\u003e\u003cspan address=\"https://doi:10.5114/wo.2019.83875\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Coster, W., D'Hert, S., Schultz, D. T., Cruts, M. \u0026amp; Van Broeckhoven, C. NanoPack: visualizing and processing long-read sequencing data. \u003cem\u003eBioinformatics\u003c/em\u003e. \u003cb\u003e34\u003c/b\u003e, 2666\u0026ndash;2669. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1093/bioinformatics/bty149\u003c/span\u003e\u003cspan address=\"https://doi:10.1093/bioinformatics/bty149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen, W., Le, S., Li, Y., Hu, F. \u0026amp; SeqKit A cross-platform and ultrafast toolkit for FASTA/Q file manipulation. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, e0163962. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1371/journal.pone.0163962\u003c/span\u003e\u003cspan address=\"https://doi:10.1371/journal.pone.0163962\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, H. Minimap2: pairwise alignment for nucleotide sequences. \u003cem\u003eBioinformatics\u003c/em\u003e. \u003cb\u003e34\u003c/b\u003e, 3094\u0026ndash;3100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1093/bioinformatics/bty191\u003c/span\u003e\u003cspan address=\"https://doi:10.1093/bioinformatics/bty191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, H. et al. 1000 genome project data processing subgroup. The sequence alignment/map format and samtools. \u003cem\u003eBioinformatics\u003c/em\u003e. \u003cb\u003e25\u003c/b\u003e, 2078\u0026ndash;2079. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1093/bioinformatics/btp352\u003c/span\u003e\u003cspan address=\"https://doi:10.1093/bioinformatics/btp352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatro, R., Duggal, G., Love, M. I., Irizarry, R. A. \u0026amp; Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. \u003cem\u003eNat. Methods\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e, 417\u0026ndash;419. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1038/nmeth.4197\u003c/span\u003e\u003cspan address=\"https://doi:10.1038/nmeth.4197\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLove, M. I., Huber, W. \u0026amp; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 550. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1186/s13059-014-0550-8\u003c/span\u003e\u003cspan address=\"https://doi:10.1186/s13059-014-0550-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, S., Ye, Z. \u0026amp; Stanton, R. Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols. \u003cem\u003eRNA\u003c/em\u003e. \u003cb\u003e26\u003c/b\u003e, 903\u0026ndash;909. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1261/rna.074922.120\u003c/span\u003e\u003cspan address=\"https://doi:10.1261/rna.074922.120\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 25\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/75556\u003c/span\u003e\u003cspan address=\"10.1038/75556\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2000). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://\u003c/span\u003e\u003cspan address=\"https://\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa, M., Goto, S., Kawashima, S., Okuno, Y. \u0026amp; Hattori, M. The KEGG resource for deciphering the genome. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e 32(Database issue):D277-80. (2004). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1093/nar/gkh063\u003c/span\u003e\u003cspan address=\"https://doi:10.1093/nar/gkh063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu, G., Wang, L. G., Han, Y. \u0026amp; He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. \u003cem\u003eOMICS\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e, 284\u0026ndash;287. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1089/omi.2011.0118\u003c/span\u003e\u003cspan address=\"https://doi:10.1089/omi.2011.0118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzklarczyk, D. et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e (D1), D607\u0026ndash;D613. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1093/nar/gky1131\u003c/span\u003e\u003cspan address=\"https://doi:10.1093/nar/gky1131\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzklarczyk, D. et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e (D1), D638\u0026ndash;D646. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1093/nar/gkac1000\u003c/span\u003e\u003cspan address=\"https://doi:10.1093/nar/gkac1000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis, G. H. Major genes affecting ovulation rate in sheep. \u003cem\u003eGenet. Sel. Evol.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e (Suppl 1), S11\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1186/1297-9686-37-S1-S11\u003c/span\u003e\u003cspan address=\"https://doi:10.1186/1297-9686-37-S1-S11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaulini, F. \u0026amp; Melo, E. O. The role of oocyte-secreted factors GDF9 and BMP15 in follicular development and oogenesis. \u003cem\u003eReprod. Domest. Anim.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1439-0531.2010.01739.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1439-0531.2010.01739.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi\u003c/span\u003e\u003cspan address=\"https://doi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e46:354\u0026thinsp;\u0026ndash;\u0026thinsp;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeenan, D. M. \u0026amp; Veldhuis, J. D. Pulsatility of Hypothalamo-Pituitary Hormones: A Challenge in Quantification. \u003cem\u003ePhysiol. (Bethesda)\u003c/em\u003e. \u003cb\u003e31\u003c/b\u003e, 34\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1152/physiol.00027.2015\u003c/span\u003e\u003cspan address=\"https://doi:10.1152/physiol.00027.2015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpicer, L. J. \u0026amp; Echternkamp, S. E. The ovarian insulin and insulin-like growth factor system with an emphasis on domestic animals. \u003cem\u003eDomest. Anim. Endocrinol.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 223\u0026ndash;245. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1016/0739-7240(95)00021-6\u003c/span\u003e\u003cspan address=\"https://doi:10.1016/0739-7240(95)00021-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1995).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTr\u0026iacute;bulo, P. et al. Effects of sex on response of the bovine preimplantation embryo to insulin-like growth factor 1, activin A, and WNT7A. \u003cem\u003eBMC Dev. Biol.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1186/s12861-018-0176-2\u003c/span\u003e\u003cspan address=\"https://doi:10.1186/s12861-018-0176-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, J. P., Baker, J., Perkins, A. S., Robertson, E. J. \u0026amp; Efstratiadis, A. Mice carrying null mutations of the genes encoding insulin-like growth factor I (Igf-1) and type 1 IGF receptor (Igf1r). \u003cem\u003eCell\u003c/em\u003e. \u003cb\u003e75\u003c/b\u003e, 59\u0026ndash;72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1016/S0092-8674(05)80084-4\u003c/span\u003e\u003cspan address=\"https://doi:10.1016/S0092-8674(05)80084-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1993).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReynolds, L. P. \u0026amp; Redmer, D. A. Utero-placental vascular development and placental function. \u003cem\u003eJ. Anim. Sci.\u003c/em\u003e \u003cb\u003e73\u003c/b\u003e, 1839\u0026ndash;1851. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.2527/1995.7361839x\u003c/span\u003e\u003cspan address=\"https://doi:10.2527/1995.7361839x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1995).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, K., Jiang, Y. Z., Chen, D. B. \u0026amp; Zheng, J. Hypoxia enhances FGF2- and VEGF-stimulated human placental artery endothelial cell proliferation: roles of MEK1/2/ERK1/2 and PI3K/AKT1 pathways. \u003cem\u003ePlacenta\u003c/em\u003e. \u003cb\u003e30\u003c/b\u003e, 1045\u0026ndash;1051. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1016/j.placenta.2009.10.007\u003c/span\u003e\u003cspan address=\"https://doi:10.1016/j.placenta.2009.10.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandolia, R. K. et al. Ultrasonography of the developing reproductive tract in ram lambs: effects of a GnRH agonist. \u003cem\u003eTheriogenology\u003c/em\u003e. \u003cb\u003e48\u003c/b\u003e, 99\u0026ndash;117. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1016/S0093-691X(97)00194-5\u003c/span\u003e\u003cspan address=\"https://doi:10.1016/S0093-691X(97)00194-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoodman, R. L. et al. Evidence that dopamine acts via kisspeptin to hold GnRH pulse frequency in check in anestrous ewes. \u003cem\u003eEndocrinology\u003c/em\u003e. \u003cb\u003e153\u003c/b\u003e, 5918\u0026ndash;5927. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1210/en.2012-1611\u003c/span\u003e\u003cspan address=\"https://doi:10.1210/en.2012-1611\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDowning, J. A., Joss, J. \u0026amp; Scaramuzzi, R. J. A mixture of the branched chain amino acids leucine, isoleucine and valine increases ovulation rate in ewes when infused during the late luteal phase of the oestrous cycle: an effect that may be mediated by insulin. \u003cem\u003eJ. Endocrinol.\u003c/em\u003e \u003cb\u003e145\u003c/b\u003e, 315\u0026ndash;323. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1677/joe.0.1450315\u003c/span\u003e\u003cspan address=\"https://doi:10.1677/joe.0.1450315\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (1995).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMusumeci, G. et al. Mammary gland: From embryogenesis to adult life. \u003cem\u003eActa Histochem.\u003c/em\u003e \u003cb\u003e117\u003c/b\u003e, 379\u0026ndash;385. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.acthis.2015.02.013\u003c/span\u003e\u003cspan address=\"10.1016/j.acthis.2015.02.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi\u003c/span\u003e\u003cspan address=\"https://doi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePansters, N. A. et al. Muscle-specific GSK-3beta ablation accelerates regeneration of disuse-atrophied skeletal muscle. \u003cem\u003eBiochim. Biophys. Acta\u003c/em\u003e. \u003cb\u003e1852\u003c/b\u003e, 490\u0026ndash;506. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1016/j.bbadis.2014.12.006\u003c/span\u003e\u003cspan address=\"https://doi:10.1016/j.bbadis.2014.12.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayashi, K., Burghardt, R. C., Bazer, F. W. \u0026amp; Spencer, T. E. WNTs in the ovine uterus: potential regulation of periimplantation ovine conceptus development. \u003cem\u003eEndocrinology\u003c/em\u003e. \u003cb\u003e148\u003c/b\u003e, 3496\u0026ndash;3506. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1210/en.2007-0283\u003c/span\u003e\u003cspan address=\"https://doi:10.1210/en.2007-0283\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNadeau, V. et al. Map2k1 and Map2k2 genes contribute to the normal development of syncytiotrophoblasts during placentation. \u003cem\u003eDevelopment\u003c/em\u003e. \u003cb\u003e136\u003c/b\u003e, 1363\u0026ndash;1374. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1242/dev.031872\u003c/span\u003e\u003cspan address=\"https://doi:10.1242/dev.031872\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi, F. \u0026amp; LaPolt, P. S. Relationship between FoxO1 protein levels and follicular development, atresia, and luteinization in the rat ovary. \u003cem\u003eJ. Endocrinol.\u003c/em\u003e \u003cb\u003e179\u003c/b\u003e, 195\u0026ndash;203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi:10.1677/joe.0.1790195\u003c/span\u003e\u003cspan address=\"https://doi:10.1677/joe.0.1790195\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2003).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sheep, hypothalamus, transcriptome, reproduction, differentially expressed gene","lastPublishedDoi":"10.21203/rs.3.rs-4847033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4847033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe hypothalamus is the coordination center of the sheep (\u003cem\u003eOvis aries\u003c/em\u003e) endocrine system and plays an important role in the reproductive processes of the sheep. However, the specific mechanism by which the hypothalamus affects sheep reproductive performance remains unclear. In this study, the hypothalamus tissues of high-reproduction small-tailed Han sheep and low-reproduction Wadi sheep were collected, and full-length transcriptome sequencing by Oxford Nanopore Technologies (ONT) was performed to explore the key functional genes associated with sheep fecundity. The differentially expressed genes (DEGs) were screened and enriched using DESeq2 software through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Approximately 41.75\u0026nbsp;million clean reads were obtained from the hypothalamus tissues of high- and low-reproduction sheep; after quality control, 32,194,872 high-quality full-length sequences and 2,114 DEGs were obtained, including 1,247 upregulated genes and 867 downregulated genes (\u003cem\u003eP\u003c/em\u003e adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log2FC|\u0026gt;1). Some DEGs were enriched in oocyte meiosis, progesterone-mediated oocyte maturation, estrogen signaling pathway, GnRH signaling pathway and other development-related signaling pathways. The constructed protein-protein interaction (PPI) networks identified the reproduction-related genes, such as \u003cem\u003eGSK3B\u003c/em\u003e, \u003cem\u003ePPP2R1B\u003c/em\u003e, and \u003cem\u003ePPP2CB\u003c/em\u003e. The results of this study will enrich and supplement the genomic information available for small-tailed Han sheep and Wadi sheep, as well as expand the understanding of the molecular mechanisms underlying the regulation of animal reproduction by the hypothalamus, and also provide reference data for further investigations on the mechanism of high reproduction in sheep.\u003c/p\u003e","manuscriptTitle":"Identification of reproduction-related genes in the hypothalamus of the sheep (Ovis aries) using the ONT full-length transcriptome sequencing technology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-25 17:21:48","doi":"10.21203/rs.3.rs-4847033/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-11T18:34:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-11T13:21:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-09T17:44:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196071981348565637841758484894765693990","date":"2024-09-01T08:10:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11801194274799293202922797228113477411","date":"2024-08-31T12:22:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-31T00:34:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-31T00:33:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-28T04:06:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-26T08:14:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-08-02T08:49:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fd8314a4-35bf-4e31-aa2f-d895de09bc61","owner":[],"postedDate":"September 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":37468195,"name":"Biological sciences/Genetics/Animal breeding"},{"id":37468196,"name":"Biological sciences/Genetics/Development"},{"id":37468197,"name":"Biological sciences/Genetics/Functional genomics"},{"id":37468198,"name":"Biological sciences/Genetics/Gene expression"},{"id":37468199,"name":"Biological sciences/Genetics/Gene regulation"},{"id":37468200,"name":"Biological sciences/Genetics/Genome"},{"id":37468201,"name":"Biological sciences/Genetics/Genomics"},{"id":37468202,"name":"Biological sciences/Genetics/Sequencing"}],"tags":[],"updatedAt":"2024-11-18T15:58:39+00:00","versionOfRecord":{"articleIdentity":"rs-4847033","link":"https://doi.org/10.1038/s41598-024-79140-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-11-13 15:56:50","publishedOnDateReadable":"November 13th, 2024"},"versionCreatedAt":"2024-09-25 17:21:48","video":"","vorDoi":"10.1038/s41598-024-79140-5","vorDoiUrl":"https://doi.org/10.1038/s41598-024-79140-5","workflowStages":[]},"version":"v1","identity":"rs-4847033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4847033","identity":"rs-4847033","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}