Identification of transcriptome differences in goat ovaries at the follicular phase and the luteal phase using an RNA-Seq method
Introduction
The goat is an important domestic animal that provides meat, skin, milk, hair and other resources to humans, and reproduction is an important process that affects the efficiency of goat farming. The female reproductive organs, the ovaries, have a remarkable influence on goat reproduction and play different roles in the follicular phase and the luteal phase. During the follicular phase, follicles in the ovarian cortex develop into dominant follicles [1]. Oocytes grow within these follicles, and gene transcription is activated, after which large numbers of maternal factors accumulate in the cytoplasm [2]. During the luteal phase, the corpus luteum on the ovarian cortex secretes progesterone, an indispensable factor for pregnancy maintenance and childbirth initiation [3]. The different functions of follicular-phase ovaries and luteal-phase ovaries are likely related to differences in gene expression. Previous research has identified a total of 2167 differentially expressed genes (DEGs) in porcine ovaries between the proestrus and estrus stages [4]. In our previous study, we investigated DEGs in the ovaries of uniparous and multiparous goats [5]. We identified a total of 2201 DEGs, 12 of which may be associated with high prolificacy. In another study, we investigated the genome-wide expression profiles of lncRNAs and transcripts of uncertain coding potential (TUCPs) in goat ovaries at different estrus phases [6]. We identified a total of 4926 differentially expressed lncRNAs and 1454 TUCPs, and some lncRNAs/TUCPs potentially associated with goat reproduction were filtered. However, transcriptomic differences in goat ovaries between the follicular and luteal phases need to be further studied to elucidate the molecular mechanisms of goat reproduction.
Many methods, such as serial analysis of gene expression (SAGE) [7], mRNA differential display reverse transcription PCR (DDRT-PCR) [8], and gene chip methods [9], have been used to analyze mammalian mRNA expression. However, these technologies have their own shortcomings, and the results they yield cannot fully represent true DEGs. RNA sequencing (RNA-Seq), a newly developed method for complete transcriptome shotgun sequencing with high repeatability and a wide detection range, is a powerful tool for RNA analysis [10]. In recent years, RNA-Seq has been widely used to study ovarian follicle development in pigs [4], cattle [11] and sheep [12]. RNA-Seq is thus an appropriate method for studying transcriptome differences in goat ovaries between the follicular phase and the luteal phase.
To comprehensively study the functions of ovaries during different periods, we explored the differences in gene expression in whole ovaries at different phases. We used RNA-Seq technology to obtain genome-wide expression profiles of mRNA in goat ovaries at the follicular and luteal phases and identified the genomic features of the mRNA molecules. The results of this study will provide theoretical and basic data supporting further research on the roles of mRNAs in the regulation of goat reproduction.
Section snippets
Animals
The experiments were carried out according to the guidelines for the care and use of laboratory animals of China. The Institutional Animal Care and Use Committee of Fuyang Normal University approved this study (BR1712005). Twenty female goats were sampled from an Anhui White goat population (1–2 years old) kept at a local farm in Fuyang, Anhui Province, China.
Collection of goat ovaries
Goat ovaries were collected from 20 female goats. Only one ovary per goat was selected for RNA-Seq. Ovaries with multiple follicles and
Genomic characteristics of goat ovary mRNAs in the luteal phase and follicular phase
Two libraries were sequenced from follicular-phase and luteal-phase ovaries; the two libraries generated 650,591,998 and 492,659,628 raw data reads, respectively, for a total of 1,164,144,548 raw data reads. After filtering the raw data, 1,122, 014, 112 clean data reads were obtained, representing 96.38% of the total reads. (Fig. 1A/B). The library of the follicular-phase ovaries produced 629,354,484 clean data reads; approximately 95.496% of these sequences could be mapped to the genome, and
Discussion
Reproduction is a complex physiological process regulated by multiple genes and pathways. The ovaries, the main female reproductive organs, directly mediate ovulation and reproductive hormone secretion, which play different roles during reproduction [6]. In this study, the expression patterns of differentially expressed mRNAs in goat ovaries at the follicular stage and the luteal stage were studied. RNA-Seq was performed on follicular-phase and luteal-phase goat ovaries, yielding 1,164,144,548
Conclusion
In this study, we conducted genome-wide RNA-Seq on follicular- and luteal-phase ovaries from goats and analyzed them by bioinformatics methods. The DEGs were identified, their functions were analyzed, and mRNAs associated with hormone secretion and oocyte maturation were screened. We found 1727 mRNAs that were highly expressed in the luteal phase, many of which were related to hormone synthesis, and 2043 mRNAs that were highly expressed in the follicular phase, most of which were related to
Availability of data and materials.
The sequencing data were submitted to the NCBI Genome Expression Omnibus (Accession Number: GSE120144) at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120144.
Ethics approval
The Institutional Animal Care and Use Committee of Fuyang Normal University approved the present study. All experimental procedures involving goats were performed according to the Regulations for the Administration of Affairs Concerning Experimental Animals (Ministry of Science and Technology, China).
CRediT authorship contribution statement
Yong Liu: Conceptualization, Validation, Formal analysis, Investigation, Writing - original draft, Project administration, Funding acquisition. Xiaoqing Wu: Methodology, Validation, Investigation, Writing - review & editing, Visualization, Funding acquisition. Juan Xie: Methodology, Formal analysis, Investigation, Writing - original draft, Visualization. Wenying Wang: Methodology, Formal analysis, Investigation. Jing Xin: Formal analysis, Investigation, Resources. Feng Kong: Formal analysis,
Declaration of competing interest
The authors declare that they have no competing interests.
Acknowledgments
This study was supported by the National Natural Science Foundation of China (Nos. 31372273, 31772566, 31501958, 31501906), the Natural Science Fund Project in Anhui Province (No. 1708085MC61), the Natural Science Research Program of Anhui Higher Education Institutions (Nos. KJ2019A0518, KJ2017A334, KJ2017A339), the Anhui University Research Innovation Platform Team Project (No. [2015]49), the Anhui Province University Leading Talents Introduction and Cultivation Program (No. gwfxZD2016171),
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These two authors contributed equally to this work.