Comparative analysis of differentially expressed genes between the ovaries from pregnant and nonpregnant goats using RNA-Seq

被引:14
|
作者
Quan, Qing [1 ,3 ]
Zheng, Qi [1 ,2 ]
Ling, Yinghui [1 ,2 ]
Fang, Fugui [1 ,2 ]
Chu, Mingxing [4 ]
Zhang, Xiaorong [1 ,2 ]
Liu, Yong [5 ]
Li, Wenyong [5 ]
机构
[1] Anhui Agr Univ, Coll Anim Sci & Technol, Hefei 230036, Anhui, Peoples R China
[2] Local Anim Genet Resources Conservat & Biobreedin, Hefei 230036, Anhui, Peoples R China
[3] Anhui Agr Univ, Coll Econ & Technol, Hefei 230036, Anhui, Peoples R China
[4] CAAS, Minist Agr, Key Lab Farm Anim Genet Resources & Germplasm Inn, Beijing 100193, Peoples R China
[5] Fuyang Normal Univ, Key Lab Embryo Dev & Reprod Regulat Anhui Prov, Fuyang 236037, Anhui, Peoples R China
基金
中国国家自然科学基金;
关键词
Differentially expressed genes; Ovary; Pregnant and nonpregnant; Goat; RNA-Seq; MESSENGER-RNA; GROWTH-FACTOR; FOLLICLE GROWTH; RECEPTOR; MICRORNAS; FAMILY; BMP15; TOOL;
D O I
10.1186/s40709-019-0095-9
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
BackgroundA multitude of genes tightly regulate ovarian follicular development and hormone secretion. These complex and coordinated biological processes are altered during pregnancy. In order to further understand the regulatory role of these genes during pregnancy, it is important to screen the differentially expressed genes (DEGs) in the ovaries of pregnant and nonpregnant mammals. To detect the genes associated with the development of pregnancy in goats, DEGs from the ovaries from pregnant and nonpregnant Anhui white goats (pAWGs and nAWGs, respectively) were analyzed using RNA sequencing technology (RNA-Seq).ResultsIn this study, 13,676,394 and 13,549,560 clean reads were generated from pAWGs and nAWGs, respectively, and 1724 DEGs were identified between the two libraries. Compared with nAWGs, 1033 genes were upregulated and 691 genes were downregulated in pAWGs, including PGR, PRLR, STAR and CYP19A1, which play important roles in goat reproduction. Gene Ontology analysis showed that the DEGs were enriched for 49 functional GO terms. Kyoto Encyclopedia of Genes and Genomes analysis revealed that 397 DEGs were significantly enriched in 13 pathways, including cell cycle, cytokine-cytokine receptor interaction and steroid biosynthesis, suggesting that the genes may be associated with cell cycle regulation, follicular development and hormone secretion to regulate the reproduction process. Additionally, quantitative real-time PCR was used to verify the reliability of the RNA-Seq data.ConclusionsThe data obtained in this work enrich the genetic resources of goat and provide a further understanding of the complex molecular regulatory mechanisms occurring during the development of pregnancy and reproduction in goats. The DEGs screened in this study may play an important role in follicular development and hormone secretion and they would provide scientific basis for related research in the future.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Exploring differentially expressed genes in the ovaries of uniparous and multiparous goats using the RNA-Seq (Quantification) method
    Ling, Ying-Hui
    Xiang, Hao
    Li, Yun-Sheng
    Liu, Ya
    Zhang, Yun-Hai
    Zhang, Zi-Juan
    Ding, Jian-Ping
    Zhang, Xiao-Rong
    GENE, 2014, 550 (01) : 148 - 153
  • [2] Comparative RNA-Seq analysis of differentially expressed genes in the testis and ovary of Takifugu rubripes
    Wang, Zhicheng
    Qiu, Xuemei
    Kong, Derong
    Zhou, Xiaoxu
    Guo, Zhongbao
    Gao, Changfu
    Ma, Shuai
    Hao, Weiwei
    Jiang, Zhiqiang
    Liu, Shengcong
    Zhang, Tao
    Meng, Xuesong
    Wang, Xiuli
    COMPARATIVE BIOCHEMISTRY AND PHYSIOLOGY D-GENOMICS & PROTEOMICS, 2017, 22 : 50 - 57
  • [3] Identification of Differentially Expressed Genes in Porcine Ovaries at Proestrus and Estrus Stages Using RNA-Seq Technique
    Yang, Songbai
    Zhou, Xiaolong
    Pei, Yue
    Wang, Han
    He, Ke
    Zhao, Ayong
    BIOMED RESEARCH INTERNATIONAL, 2018, 2018
  • [4] Identification of differentially expressed genes in the development of osteosarcoma using RNA-seq
    Yang, Yihao
    Zhang, Ya
    Qu, Xin
    Xia, Junfeng
    Li, Dongqi
    Li, Xiaojuan
    Wang, Yu
    He, Zewei
    Li, Su
    Zhou, Yonghong
    Xie, Lin
    Yang, Zuozhang
    ONCOTARGET, 2016, 7 (52) : 87194 - 87205
  • [5] RNA-Seq analysis of differentially expressed genes in rice under photooxidation
    J. Ma
    B. -B. Zhang
    F. Wang
    M. -M. Sun
    W. -J. Shen
    C. Lv
    Z. Gao
    G. -X. Chen
    Russian Journal of Plant Physiology, 2017, 64 : 698 - 706
  • [6] Exploring differentially expressed genes in the ovaries of estrous and anestrous Qira black sheep using RNA-seq technique
    Zeng, X. C.
    Chen, H. Y.
    Jia, B.
    Shi, H. C.
    Mirenisha
    Zhang, Y. S.
    Shen, H.
    INDIAN JOURNAL OF ANIMAL SCIENCES, 2016, 86 (02): : 158 - 162
  • [7] RNA-Seq Analysis of Differentially Expressed Genes in Rice under Photooxidation
    Ma, J.
    Zhang, B. -B.
    Wang, F.
    Sun, M. -M.
    Shen, W. -J.
    Lv, C.
    Gao, Z.
    Chen, G. -X.
    RUSSIAN JOURNAL OF PLANT PHYSIOLOGY, 2017, 64 (05) : 698 - 706
  • [8] Robust identification of differentially expressed genes from RNA-seq data
    Shahjaman, Md
    Mollah, Md Manir Hossain
    Rahman, Md Rezanur
    Islam, S. M. Shahinul
    Mollah, Md Nurul Haque
    GENOMICS, 2020, 112 (02) : 2000 - 2010
  • [9] Detecting differentially expressed genes from RNA-seq data using fuzzy clustering
    Ando, Yuki
    Shimokawa, Asanao
    INTERNATIONAL JOURNAL OF BIOSTATISTICS, 2024,
  • [10] Detection of differentially expressed genes using feature selection approach from RNA-seq
    Piao, Yongjun
    Ryu, Keun Ho
    2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP), 2017, : 304 - 308