ArticleFrontiers in genetics2019
mRNA and miRNA Transcriptome Profiling of Granulosa and Theca Layers From Geese Ovarian Follicles Reveals the Crucial Pathways and Interaction Networks for Regulation of Follicle Selection.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 41 citations in OpenAlex.
- EV-derived small non-coding RNAs from porcine follicular fluid regulate follicle development using Pandora sequence.Frontiers in veterinary science · 2026Article
- Transcriptome Analysis Reveals the Molecular Mechanism of PLIN1 in Goose Hierarchical and Pre-Hierarchical Follicle Granulosa Cells.Animals : an open access journal from MDPI · 2025Article
- Multi-omics and AI-driven advances in miRNA-mediated hair follicle regulation in cashmere goats.Frontiers in veterinary science · 2025Review
- tsRNA-00764 Regulates Estrogen and Progesterone Synthesis and Lipid Deposition by Targeting PPAR-γ in Duck Granulosa Cells.International journal of molecular sciences · 2024Article
- miRNA profiling of chicken follicles during follicular development.Scientific reports · 2024Article
- Estrogen Receptor Gene 1 (Biology · 2023Article
- Transcriptome Profiling of Goose Ovarian Follicle Granulosa Cells Reveals Key Regulatory Networks for Follicle Selection.Animals : an open access journal from MDPI · 2023Article
- Article
- miR-202-5p Inhibits Lipid Metabolism and Steroidogenesis of Goose Hierarchical Granulosa Cells by Targeting ACSL3.Animals : an open access journal from MDPI · 2023Article
- Identification of microRNAs implicated in modulating resveratrol-induced apoptosis in porcine granulosa cells.Frontiers in cell and developmental biology · 2023Article
- Mechanism ofGenes · 2022Article
- Comparative Transcriptome Analysis Provides Novel Insights into the Effect of Lipid Metabolism on Laying of Geese.Animals : an open access journal from MDPI · 2022Article
- Identification of Prognostic Fatty Acid Metabolism lncRNAs and Potential Molecular Targeting Drugs in Uveal Melanoma.Computational and mathematical methods in medicine · 2022Article
- Genetic Basis of Follicle Development in Dazu Black Goat by Whole-Transcriptome Sequencing.Animals : an open access journal from MDPI · 2021Article
- Identification of the Key microRNAs and miRNA-mRNA Interaction Networks during the Ovarian Development of Hens.Animals : an open access journal from MDPI · 2020Article
- Metabolomic Analysis ofGenes · 2020Article
- Characterization of microRNAs during Embryonic Skeletal Muscle Development in the Shan Ma Duck.Animals : an open access journal from MDPI · 2020Article
- Article
- Dynamics of the Transcriptome and Accessible Chromatin Landscapes During Early Goose Ovarian Development.Frontiers in cell and developmental biology · 2020Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Follicle development is characterized by the recruitment, growth, selection, and dominance of follicles, and follicle selection determines the lifetime reproductive performance. However, in birds, the molecular mechanisms underlying follicle selection still remain elusive. This study analyzed genome-wide changes in the mRNA and miRNA expression profiles in both the granulosa and theca layers of geese ovarian follicles before selection (4-6- and 8-10-mm follicles) and after selection (F5). The sequencing results showed that a higher number of both differentially expressed (DE) mRNAs and DE miRNAs were identified between 8-10-mm and F5 follicles compared with those between the 4-6- and 8-10-mm follicles, especially in the granulosa layer. Moreover, a Short Time-series Expression Miner analysis identified a large number of DE mRNAs and DE miRNAs that are associated with follicle selection. The functional enrichment analysis showed that DE genes in the granulosa layer during follicle selection were mainly enriched in five pathways related to junctional adhesion and two pathways associated with lipid metabolism. Additionally, an interaction network was constructed to visualize interactions among protein-coding genes, which identified 53 junctional adhesion- and 15 lipid regulation-related protein-coding genes. Then, a co-expression network between mRNAs and miRNAs in relation to junctional adhesion was also visualized and mainly included
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