Differentially expressed genes and gene networks involved in pig ovarian follicular atresia.
Elena TereninaStephane FabreAgnès BonnetDanielle MonniauxChristèle Robert-GraniéMagali SanCristobalJulien SarryFlorence VignolesFlorence GondretPhilippe MongetGwenola Tosser-KloppPublished in: Physiological genomics (2016)
Ovarian folliculogenesis corresponds to the development of follicles leading to either ovulation or degeneration, this latter process being called atresia. Even if atresia involves apoptosis, its mechanism is not well understood. The objective of this study was to analyze global gene expression in pig granulosa cells of ovarian follicles during atresia. The transcriptome analysis was performed on a 9,216 cDNA microarray to identify gene networks and candidate genes involved in pig ovarian follicular atresia. We found 1,684 significantly regulated genes to be differentially regulated between small healthy follicles and small atretic follicles. Among them, 287 genes had a fold-change higher than two between the two follicle groups. Eleven genes (DKK3, GADD45A, CAMTA2, CCDC80, DAPK2, ECSIT, MSMB, NUPR1, RUNX2, SAMD4A, and ZNF628) having a fold-change higher than five between groups could likely serve as markers of follicular atresia. Moreover, automatic confrontation of deregulated genes with literature data highlighted 93 genes as regulatory candidates of pig granulosa cell atresia. Among these genes known to be inhibitors of apoptosis, stimulators of apoptosis, or tumor suppressors INHBB, HNF4, CLU, different interleukins (IL5, IL24), TNF-associated receptor (TNFR1), and cytochrome-c oxidase (COX) were suggested as playing an important role in porcine atresia. The present study also enlists key upstream regulators in follicle atresia based on our results and on a literature review. The novel gene candidates and gene networks identified in the current study lead to a better understanding of the molecular regulation of ovarian follicular atresia.
Keyphrases
- genome wide identification
- genome wide
- transcription factor
- genome wide analysis
- bioinformatics analysis
- cell cycle arrest
- dna methylation
- gene expression
- copy number
- oxidative stress
- endoplasmic reticulum stress
- induced apoptosis
- rheumatoid arthritis
- stem cells
- electronic health record
- polycystic ovary syndrome
- deep learning
- machine learning
- bone marrow
- big data
- toll like receptor
- mesenchymal stem cells
- single molecule
- artificial intelligence
- pi k akt