Peroxisome proliferator-activated receptor γ (PPARγ) is a key transcription factor in mammalian adipogenesis. Genome-wide approaches have identified thousands of PPARγ binding sites in mouse adipocytes and PPARγ upregulates hundreds of protein-coding genes during adipogenesis. However, no microRNA (miRNA) genes have been identified as primary PPARγ-targets. By integration of four separate datasets of genome-wide PPARγ binding sites in 3T3-L1 adipocytes we identified 98 miRNA clusters with PPARγ binding within 50 kb from miRNA transcription start sites. Nineteen mature miRNAs were upregulated ≥2-fold during adipogenesis and for six of these miRNA loci the PPARγ binding sites were confirmed by at least three datasets. The upregulation of five miRNA genes miR-103-1 (host gene Pank3), miR-148b (Copz1), miR-182/96/183, miR-205 and miR-378 (Ppargc1b) followed that of Pparg. The PPARγ-dependence of four of these miRNA loci was demonstrated by PPARγ knock-down and the loci of miR-103-1 (Pank3), miR-205 and miR-378 (Ppargc1b) were also responsive to the PPARγ ligand rosiglitazone. Finally, chromatin immunoprecipitation analysis validated in silico predicted PPARγ binding sites at all three loci and H3K27 acetylation was analyzed to confirm the activity of these enhancers. In conclusion, we identified 22 putative PPARγ target miRNA genes, showed the PPARγ dependence of four of these genes and demonstrated three as direct PPARγ target genes in mouse adipogenesis.
Background Hsa-miR-548ba expressed in ovarian granulosa cells targets PTEN and LIFR, which are essential for ovarian follicle activation and growth. The expression pattern of hsa-miR-548ba correlates with its host gene follicle-stimulating hormone receptor (FSHR), and FSH has a positive influence on hsa-miR-548ba expression. However, hsa-miR-548ba is a member of a large hsa-mir-548 family with potentially overlapping targets. The current study aims to investigate the co-expression of hsa-mir-548 family members in FSHR-positive reproductive tissues and to explore the potential co-regulation of pathways. Results For the above-described analysis, small RNA sequencing data from public data repositories were used. Sequencing results revealed that hsa-miR-548ba was expressed at the highest level in the ovarian granulosa cells and uterine myometrial samples together with another twelve and one hsa-miR-548 family members, respectively. Pathway enrichment analysis of microRNA targets in the ovarian samples revealed the hsa-miR-548ba and hsa-miR-548b-5p co-regulation of RAB geranylgeranylation in mural granulosa cells. Moreover, other hsa-mir-548 family members co-regulate pathways essential for ovarian functions (PIP3 activates AKT signalling and signalling by ERBB4). In addition to hsa-miR-548ba, hsa-miR-548o-3p is expressed in the myometrium, which separately targets the peroxisome proliferator-activated receptor alpha (PPARA) pathway. Conclusion This study reveals that hsa-mir-548 family members are expressed in variable combinations in the reproductive tract, where they potentially fulfil different regulatory roles. The results provide a reference for further studies of the hsa-mir-548 family role in the reproductive tract.
Reduction in responsiveness to gonadotropins or hyporesponsiveness may lead to the failure of in vitro fertilization (IVF), due to a low number of retrieved oocytes. The ovarian sensitivity index (OSI) is used to reflect the ovarian responsiveness to gonadotropin stimulation before IVF. Although introduced to clinical practice already years ago, its usefulness to predict clinical outcomes requires further research. Nevertheless, pathophysiological mechanisms of ovarian hyporesponse, along with advanced maternal age and in younger women, have not been fully elucidated. Follicles consist of multiple cell types responsible for a repertoire of biological processes including responding to pituitary gonadotropins necessary for follicle growth and oocyte maturation as well as ovulation. Encouraging evidence suggests that hyporesponse could be influenced by many contributing factors, therefore, investigating the variability of ovarian follicular cell types and their gene expression in hyporesponders is highly informative for increasing their prognosis for IVF live birth. Due to advancements in single-cell analysis technologies, the role of somatic cell populations in the development of infertility of ovarian etiology can be clarified. Here, somatic cells were collected from the fluid of preovulatory ovarian follicles of patients undergoing IVF, and RNA-seq was performed to study the associations between OSI and gene expression. We identified 12 molecular pathways differentially regulated between hypo- and normoresponder patient groups (FDR<0.05) from which extracellular matrix organization, post-translational protein phosphorylation, and regulation of Insulin-like Growth Factor (IGF) transport and uptake by IGF Binding Proteins were regulated age-independently. We then generated single-cell RNA-seq data from matching follicles revealing 14 distinct cell clusters. Using cell cluster-specific deconvolution from the bulk RNA-seq data of 18 IVF patients we integrated the datasets as a novel approach and discovered that the abundance of three cell clusters significantly varied between hypo- and normoresponder groups suggesting their role in contributing to the deviations from normal ovarian response to gonadotropin stimulation. Our work uncovers new information regarding the differences in the follicular gene expression between hypo- and normoresponders. In addition, the current study fills the gap in understanding the inter-patient variability of cell types in human preovulatory follicles, as revealed by single-cell analysis of follicular fluid cells.
Study question Is there a difference in the proportions of preovulatory follicle cell types between normo- and hyporesponders? Summary answer Human preovulatory follicles consist of 14 distinct cell types, 3 of them are significantly different between normo- and hyporesponder patients. What is known already Human ovarian follicles are a diverse dynamic environment for oocytes to mature and ovulate. Follicles consist of multiple cell types in layers and altogether they are responsible for a repertoire of biological processes. Dysregulations in follicular cells could result in hyporesponsiveness to ovarian stimulation. Hyporesponse might lead to the failure of IVF, due to a low number of retrieved oocytes. Its pathophysiological mechanisms have not been fully elucidated so far. This condition affects up to 25% of patients and investigating their follicular cell populations in detail is highly informative for increasing their prognosis for the live birth outcome. Study design, size, duration Study groups consisted of IVF patients with normoresponse (n = 10) and hyporesponse (n = 9) to rFSH stimulation according to antagonist protocol undergoing in vitro fertilization treatment at Nova Vita Clinic, Estonia. Hyporesponse to treatment was diagnosed if ≥ 200 IU of rFSH was administered to retrieve an oocyte. All study participants were ≤40 years of age with matched BMI and normal ovarian reserve. Women with polycystic ovary morphology and ovarian abnormalities observed by ultrasound examination were excluded. Participants/materials, setting, methods Oocyte pick-up was performed for patients after 36 hours of hCG trigger. Cumulus-oocyte complex was removed from the follicular fluid for IVF and the remaining cells were collected by centrifugation and treated with hyaluronidase and DNase. Genome-wide single-cell RNA-seq was performed for >6000 individual cells of 3 normoresponder patients each. RNA-seq was performed for pooled follicular cells of each participant. CIBERTSORTx software was used to deconvolute the proportion of cell types from bulk RNA-seq data. Main results and the role of chance By analyzing a total number of 24 213 single cells from preovulatory follicles, we identified 4 immune cell lineages (CD45+), including neutrophils, T cells, M1 and M2 macrophages along with 10 non-immune cell lineages (CD45-): epithelial cells and theca cells, cumulus cells and 7 subtypes of granulosa cells with different functional transcriptomic profiles (p < 0.05). We identified granulosa cell subclusters that were highly active in progesterone or estrogen production, extracellular matrix remodeling, ovulational process, cell migration, and apoptosis. Comparing the proportion of cell populations between normo- and hyporesponders from deconvoluted RNA-seq data (adjusted to age), we identified that 3 cell clusters varied with statistical significance (p < 0.05). These detected clusters with different proportions between the studied patient groups suggest their role in contributing to hyporesponse in a preovulatory follicle regarding patients undergoing IVF treatment. Limitations, reasons for caution The small sample size limits the study. Wider implications of the findings Our work advances understanding the heterogeneity of cell types in human preovulatory follicles. The differences in the proportions of cell types of preovulatory follicles of hyporesponders could provide valuable insight for assisting IVF treatment by introducing potential therapeutic targets to improve their live birth outcome. Trial registration number not applicable
Study question What is the difference in the composition and gene expression of pre-ovulatory follicular somatic cells between normo- and hyporesponder patients to rFSH stimulation? Summary answer Gene expression changes in the somatic cells of the preovulatory follicles of hyporesponder patients is partly attributable to distinct cell populations. What is known already Some women undergoing IVF respond suboptimally to the preceding ovarian stimulation without an indication of poor ovarian insufficiency nor advanced age. The gene expression levels in the somatic cells of their pre-ovulatory follicles have not been determined. However, the somatic cells play key roles in the transfer of gonadotrophin signals and in steroidogenesis. In the preovulatory follicle, being >20 mm in size, the somatic cells are exposed to various molecular gradients that drive their differentiation. Due to advancements in sequencing technologies the importance of somatic cell sub-populations in the etiologies of ovarian infertility can now be investigated by bioinformatic methods. Study design, size, duration Consecutive IVF patients <41 years of age were recruited at Nova Vita Clinic, Estonia, undergoing the antagonist stimulation protocol with rFSH administration. Ovarian puncture was performed for all patients after 36 hours of hCG stimulaton. rFSH dose >200 IU per one retrieved oocyte was considered as an indication for hyporesponse. Ten normo- and 9 hyporesponder patients were enrolled. Participants/materials, setting, methods Cells from the follicular fluid devoid of the cumulus oocyte complex were collected by centrifugation. RNA-seq was performed on the pooled cells of all participants. The study groups were compared by differential gene expression analysis by DESeq2. Single-cell RNA-seq was performed for >6000 individual cells of 3 normoresponder patients and cellular sub-populations were determined with Seurat package. CIBERTSORTx software was used to deconvolute the proportion of cell types from bulk RNA-seq data of all participants. Main results and the role of chance Alterations in the gene expression of ovarian somatic cells have been previously attributed to female age. We demonstrate that 407 genes are differentially expressed (FDR<0.05) between normo- and hyporesponder IVF patients after age adjustment. These genes were enriched into pathways of extracellular matrix reorganization (31 genes, FDR<0.005), post-translational protein phosphorylation (16 genes, FDR<0.006) and regulation of insulin-like growth factor transport and uptake by insulin-like growth factor binding proteins (16 genes, FDR<0.019). In addition, the sequencing of > 24 000 individual cells from 3 normoresponder patients revealed the somatic cell types of the preovulatory follicle in high resolution indicating the presence of immune cell populations (N = 4), epithelial cells, theca, cumulus cells and mural granulosa cell sub-populations (N = 7). Bioinformatic cell type deconvolution demonstrated significant differences between proportions of distinct cell sub-populations between normo- and hyporesponder patients and that several differentially expressed genes between the study groups can be attributed to a specific mural granulosa sub-population. Limitations, reasons for caution The number of study participants is small due to the high cost of the study methods. Wider implications of the findings The study demonstrates that hyporesponse to stimulation is associated with age-unrelated disturbances in gene expression of preovulatory somatic cells and that different somatic cell populations may have important underlying functions in the ovarian etiologies of infertility. Trial registration number not applicable
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.