Dataset Information

Animal Models to Understand the Etiology and Pathophysiology of Polycystic Ovary Syndrome.

ABSTRACT: More than 1 out of 10 women worldwide are diagnosed with polycystic ovary syndrome (PCOS), the leading cause of female reproductive and metabolic dysfunction. Despite its high prevalence, PCOS and its accompanying morbidities are likely underdiagnosed, averaging > 2 years and 3 physicians before women are diagnosed. Although it has been intensively researched, the underlying cause(s) of PCOS have yet to be defined. In order to understand PCOS pathophysiology, its developmental origins, and how to predict and prevent PCOS onset, there is an urgent need for safe and effective markers and treatments. In this review, we detail which animal models are more suitable for contributing to our understanding of the etiology and pathophysiology of PCOS. We summarize and highlight advantages and limitations of hormonal or genetic manipulation of animal models, as well as of naturally occurring PCOS-like females.

SUBMITTER: Stener-Victorin E

PROVIDER: S-EPMC7279705 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Animal Models to Understand the Etiology and Pathophysiology of Polycystic Ovary Syndrome.

Stener-Victorin Elisabet E Padmanabhan Vasantha V Walters Kirsty A KA Campbell Rebecca E RE Benrick Anna A Giacobini Paolo P Dumesic Daniel A DA Abbott David H DH

Endocrine reviews 20200701 4

More than 1 out of 10 women worldwide are diagnosed with polycystic ovary syndrome (PCOS), the leading cause of female reproductive and metabolic dysfunction. Despite its high prevalence, PCOS and its accompanying morbidities are likely underdiagnosed, averaging > 2 years and 3 physicians before women are diagnosed. Although it has been intensively researched, the underlying cause(s) of PCOS have yet to be defined. In order to understand PCOS pathophysiology, its developmental origins, and how t ...[more]

PMID: 32310267

Similar Datasets

Project description:Objective This study aimed to evaluate the efficacy of Chinese herbal medicine (CHM) on ovarian mass, weight, sex hormone disorders, and insulin resistance in animal models of polycystic ovary syndrome (PCOS). Methods This systematic review and meta-analysis was conducted through a comprehensive search in three databases to find studies testing CHM in animal models of PCOS. Two researchers independently reviewed the retrieval, extraction, and quality assessment of the dataset. The pooled effects were calculated using random-effect models; heterogeneity was explored through subgroup analysis; and stability was assessed through sensitivity analysis. In addition, publication bias was assessed using the Egger's bias test. Results Fifteen studies with twelve mice and 463 rats published from 2016 to 2021 met the inclusion criteria. The results of primary outcomes revealed that CHM therapy was significantly different with control animals in ovarian mass and testosterone (SMD, −1.01 (95% CI, −1.58, −1.45); SMD, −1.62 (95% CI, −2.07, −1.16), respectively). The secondary outcomes as well showed an overall positive effect of CHM compared with control animals in weight (SMD, −1.02 (95% CI, −1.39, −0.65)), follicle-stimulating hormone (FSH) (SMD, 0.58 (95% CI, 0.19, 0.97)), luteinizing hormone (LH) (SMD, −0.94 [95% CI, −1.25, −0.64)), homeostasis model assessment-insulin resistance (HOMA-IR) (SMD, −1.24 (95% CI, −1.57, −0.92)). Subgroup analyses indicated that PCOS induction drug, formula composition, random allocation, and assessment of model establishment were relevant factors that influenced the effects of interventions. The stability of the meta-analysis was showed robust through sensitivity analysis. The publication bias was substantial. Conclusions Administration with CHM revealed a statistically positive effect on ovarian mass, weight, sex hormone disorders, and insulin resistance. Moreover, these data call for further high-quality studies investigating the underlying mechanism in more depth.

Project description:PurposeThis study aimed to identify differentially methylated genes (DMGs) and differentially expressed genes (DEGs) to investigate new biomarkers for the diagnosis and treatment of polycystic ovary syndrome (PCOS).MethodsTo explore the potential biomarkers of PCOS diagnosis and treatment, we performed methyl-binding domain sequencing (MBD-seq) and RNA sequencing (RNA-seq) on ovarian granulosa cells (GCs) from PCOS patients and healthy controls. MBD-seq was also performed on the ovarian tissue of constructed prenatally androgenized (PNA) mice. Differential methylation and expression analysis were implemented to identify DMGs and DEGs, respectively. The identified gene was further verified by real-time quantitative PCR (RT-qPCR) and methylation-specific PCR (MSP) in clinical samples. Furthermore, ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) was carried out on PCOS patients and healthy controls to identify differential lipid metabolites.ResultsCompared to the control group, 13,526 DMGs related to the promoter region and 2429 DEGs were found. The function analysis of DMGs and DEGs showed that they were mainly enriched in glycerophospholipid, ovarian steroidogenesis, and other lipid metabolic pathways. Moreover, 5753 genes in DMGs related to the promoter region were screened in the constructed PNA mice. Integrating the DMGs data from PCOS patients and PNA mice, we identified the following 8 genes: CDC42EP4, ERMN, EZR, PIK3R1, ARHGEF18, NECTIN2, TSC2, and TACSTD2. RT-qPCR and MSP verification results showed that the methylation and expression of TACSTD2 were consistent with sequencing data. Additionally, 15 differential lipid metabolites were shown in the serum of PCOS patients. The differential lipids were involved in glycerophospholipid and glycerolipid metabolism.ConclusionUsing integration of methylome and lipid metabolites profiling we identified 8 potential epigenetic markers and 15 potential lipid metabolite markers for PCOS. Our results suggest that aberrant DNA methylation and lipid metabolite disorders may provide novel insights into the diagnosis and etiology of PCOS.

Dataset Information

Animal Models to Understand the Etiology and Pathophysiology of Polycystic Ovary Syndrome.

Publications

Animal Models to Understand the Etiology and Pathophysiology of Polycystic Ovary Syndrome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets