Dataset Information

Human Ovarian Proteome - A draft map of the human ovarian proteome for tissue engineering and clinical applications

ABSTRACT: Fertility preservation research in women today is increasingly taking advantage of bioengineering techniques to develop new biomimetic materials and solutions to safeguard ovarian cell function and microenvironment in vitro and in vivo. However, available data on the human ovary are limited and fundamental differences between animal models and humans are hampering researchers in their quest for more extensive knowledge of human ovarian physiology and key reproductive proteins that need to be preserved. We therefore turned to multi-dimensional label-free mass spectrometry to analyze human ovarian cortex, as it is a high-throughput and conclusive technique providing information on the proteomic composition of complex tissues like the ovary. In-depth proteomic profiling through two-dimensional liquid chromatography-mass spectrometry, western blot, histological and immunohistochemical analyses, and data mining helped us to confidently identify 1,508 proteins. Moreover, our method allowed us to chart the most complete representation so far of the ovarian matrisome, defined as the ensemble of extracellular matrix proteins and associated factors, including more than 80 proteins. In conclusion, this study will provide a better understanding of ovarian proteomics, with a detailed characterization of the ovarian follicle microenvironment, in order to enable bioengineers to create biomimetic scaffolds for transplantation and three-dimensional in vitro culture. By publishing our proteomic data, we also hope to contribute to accelerating biomedical research into ovarian health and disease in general

INSTRUMENT(S): LTQ

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Ovarian Follicle

SUBMITTER: Didier Vertommen

LAB HEAD: Christiani Andrade Amorim

PROVIDER: PXD008183 | Pride | 2018-02-27

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

Items per page:

1 - 5 of 126

Publications

A Draft Map of the Human Ovarian Proteome for Tissue Engineering and Clinical Applications.

Ouni Emna E Vertommen Didier D Chiti Maria Costanza MC Dolmans Marie-Madeleine MM Amorim Christiani A CA

Molecular & cellular proteomics : MCP 20180223 Suppl 1

Fertility preservation research in women today is increasingly taking advantage of bioengineering techniques to develop new biomimetic materials and solutions to safeguard ovarian cell function and microenvironment <i>in vitro</i>, and <i>in vivo</i>,. However, available data on the human ovary are limited and fundamental differences between animal models and humans are hampering researchers in their quest for more extensive knowledge of human ovarian physiology and key reproductive proteins tha ...[more]

PMID: 29475978

Similar Datasets

Project description:The etiology of ovarian cancer is poorly understood, mainly due to the lack of an appropriate experimental model for studying the onset and progression of this disease. We have created a mouse model termed ERalpha d/d in which a conditional deletion of estrogen receptor alpha (ERalpha) gene occurred in the anterior pituitary, but ERalpha expression remained intact in the hypothalamus and the ovary. The loss of negative-feedback regulation by estrogen (E) at the level of the pituitary led to elevated production of luteinizing hormone (LH) by this tissue. Hyperstimulation of ovarian cells by LH resulted in increased steroidogenesis, leading to high circulating levels of progesterone, testosterone and E. The ERalpha d/d mice exhibited formation of palpable ovarian epithelial tumors starting at 5 months of age, and by 12 months, most mice carrying these tumors died. Besides proliferating epithelial cells, these tumors also contained an expanded population of stromal cells, which express P450 aromatase suggesting that these cells acquired the ability to synthesize E. In ERalpha d/d mice, in response to the E produced by the stromal cells, the ERalpha signaling is accentuated in the ovarian epithelial cells, triggering increased ERalpha-dependent gene expression, abnormal cell proliferation, and tumorigenesis. The ERalpha d/d animal model of ovarian epithelial tumorigenesis will serve as a powerful tool for exploring the involvement of E-dependent signaling pathways in the etiology of ovarian cancer. To identify aberrantly regulated genes in epithelial ovarian tumors, we performed gene expression profling of ovarian tumor tissue isolated from ERaplha d/d mice and normal ovary tissue isolated from ERaplha f/f control mice. The ERalpha d/d mouse model was created via conditional deletion of ERalpha by employing the Cre-LoxP strategy. Transgenic mice expressing Cre recombinase under the control of progesterone receptor (PR) promoter, termed PR-cre mice, were crossed with mice harboring the M-bM-^@M-^XfloxedM-bM-^@M-^Y ERalpha gene (ERalpha f/f) to create the ERalpah d/d mice in which the ERalpha gene is deleted in cells expressing PR. Ovarian tumor tissue was isolated from ERalpha d/d mice and normal ovary tissue was isolated from ERalpha f/f mice at 5 months and 10 months of age. Tissues were snap frozen and total RNA was isolated. Total RNA was pooled from 3 mice for each sample subjected to microarray analysis.

Project description:Thiele2013 - Ovary ovarian stroma cells The model of ovary ovarian stroma cells metabolism is derived from the community-driven global reconstruction of human metabolism (version 2.02, MODEL1109130000 ). This model is described in the article: A community-driven global reconstruction of human metabolism. Thiele I, et al . Nature Biotechnology Abstract: Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2x more reactions and ~1.7x more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/. This model is hosted on BioModels Database and identified by: MODEL1310110029 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

			Action	DRS
	GYNE361_fresh.msf	Msf
	GYNE361_fresh2.msf	Msf
	GYNE361_fresh2_frac06.RAW	Raw
	GYNE361_fresh2_frac07.RAW	Raw
	GYNE361_fresh2_frac08.RAW	Raw