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Quantitative proteomic profiling identifies global protein network dynamics in murine embryonic heart development.

ABSTRACT: Defining the mechanisms that govern heart development is essential for identifying the etiology of congenital heart disease. Here, quantitative proteomics was used to measure temporal changes in the proteome at critical stages of murine embryonic heart development. Global temporal profiles of the over 7,300 proteins uncovered signature cardiac protein interaction networks that linked protein dynamics with molecular pathways. Using this integrated dataset, we identified and demonstrated a functional role for the mevalonate pathway in regulating the cell cycle of embryonic cardiomyocytes. Overall, our proteomic datasets are a resource for studying events that regulate embryonic heart development and contribute to congenital heart disease.

SUBMITTER: Edwards W

PROVIDER: S-EPMC10330608 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Quantitative proteomic profiling identifies global protein network dynamics in murine embryonic heart development.

Edwards Whitney W Greco Todd M TM Miner Gregory E GE Barker Natalie K NK Herring Laura L Cohen Sarah S Cristea Ileana M IM Conlon Frank L FL

Developmental cell 20230505 12

Defining the mechanisms that govern heart development is essential for identifying the etiology of congenital heart disease. Here, quantitative proteomics was used to measure temporal changes in the proteome at critical stages of murine embryonic heart development. Global temporal profiles of the over 7,300 proteins uncovered signature cardiac protein interaction networks that linked protein dynamics with molecular pathways. Using this integrated dataset, we identified and demonstrated a functio ...[more]

PMID: 37148880

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Project description:Development of aromatase inhibitor resistant breast cancer among postmenopausal women continues to be a major clinical obstacle. Previously, our group demonstrated that as breast cancer cells transition from hormone-dependent to hormone-independent, they are associated with increased growth factor signaling, enhanced cellular motility, and the epithelial to mesenchymal transition (EMT). Given the complexity of cancer stem cells (CSC) and their implications on endocrine resistance and EMT, we sought to understand their contribution towards the development of aromatase inhibitor resistant breast cancer. Cells cultured three dimensionally as mammospheres are enriched for CSCs and more accurately recapitulates tumors in vivo. Therefore, a global proteomic analysis was conducted using letrozole resistant breast cancer cells (LTLT-Ca) mammospheres and compared to their adherent counterparts. Results demonstrated over 1000 proteins with quantitative abundance ratios were identified. Among the quantified proteins, 359 were significantly altered (p < 0.05), where 173 were upregulated and 186 downregulated (p < 0.05, fold change >1.20). Notably, midasin, a chaperone protein required for maturation and nuclear export of the pre-60S ribosome was increased 35-fold. Protein expression analyses confirmed midasin is ubiquitously expressed in normal tissue but is overexpressed in lobular and ductal breast carcinoma tissue as well as ER+ and ER- breast cancer cell lines. Functional enrichment analyses indicated that 19 gene ontology terms and one KEGG pathway were over-represented by the down-regulated proteins and both were associated with protein synthesis. Increased midasin was strongly correlated with decreased relapse free survival in hormone independent breast cancer. For the first time, we characterized the global proteomic signature of CSC-enriched letrozole-resistant cells associated with protein synthesis, which may implicate a role for midasin in endocrine resistance.

Dataset Information

Quantitative proteomic profiling identifies global protein network dynamics in murine embryonic heart development.

Publications

Quantitative proteomic profiling identifies global protein network dynamics in murine embryonic heart development.

Similar Datasets

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