Space- and Time-Resolved Metabolomics of a High-Grade Serous Ovarian Cancer Mouse Model

Abstract

The dismally low survival rate of ovarian cancer patients diagnosed with high-grade serous carcinoma (HGSC) emphasizes the lack of effective screening strategies. One major obstacle is the limited knowledge of the underlying mechanisms of HGSC pathogenesis at very early stages. Here, we present the first 10-month time-resolved serum metabolic profile of a triple mutant (TKO) HGSC mouse model, along with the spatial lipidome profile of its entire reproductive system. A high-coverage liquid chromatography mass spectrometry-based metabolomics approach was applied to longitudinally collected serum samples from both TKO (n = 15) and TKO control mice (n = 15), tracking metabolome and lipidome changes from premalignant stages to tumor initiation, early stages, and advanced stages until mouse death. Time-resolved analysis showed specific temporal trends for 17 lipid classes, amino acids, and TCA cycle metabolites, associated with HGSC progression. Spatial lipid distributions within the reproductive system were also mapped via ultrahigh-resolution matrix-assisted laser desorption/ionization (MALDI) mass spectrometry and compared with serum lipid profiles for various lipid classes. Altogether, our results show that the remodeling of lipid and fatty acid metabolism, amino acid biosynthesis, TCA cycle and ovarian steroidogenesis are critical components of HGSC onset and development. These metabolic alterations are accompanied by changes in energy metabolism, mitochondrial and peroxisomal function, redox homeostasis, and inflammatory response, collectively supporting tumorigenesis.

Monitoring and modelling the dynamics of the cellular glycolysis pathway: A review and future perspectives

“…Metabolic flux analysis describes the true flux through the pathway at a given state, but to get a dynamic view of the system which would describe the systems transition through different states at a molecular level would require a wide array of experimentation [ 171 ] which would again encounter the above mentioned challenges. Visualising the dynamics of the single-cell metabolome and proteome is possible using time-lapse microscopy or using time-resolved omics experiments [ [172] , [173] , [174] ].…”

Targeted Microchip Capillary Electrophoresis-Orbitrap Mass Spectrometry Metabolomics to Monitor Ovarian Cancer Progression

“…The higher sample throughput should prove beneficial when investigating very large animal or human cohorts. Additionally, the time-course trajectories of serine, threonine, citrulline, ornithine and tryptophan-5 out of the 40 metabolites targeted in this study-have been previously characterized by non-targeted ultra-high performance liquid chromatography (UHPLC)-MS [27]. A comparison of the µCE-HRMS dataset with previously published time-resolved UHPLC-MS data revealed that µCE-HRMS was able to capture identical temporal trends for all five analytes (Figures 3 and S3)-only the µCE-HRMS data for ornithine was slightly noisier than the UHPLC-MS data.…”

“…Serum samples were collected from triple-mutant (TKO) p53-Dicer1-Pten mice as described elsewhere [16,27]. Briefly, TKO p53 LSL-R172H/+ Dicer1 flox/flox Pten flox/flox Amhr2 cre/+ mice were generated by mating p53 LSL-R172H/+ Dicer1 flox/flox Pten flox/flox female mice with Dicer1 flox/flox Pten flox/flox Amhr2 cre/+ male mice.…”

Identification of Novel Drugs Targeting Cell Cycle Regulators for the Treatment of High-Grade Serous Ovarian Cancer via Integrated Bioinformatics Analysis

“…Ovarian cancer (OV) is the most lethal gynecologic malignancy, with an estimated 313,959 new cases and 207,252 deaths occurring in 2020 [1]. High-grade serous ovarian carcinoma (HGSC) is the most common and fatal type of OV, accounting for 70-80% of OV deaths [2]. Especially, HGSC is characterized by drug resistance and poor prognosis, posing a serious threat to women's health.…”