Browsing by Author "Palaniappan, Murugesan"

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    Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer
    (Cold Spring Harbor Laboratory, 2023-01-04) Bifarin, Olatomiwa O.; Sah, Samyukta; Gaul, David A.; Moore, Samuel G.; Chen, Ruihong; Palaniappan, Murugesan; Kim, Jaeyeon; Matzuk, Martin M.; Fernández, Facundo M.; Biochemistry and Molecular Biology, School of Medicine
    Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages, and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipids alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer.
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    Ultrahigh resolution lipid mass spectrometry imaging of high-grade serous ovarian cancer mouse models
    (Frontiers Media, 2024-01-08) Ma, Xin; Botros, Andro; Yun, Sylvia R.; Park, Eun Young; Kim, Olga; Park, Soojin; Pham, Thu-Huyen; Chen, Ruihong; Palaniappan, Murugesan; Matzuk, Martin M.; Kim, Jaeyeon; Fernández, Facundo M.; Biochemistry and Molecular Biology, School of Medicine
    No effective screening tools for ovarian cancer (OC) exist, making it one of the deadliest cancers among women. Considering that little is known about the detailed progression and metastasis mechanism of OC at a molecular level, it is crucial to gain more insights into how metabolic and signaling alterations accompany its development. Herein, we present a comprehensive study using ultra-high-resolution Fourier transform ion cyclotron resonance matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to investigate the spatial distribution and alterations of lipids in ovarian tissues collected from double knockout (n = 4) and triple mutant mouse models (n = 4) of high-grade serous ovarian cancer (HGSOC). Lipids belonging to a total of 15 different classes were annotated and their abundance changes were compared to those in healthy mouse reproductive tissue (n = 4), mapping onto major lipid pathways involved in OC progression. From intermediate-stage OC to advanced HGSC, we provide direct visualization of lipid distributions and their biological links to inflammatory response, cellular stress, cell proliferation, and other processes. We also show the ability to distinguish tumors at different stages from healthy tissues via a number of highly specific lipid biomarkers, providing targets for future panels that could be useful in diagnosis.