Browsing by Author "Sah, Samyukta"

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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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    Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women
    (American Association for Cancer Research, 2024) Sah, Samyukta; Bifarin, Olatomiwa O.; Moore, Samuel G.; Gaul, David A.; Chung, Hyewon; Kwon, Sun Young; Cho, Hanbyoul; Cho, Chi-Heum; Kim, Jae-Hoon; Kim, Jaeyeon; Fernández, Facundo M.; Biochemistry and Molecular Biology, School of Medicine
    Background: Distinguishing ovarian cancer from other gynecological malignancies is crucial for patient survival yet hindered by non-specific symptoms and limited understanding of ovarian cancer pathogenesis. Accumulating evidence suggests a link between ovarian cancer and deregulated lipid metabolism. Most studies have small sample sizes, especially for early-stage cases, and lack racial/ethnic diversity, necessitating more inclusive research for improved ovarian cancer diagnosis and prevention. Methods: Here, we profiled the serum lipidome of 208 ovarian cancer, including 93 early-stage patients with ovarian cancer and 117 nonovarian cancer (other gynecological malignancies) patients of Korean descent. Serum samples were analyzed with a high-coverage liquid chromatography high-resolution mass spectrometry platform, and lipidome alterations were investigated via statistical and machine learning (ML) approaches. Results: We found that lipidome alterations unique to ovarian cancer were present in Korean women as early as when the cancer is localized, and those changes increase in magnitude as the diseases progresses. Analysis of relative lipid abundances revealed specific patterns for various lipid classes, with most classes showing decreased abundance in ovarian cancer in comparison with other gynecological diseases. ML methods selected a panel of 17 lipids that discriminated ovarian cancer from nonovarian cancer cases with an AUC value of 0.85 for an independent test set. Conclusions: This study provides a systemic analysis of lipidome alterations in human ovarian cancer, specifically in Korean women.
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    Space- and Time-Resolved Metabolomics of a High-Grade Serous Ovarian Cancer Mouse Model
    (MDPI, 2022-04-30) Sah, Samyukta; Ma, Xin; Botros, Andro; Gaul, David A.; Yun, Sylvia R.; Park, Eun Young; Kim, Olga; Moore, Samuel G.; Kim, Jaeyeon; Fernández, Facundo M.; Biochemistry and Molecular Biology, School of Medicine
    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.
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    Targeted Microchip Capillary Electrophoresis-Orbitrap Mass Spectrometry Metabolomics to Monitor Ovarian Cancer Progression
    (MDPI, 2022-06-09) Sah, Samyukta; Yun, Sylvia R.; Gaul, David A.; Botros, Andro; Park, Eun Young; Kim, Olga; Kim, Jaeyeon; Fernández, Facundo M.; Biochemistry and Molecular Biology, School of Medicine
    The lack of effective screening strategies for high-grade serous carcinoma (HGSC), a subtype of ovarian cancer (OC) responsible for 70-80% of OC related deaths, emphasizes the need for new diagnostic markers and a better understanding of disease pathogenesis. Capillary electrophoresis (CE) coupled with high-resolution mass spectrometry (HRMS) offers high selectivity and sensitivity for ionic compounds, thereby enhancing biomarker discovery. Recent advances in CE-MS include small, chip-based CE systems coupled with nanoelectrospray ionization (nanoESI) to provide rapid, high-resolution analysis of biological specimens. Here, we describe the development of a targeted microchip (µ) CE-HRMS method, with an acquisition time of only 3 min and sample injection volume of 4nL, to analyze 40 target metabolites in serum samples from a triple-mutant (TKO) mouse model of HGSC. Extracted ion electropherograms showed sharp, baseline resolved peak shapes, even for structural isomers such as leucine and isoleucine. All calibration curves of the analytes maintained good linearity with an average R2 of 0.994, while detection limits were in the nM range. Thirty metabolites were detected in mouse serum with recoveries ranging from 78 to 120%, indicating minimal ionization suppression and good accuracy. We applied the µCE-HRMS method to biweekly-collected serum samples from TKO and TKO control mice. A time-resolved analysis revealed characteristic temporal trends for amino acids, nucleosides, and amino acid derivatives. These metabolic alterations are indicative of altered nucleotide biosynthesis and amino acid metabolism in HGSC development and progression. A comparison of the µCE-HRMS dataset with non-targeted ultra-high performance liquid chromatography (UHPLC)-MS results showed identical temporal trends for the five metabolites detected with both platforms, indicating the µCE-HRMS method performed satisfactorily in terms of capturing metabolic reprogramming due to HGSC progression while reducing the total data collection time three-fold.