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Interfering with lipid metabolism through targeting CES1 sensitizes hepatocellular carcinoma for chemotherapy
Gang Li, … , Richard Lehner, Kai Sun
Gang Li, … , Richard Lehner, Kai Sun
Published December 6, 2022
Citation Information: JCI Insight. 2023;8(2):e163624. https://doi.org/10.1172/jci.insight.163624.
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Research Article Metabolism

Interfering with lipid metabolism through targeting CES1 sensitizes hepatocellular carcinoma for chemotherapy

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Abstract

Hepatocellular carcinoma (HCC) is the most common lethal form of liver cancer. Apart from surgical removal and transplantation, other treatments have not yet been well established for patients with HCC. In this study, we found that carboxylesterase 1 (CES1) is expressed at various levels in HCC. We further revealed that blockage of CES1 by pharmacological and genetical approaches leads to altered lipid profiles that are directly linked to impaired mitochondrial function. Mechanistically, lipidomic analyses indicated that lipid signaling molecules, including polyunsaturated fatty acids (PUFAs), which activate PPARα/γ, were dramatically reduced upon CES1 inhibition. As a result, the expression of SCD, a PPARα/γ target gene involved in tumor progression and chemoresistance, was significantly downregulated. Clinical analysis demonstrated a strong correlation between the protein levels of CES1 and SCD in HCC. Interference with lipid signaling by targeting the CES1-PPARα/γ-SCD axis sensitized HCC cells to cisplatin treatment. As a result, the growth of HCC xenograft tumors in NU/J mice was potently slowed by coadministration of cisplatin and CES1 inhibition. Our results, thus, suggest that CES1 is a promising therapeutic target for HCC treatment.

Authors

Gang Li, Xin Li, Iqbal Mahmud, Jazmin Ysaguirre, Baharan Fekry, Shuyue Wang, Bo Wei, Kristin L. Eckel-Mahan, Philip L. Lorenzi, Richard Lehner, Kai Sun

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Figure 3

Blockage of CES1 impairs mitochondrial function.

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Blockage of CES1 impairs mitochondrial function.
(A) Dot plot showing th...
(A) Dot plot showing the enrichment for lipid ontology based on the lipidomic analysis. (B) Box plot showing the level of acylcarnitine (16:0) in WT, KO, and WWL229-treated HepG2 cells (n = 3). (C) Analysis of fatty acid oxidation in HepG2 cells treated with or without 50 μM WWL229 for 24 hours (n = 3).(D) Analysis of fatty acid oxidation in WT and KO HepG2 cells (n = 3). (E) Oxygen consumption rate (OCR) of HepG2 cells treated with or without 10 μM WWL229 analyzed by the Seahorse xFe24 instrument (n = 5). (F) Bioenergetic parameters inferred from OCR traces in E (n = 5). (G) OCR of WT and KO HepG2 cells analyzed using the Seahorse xFe24 instrument (n = 5). (H) Bioenergetic parameters inferred from OCR traces in G (n = 5). (I) qPCR analysis of β-oxidation–related genes in HepG2 cells treated with or without 50 μM WWL229 for 48 hours (n = 6). (J) qPCR analysis of mitochondrial biogenesis–related genes in HepG2 cells treated with or without 50 μM WWL229 for 48 hours (n = 6). (K) qPCR analysis of β-oxidation–related genes in WT, KO, and KO with reexpression of CES1 (KO + CES1) HepG2 cells (n = 3). (L) qPCR analysis of mitochondrial biogenesis–related genes in the WT, KO, and KO with reexpression of Ces1d (KO + CES1) HepG2 cells (n = 3). Each point represents a biological replicate. Data are represented as mean ± SD. Student’s t test for B, C, D, F, and H. One-way ANOVA followed by Dunnett T3 test for K and J. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 versus WT or Control. #P < 0.05, ###P < 0.001 versus KO.

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