Proteomic analysis reveals USP7 as a novel regulator of palmitic acid-induced hepatocellular carcinoma cell death.
Sandhini SahaRohit VermaChandan KumarBhoj KumarAmit Kumar DeyMilan SurjitSivaram V S MylavarapuTushar Kanti MaitiPublished in: Cell death & disease (2022)
Nutrient surplus and consequent free fatty acid accumulation in the liver cause hepatosteatosis. The exposure of free fatty acids to cultured hepatocyte and hepatocellular carcinoma cell lines induces cellular stress, organelle adaptation, and subsequent cell death. Despite many studies, the mechanism associated with lipotoxicity and subsequent cell death still remains poorly understood. Here, we have used the proteomics approach to circumvent the mechanism for lipotoxicity using hepatocellular carcinoma cells as a model. Our quantitative proteomics data revealed that ectopic lipids accumulation in cells severely affects the ubiquitin-proteasomal system. The palmitic acid (PA) partially lowered the expression of deubiquitinating enzyme USP7 which subsequently destabilizes p53 and promotes mitotic entry of cells. Our global phosphoproteomics analysis also provides strong evidence of an altered cell cycle checkpoint proteins' expression that abrogates early G2/M checkpoints recovery with damaged DNA and induced mitotic catastrophe leading to hepatocyte death. We observe that palmitic acid prefers apoptosis-inducing factor (AIF) mediated cell death by depolarizing mitochondria and translocating AIF to the nucleus. In summary, the present study provides evidence of PA-induced hepatocellular death mediated by deubiquitinase USP7 downregulation and subsequent mitotic catastrophe.
Keyphrases
- cell death
- cell cycle arrest
- cell cycle
- fatty acid
- cell proliferation
- poor prognosis
- high glucose
- mass spectrometry
- induced apoptosis
- diabetic rats
- pi k akt
- drug induced
- liver injury
- endothelial cells
- binding protein
- oxidative stress
- single cell
- high resolution
- endoplasmic reticulum stress
- machine learning
- single molecule
- heat stress
- deep learning