Abstract 4329: Lipid droplets and associated proteins in hepatocellular carcinoma tumour cells
Bibliographic record
Abstract
Abstract Introduction: Fatty acids are a major source of energy but can be toxic. Cells avoid cytotoxicity by esterifying fatty acids into neutral lipids and packaging them into lipid droplets (LDs). The perilipins (PLINs) are a family of the 5 most abundant LD-associated proteins coating and regulating the LDs1. Numerous studies have shown links between deregulated lipid metabolism and a wide variety of diseases, including diabetes, obesity, and cancer2. Hepatocellular carcinoma (HCC) arising from a background of fatty liver disease accounts for 15%-50% of HCC cases in the United States3. As it has been proposed that an imbalance of lipid composition may disrupt cellular functions and lead to the onset of cancer4, and as it was found that deregulated cancer pathways such as the mTOR pathway lead to increased lipogenesis5, it is crucial to consider the impact of the expression levels of specific PLINs in the development and progression of HCC. We hypothesize that understanding the PLIN expression and lipid profile of the tumor and the target organ will allow us to further understand the role of lipid metabolism in HCC. To address this we are manipulating PLIN expression levels in an in vitro system, and using human samples obtained through the Liver Disease Biobank (Dr. Metrakos: IRB approved protocol) to evaluate PLIN function ex vivo. Preliminary Results: Knock-down of PLINs - Lentiviral constructs containing shRNA sequences for RNA-interference of PLINs 2, 3, and 5 (which were found to be most highly expressed in HCC6) were generated. Knock-Down (KD) lines of PLINs 2 and 3 were successfully created in HepG2. The PLIN 5 KD line has proved difficult to identify due to antibody specificity. Understanding the role of PLINs - Assessment of cell proliferation in PLIN 2 and 3 KD lines has shown a decrease in growth compared to the Scrambled shRNA control, with no significant difference in proliferation between PLIN 2 and 3 KD lines. When challenged with Oleic Acid, proliferation is further reduced compared to the control line, and PLIN 3 KD cell growth is comparably less than that of PLIN 2 KD cells. Next steps: Having identified a correlation between PLIN 2 and 3 expression levels and cancer cell proliferation, we are now using the Fluorescent Activated Cell Sorting system to further characterize these cell lines. We are also analyzing human blood and tissue (cancerous and normal) to broaden our understanding of the impact of the PLINs in cancer development and progression. These studies may allow for potential targeting of PLINs in cancer therapy, and as biomarkers for liver disease. References: (1)Brasaemle, L.(2007) J Lipid Res.48(12):2547-59 (2)Baffy, G. et al.(2012) J. of Hepat.56:1384-91 (3)Sanyal, A. et al.(2010) Curr. Med. Research & Opinion.26(9):2183-2191 (4)Fernandis, A. et al.(2009) J of Chromat.877(26):2830-35 (5)Chakrabarti, P. et al.(2010) Diabetes.59(4):775-781 (6)Straub, B. et al.(2010) Modern. Pathol.23:480-92 Citation Format: Evette Yassa, Hussam Alamri, Tommy Alain, Anthoula Lazaris, Peter Ghali, Nahum Sonenberg, Peter Metrakos. Lipid droplets and associated proteins in hepatocellular carcinoma tumour cells. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4329. doi:10.1158/1538-7445.AM2014-4329
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".