Abstract 4329: Lipid droplets and associated proteins in hepatocellular carcinoma tumour cells
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».