Members of the tumor necrosis factor (TNF) transmembrane cytokine superfamily, such as TNFα and Fas ligand (FasL), play crucial roles in inflammation and immunity. TRAIL is a member of this superfamily with the ability to selectively trigger cancer cell death but does not motive cytotoxicity to most normal cells. Troglitazone are used in the cure of type II diabetes to reduce blood glucose levels and improve the sensitivity of an amount of tissues to insulin. In this study, we revealed that troglitazone could trigger TRAIL-mediated apoptotic cell death in human lung adenocarcinoma cells. Pretreatment of troglitazone induced activation of PPARγ in a dose-dependent manner. In addition conversion of LC3-I to LC3-II and PPARγ was suppressed in the presence of GW9662, a well-characterized PPARγ antagonist. Treatment with troglitazone resulted in a slight increase in conversion rate of LC3-I to LC3-II and significantly decreased p62 expression levels in a dose-dependent manner. This indicates that troglitazone induced autophagy flux activation in human lung cancer cells. Inhibition of autophagy flux applying a specific inhibitor and genetically modified ATG5 siRNA enclosed troglitazone-mediated enhancing effect of TRAIL. These data demonstrated that activation of PPARγ mediated by troglitazone enhances human lung cancer cells to TRAIL-induced apoptosis via autophagy flux and also suggest that troglitazone may be a combination therapeutic target with TRAIL protein in TRAIL-resistant cancer cells.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5432299 | PMC |
http://dx.doi.org/10.18632/oncotarget.15819 | DOI Listing |
Vet Res
January 2025
Veterinary Diagnostic Laboratory, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, USA.
Cranioventral pulmonary consolidation (CVPC) is a common lesion observed in the lungs of slaughtered pigs, often associated with Mycoplasma (M.) hyopneumoniae infection. There is a need to implement simple, fast, and valid CVPC scoring methods.
View Article and Find Full Text PDFJ Transl Med
January 2025
Department of Critical Care Medicine, Peking University Third Hospital, Beijing, 100191, China.
Background: Acute respiratory distress syndrome (ARDS) is a prevalent complication among critically ill patients, constituting around 10% of intensive care unit (ICU) admissions and mortality rates ranging from 35 to 46%. Hence, early recognition and prediction of ARDS are crucial for the timely administration of targeted treatment. However, ARDS is frequently underdiagnosed or delayed, and its heterogeneity diminishes the clinical utility of ARDS biomarkers.
View Article and Find Full Text PDFBackground: Systemic sclerosis (SSc) is a rare connective tissue disease, frequently affecting the skin, lungs, and pulmonary vasculature. Approximately 30-50% of SSc patients develop interstitial lung disease (SSc-ILD), with 30-35% of related deaths attributed to it. Even though men are less likely to develop systemic sclerosis, they have a higher incidence of SSc-ILD than women, and they tend to develop it at a younger age with a higher mortality rate.
View Article and Find Full Text PDFBMC Infect Dis
January 2025
Department of Respiratory Medicine, Children's Hospital of Soochow University, Jingde Road No. 303, Suzhou, 215003, China.
Background: The aim of this study was to investigate the clinical characteristics of severe pneumonia caused by human bocavirus (HBoV) infection to explore the associated risk factors.
Methods: We conducted a retrospective review of data from children hospitalized with HBoV pneumonia. Based on the severity of pneumonia, patients were categorized into severe pneumonia and non-severe pneumonia groups.
BMC Bioinformatics
January 2025
Beijing Institute of Heart Lung and Blood Vessel Diseases, Beijing Anzhen Hospital of Capital Medical University, Beijing, 101100, China.
Background: MicroRNAs (miRNAs) are pivotal in the initiation and progression of complex human diseases and have been identified as targets for small molecule (SM) drugs. However, the expensive and time-intensive characteristics of conventional experimental techniques for identifying SM-miRNA associations highlight the necessity for efficient computational methodologies in this field.
Results: In this study, we proposed a deep learning method called Multi-source Data Fusion and Graph Neural Networks for Small Molecule-MiRNA Association (MDFGNN-SMMA) to predict potential SM-miRNA associations.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!