The acquisition of resistance to TNFalpha in breast cancer cells is associated with constitutive activation of autophagy as revealed by a transcriptome analysis using a custom microarray.
- Tumor Stroma Interactions
- Tumor Immunotherapy and Microenvironment
- Platform LUXGEN - Micro-Array
While the autophagic process is mainly regulated at the post-translational level, a growing body of evidence suggests that autophagy might also be regulated at the transcriptional level. The identification of transcription factors involved in the regulation of autophagy genes has provided compelling evidence for such regulation. In this context, a powerful high throughput analysis tool to simultaneously monitor the expression level of autophagy genes is urgently needed. Here we describe setting up the first comprehensive human autophagy database (HADb, available at www.autophagy.lu) and the development of a companion Human Autophagy-dedicated cDNA Microarray which comprises 234 genes involved in or related to autophagy. The autophagy microarray tool used on breast adenocarcinoma MCF-7 cell line allowed the identification of 47 differentially expressed autophagy genes associated with the acquisition of resistance to the cytotoxic effect of TNFalpha. The autophagy-core machinery genes DRAM (Damage-Regulated Autophagy Modulator), BNIP3L (BCL2/adenovirus E1B 19 kDa interacting protein 3-like), BECN1 (Beclin 1), GABARAP (Gamma-AminoButyric Acid Receptor-Associated Protein) and UVRAG (UV radiation resistance associated gene) were found upregulated in TNF-resistant cells, suggesting a constitutive activation of the autophagy machinery in these cells. More interestingly, we identified NPC1 as the most upregulated genes in TNF-resistant compared to TNF-sensitive MCF-7 cells, suggesting a relation between the intracellular transport of cholesterol, the regulation of autophagy and NPC1 expression in TNF-resistant tumor cells. In conclusion, we describe here new tools that may help investigating autophagy gene regulation in various cellular models and diseases.