Mitochondrial transcription factor A (TFAM) shapes metabolic and invasion gene signatures in melanoma.
L F AraujoA D D SienaJ R PlaçaD B BrottoI I BarrosB R MuysC A O BiagiK C PeronniJ F SousaG A MolfettaL C WestA P WestAndréia Machado LeopoldinoE M EspreaficoWilson Araújo da Silva JuniorPublished in: Scientific reports (2018)
Mitochondria are central key players in cell metabolism, and mitochondrial DNA (mtDNA) instability has been linked to metabolic changes that contribute to tumorigenesis and to increased expression of pro-tumorigenic genes. Here, we use melanoma cell lines and metastatic melanoma tumors to evaluate the effect of mtDNA alterations and the expression of the mtDNA packaging factor, TFAM, on energetic metabolism and pro-tumorigenic nuclear gene expression changes. We report a positive correlation between mtDNA copy number, glucose consumption, and ATP production in melanoma cell lines. Gene expression analysis reveals a down-regulation of glycolytic enzymes in cell lines and an up-regulation of amino acid metabolism enzymes in melanoma tumors, suggesting that TFAM may shift melanoma fuel utilization from glycolysis towards amino acid metabolism, especially glutamine. Indeed, proliferation assays reveal that TFAM-down melanoma cell lines display a growth arrest in glutamine-free media, emphasizing that these cells rely more on glutamine metabolism than glycolysis. Finally, our data indicate that TFAM correlates to VEGF expression and may contribute to tumorigenesis by triggering a more invasive gene expression signature. Our findings contribute to the understanding of how TFAM affects melanoma cell metabolism, and they provide new insight into the mechanisms by which TFAM and mtDNA copy number influence melanoma tumorigenesis.
Keyphrases
- copy number
- mitochondrial dna
- genome wide
- gene expression
- dna methylation
- skin cancer
- poor prognosis
- amino acid
- transcription factor
- single cell
- cell therapy
- cell cycle arrest
- machine learning
- high throughput
- electronic health record
- pi k akt
- insulin resistance
- cell cycle
- type diabetes
- skeletal muscle
- mesenchymal stem cells
- anti inflammatory
- big data
- dna binding
- endoplasmic reticulum stress
- artificial intelligence