Back

MitoNEET reduces the mitochondrial oxidative phosphorylation during epithelial-mesenchymal transition

Handa, H.; Onodera, Y.; Oikawa, T.; Takada, S.; Ueda, K.; Setoyama, D.; Yokota, T.; Yamasaki, M.; Watanabe, M.; Fumoto, Y.; Hashimoto, A.; Hata, S.; Murakami, M.; Sabe, H.

2024-07-29 cell biology
10.1101/2024.07.29.603210 bioRxiv
Show abstract

Mitochondrial functions range from catabolic to anabolic, which are tightly coordinated to meet cellular demands for proliferation and motility. MitoNEET is a mitochondrial outer membrane protein with a CDGSH domain and is involved in mitochondrial function. Epithelial-to-mesenchymal transition (EMT) is the process in which cells lose their epithelial characteristics and acquire mesenchymal traits, such as motility, which is a vital step for organism development and wound-healing. Cellular motility is associated with high ATP consumption owing to lamellipodia formation, which is supported by upregulated oxidative phosphorylation (OXPHOS) capacity. However, how mitoNEET is involved in the regulation of OXPHOS capacity and subsequent cellular motility remains unclear. Here we show that loss of mitoNEET regulation during EMT impairs both OXPHOS enhancement and cell motility in non-transformed NMuMG mouse mammary gland epithelial cells. We found that mitoNEET is downregulated during EMT, and that the aberrant expression of mitoNEET abolishes the upregulation of OXPHOS, leading to the inhibition of cell motility. Furthermore, we found that mitoNEET topology may be crucial for the regulation of the mitochondrial electron transfer chain, suggesting an additional regulatory pathway for OXPHOS capacity. Our results demonstrate that mitochondrial OXPHOS capacity during EMT is partly regulated by the dynamics of the outer membrane protein. We believe that our findings are the first step towards understanding the mechanisms by which mitochondrial outer membrane protein topology affects organelle functions.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.