Back

Tumour-driven lipid accumulation in oenocytes reflects systemic lipid alterations

Cheng, L.; Liu, C.; Golenkina, S.

2025-07-03 developmental biology
10.1101/2025.07.01.662522 bioRxiv
Show abstract

Cancer cachexia is a multifactorial syndrome characterized by systemic metabolic dysfunction, including liver steatosis. In this study, we examined the role of larval oenocytes - hepatocyte-like cells, in a Drosophila model of cancer cachexia. We found that oenocytes in tumour-bearing larvae accumulate lipid droplets in response to tumour-secreted signals, Gbb and ImpL2. This lipid accumulation reflects systemic changes in lipid metabolism, responding to lipid metabolism manipulations in either the fat body or the muscle. Disrupting lipid synthesis (via FASN1 and DGAT1), storage (via Lsd2), or trafficking (via apolipoproteins) in these tissues significantly modulated lipid droplet accumulation in oenocytes. Moreover, oenocyte-specific knockdown of FASN1 reduced their lipid content and non-autonomously affected lipid droplet size in the fat body, suggesting cross-regulatory interactions between these tissues. Cachectic oenocytes also exhibited altered signaling profiles, characterized by reduced PI3K and elevated Wnt and Ecdysone activity. Enhancing PI3K signaling through Akt overexpression restored oenocyte size and reduced lipid levels; however, these changes did not significantly improve muscle integrity. Together, our data suggests that dynamic exchange of lipids occur between the fat body, oenocytes and the muscle during cancer cachexia. While the fat body and muscle lipid pools are key regulators of muscle integrity, oenocytes - despite their metabolic responsiveness, do not appear to play an active role in preserving muscle function during cachexia.

Published in PLOS Genetics (predicted rank #12) · training set

Matching journals

The top 4 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.