Back

Recurrent phases of strict protein limitation inhibit tumor growth and restore lifespan in a Drosophila intestinal cancer model

Pfefferkorn, R. M.; Mortzfeld, B. M.; Fink, C.; von Frieling, J.; Bossen, J.; Esser, D.; Kaleta, C. M.; Rosenstiel, P.; Heine, H.; Roeder, T.

2023-01-20 cancer biology
10.1101/2023.01.18.524563 bioRxiv
Show abstract

Diets that restrict caloric or protein intake offer a variety of benefits, including decreasing the incidence of cancer. However, whether such diets pose a substantial therapeutic benefit as auxiliary cancer treatments remains unclear. We determined the effects of severe protein depletion on tumorigenesis in a Drosophila melanogaster intestinal tumor model, using a human RAF gain-of-function allele. Severe and continuous protein restriction significantly reduced tumor growth but resulted in premature death. Therefore, we developed a diet in which short periods of severe protein restriction alternated cyclically with periods of complete feeding. This nutritional regime reduced tumor mass, restored gut functionality, and normalized the lifespan of oncogene-expressing flies to the levels observed in healthy control flies. Furthermore, this diet reduced the chemotherapy-induced stem cell activity associated with tumor recurrence. Transcriptome analysis revealed long-lasting changes in the expression of key genes involved in multiple major developmental signaling pathways. Overall, the data suggest that recurrent severe protein depletion effectively mimics the health benefits of continuous protein restriction, without undesired nutritional shortcomings. This provides seminal insights into the mechanisms of the transcriptomic memory effect required to maintain the positive effects of protein restriction throughout the phases of a full diet.

Matching journals

The top 8 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.