Back

A Weight Loss Fibrotic Response in Visceral Fat Is Driven by Defective Lysosome-Dependent Collagenolysis

Rebiere, C.; Oliveira, A.; Dussaud, S.; Haddad, I.; de Assis Ferreira, A.; Babetto, A.; Rouault, C.; Merabtene, F.; Benbida, C.; Boniface Abatan, J.; Vargas da Silva, S.; Bhering Martins, L.; Torres, L.; Malheiros Silveira, A. L.; Rose, C.; Genser, L.; Andreelli, F.; Dugail, I.; Chaves de Oliveira, M.; Vinh, J.; Lecoutre, S.; Versiani Matos Ferreira, A.; Clement, K.; Gautier, E. L.; Marcelin, G.

2026-10-05 pathology
10.64898/2026.09.29.755405 bioRxiv
Show abstract

Obesity induces pathological remodeling of white adipose tissue (WAT), including chronic inflammation and fibrosis, which drive metabolic dysfunction. Although weight loss (WL) is the cornerstone of obesity treatment and improves systemic metabolic health, its impact on WAT remodeling remains incompletely understood. Using a mouse model of diet-induced obesity followed by nutritional weight loss, we show that visceral WAT (vWAT) fails to fully recover despite normalization of body weight and marked metabolic improvment. Notably, vWAT fibrosis persists and continues to progress after WL. This occurs while extracellular matrix (ECM) production by adipose progenitor cells is markedly reduced following WL, explaining why pharmacological inhibition of ECM synthesis fails to prevent fibrosis during WL. Instead, we observed impaired collagen degradation by both progenitor cells and macrophages, along with lysosomal dysfunction, during WL. This suggests that defective ECM degradation, rather than continued ECM production, underlies fibrosis persistence and aggravation during WL. Together, these findings identify impaired collagenolysis as a critical barrier to adipose tissue recovery after WL and suggest that enhancing ECM clearance, rather than inhibiting ECM synthesis, may represent a more effective therapeutic strategy to restore adipose tissue function.

Matching journals

The top 6 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.