Back

Small non-coding RNA profiling reveals inflammatory and mitochondrial related changes in aging β-cells and islet macrophages

Guay, C.; Perrard, J.; Mangano, E.; Menoud, V.; Aiello, E.; Fignani, D.; Dotta, F.; Bonner-Weir, S.; Sebastiani, G.; Aguayo-Mazzucato, C.; Cosentino, C.; Regazzi, R.

2025-12-18 molecular biology
10.64898/2025.12.17.694889 bioRxiv
Show abstract

Aging is accompanied by functional decline and increased senescence of pancreatic {beta}-cells. These changes may be influenced by islet-resident macrophages (iMACs) that remodel tissue in response to environmental cues. To explore the molecular mechanisms underlying {beta}-cell aging and senescence, we profiled small non-coding RNAs (sncRNAs) in FACS-sorted {beta}-cells and iMACs from 3-, 12-, and 22-month-old mouse islets or from senescence-associated {beta}gal (SA-{beta}gal) positive {beta}-cells of 8-month-old mice. Overall, senescent {beta}-cells displayed distinct sncRNA signatures that only partially overlapped with those of aging. However, several miRNAs previously found to be deregulated in obese or diabetic conditions were modulated in both aging and senescent {beta}-cells, including upregulation of miRNAs linked to inflammation. In vitro exposure to pro-inflammatory cytokines partially reproduced these profiles. Aging also reshaped the {beta}-cell tRNA-derived fragment (tRF) pool, enhancing global mitochondrial tRF levels. Interestingly, some changes in miRNAs and tRFs were {beta}-cell specific, whereas others occurred also in other aged metabolic tissues. iMACs also showed age-related sncRNA remodeling, including upregulation of anti-inflammatory miRNAs and mitochondrial tRFs, suggesting adaptive immune reprogramming. Together, these data reveal a profound, coordinated reshaping of the sncRNA landscape in {beta}-cells and iMACs during aging, offering new insights into molecular mechanisms driving age-related islet dysfunction.

Matching journals

The top 2 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.