Biology subjects

Aguayo-Mazzucato, C.

Publications and source records attributed to Aguayo-Mazzucato, C..

3 recordsLinked to original sources

bioRxiv2025

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

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.

molecular biology↗

bioRxiv2025

A human and mouse subpopulation of senescent β-cells induces pathologic dysfunction through targetable paracrine signaling

Cellular senescence is a stress response mechanism marked by irreversible growth arrest, upregulation of antiapoptotic pathways, loss of cellular function, and remodelling of the cellular secretory profile. In both humans and mice, pancreatic {beta}-cells undergo senescence with age and insulin resistance. Targeted removal of senescent cells in mouse models of diabetes improves glucose homeostasis, demonstrating the role {beta}-cell senescence in diabetes progression. In contrast, {beta}-cell senescence also promotes immune surveillance, promoting {beta}-cell survival and function. Thus, a better understanding of senescent cells phenotypic and functional heterogeneity is needed to develop effective therapeutic strategies. Herein, we show that subpopulations of senescent {beta}-cells in mice and humans, which were identified through the expression of Cdkn1a (encoding p21Cip1) and Cdkn2a (encoding p16Ink4a) by single-cell RNA sequencing (scRNA-seq), flow cytometry, spatial transcriptomics, and spatial proteomics, exhibit distinct transcriptional and functional identities. The predominant senescent {beta}-cell subpopulation expressed Cdkn1a and was characterized by a lack of glucose responsiveness, high basal insulin secretion, and transcription of canonical SASP factors. The SASP of Cdkn1a-expressing {beta}-cells had non-cell autonomous effects on neighbouring cells. A subset of four SASP factors from Cdkn1a+ cells was sufficient to induce secondary senescence and {beta}-cell dysfunction in vitro. JAK inhibitors (JAK1/2 and JAK1/3) counteracted secondary senescence induction and restored {beta}-cell function in high-fat diet-fed mice and human islets from donors with or without type 2 diabetes. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=196 SRC="FIGDIR/small/648438v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@9b8addorg.highwire.dtl.DTLVardef@1b9c4eborg.highwire.dtl.DTLVardef@12f2964org.highwire.dtl.DTLVardef@1468297_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2022

DNA methylation supports accelerated biological age in Type 2 Diabetes which can be reversed with pharmacological treatments: Retrospective Cohort Study

BackgroundBiological age (BA) closely depicts age-related changes at a cellular level. Type 2 Diabetes mellitus (T2D) accelerates BA when calculated using clinical biomarkers. However, there is a large spread of individual BA within these groups and it is unclear what clinical biomarkers correlate with different speeds of aging and whether pharmacological treatment of diabetes alter BA. We hypothesized that accelerated BA would be seen at the DNA methylation (DNAm) level, the gold standard to determine BA, and biomarkers and treatments would correlate the rate of BA in T2D. MethodsPublicly available DNAm samples were obtained from the GEO NCBI database and the NHANES 2017-2018 and ACCORD Cohorts were used for our analysis. We used the DNA Methylation Phenotypic Age algorithm and the Klemera and Doubal (KDM) methods to calculate BA with DNA methylation and clinical biomarkers, respectively. ResultsDNAm showed increased BA in whole blood and pancreatic islets in T2D in aging-related pathways, such as DNA damage and inflammation. Using the NHANES and ACCORD Trial cohorts, we found that avoidance of fried and fatty foods, and vigorous activity correlated with decreased BA in T2D. Cardiovascular, glycemic, and inflammatory biomarkers associated with the rate of aging in DM. Intensive blood pressure and T2D treatment associated with a greater deceleration in the speed of aging as compared to the standard. ConclusionsOverall, we show that certain tissues age faster in people with T2D and this strongly associates with blood glucose control, inflammation and cardiovascular health. Effective treatment of the disease can decelerate aging and decrease BA suggesting the latter as a novel and integrated index to evaluate and follow people with T2D. FundingThis study was supported by Institutional Startup Funds to C.A.M. (Joslin Diabetes Center) and NIH grants P30 DK036836 Joslin Diabetes Research Center (Bioinformatic Core).

physiology↗