Biology subjects

Salamini-Montemurri, M.

Publications and source records attributed to Salamini-Montemurri, M..

2 recordsLinked to original sources

bioRxiv2026

Reduced LANCL1-AS1 in old human skeletal muscle diminishes mitochondrial activity, shortens mt-mRNA poly(A) tails, and suppresses myogenesis

Regeneration of skeletal muscle preserves muscle mass and function, which decline with age. Here, we sought to identify long noncoding (lnc)RNAs involved in skeletal muscle myogenesis and potentially relevant to muscle aging. Cross-sectional analysis of skeletal muscle transcriptomes from healthy 22-through 89-year-old individuals revealed lncRNA LANCL1-AS1 among the top declining transcripts. Conversely, LANCL1-AS1 increased robustly during skeletal myogenesis and promoted myogenic differentiation in culture. Affinity pulldown by ChIRP followed by mass spectrometry revealed that LANCL1-AS1 associated with the mitochondrial protein LRPPRC, enhancing the formation of the chaperone complex LRPPRC-SLIRP, which maintains longer poly(A) tails of mitochondrial (mt-)mRNAs and stabilizes mt-mRNAs. Importantly, while myoblasts from old rhesus monkey muscle expressed lower levels of LANCL1-AS1 and mt-mRNAs, and displayed lower mitochondrial activity than young monkey myoblasts, overexpressing LANCL1-AS1 in old myoblasts restored mitochondrial activity and myogenesis. We propose that the age-associated reduction in LANCL1-AS1 contributes to impaired mitochondrial function and reduced myogenic capacity in aging skeletal muscle.

molecular biology↗

bioRxiv2026

SenCat: Cataloging human cell senescence through multiomic profiling of multiple senescent primary cell types

There is an urgent need to comprehensively catalog senescence markers across cell types in an organism in order to characterize senotypes and senescent cell heterogeneity. Here, we profiled the transcriptomes and proteomes in 14 different primary human cell types undergoing over 30 senescence paradigms to create a senescence catalog we termed SenCat. We found that, while senescent cells from all primary tissue types did not share a single unique marker, they did activate shared specific metabolic and damage-response pathways implicated in tissue repair. Machine learning analysis of the SenCat transcriptomic and proteomic datasets successfully identified independent sets of senescent human cells, and senescent-like cells in mouse lung and kidney. In sum, SenCat represents a much-needed resource to identify senescent cells across tissues in the body. HIGHLIGHTSO_LIIdentifying senescent cells in organisms in vivo remains a challenge C_LIO_LIWe created SenCat: a catalog transcriptomes and proteomes of senescent primary cells C_LIO_LIMachine learning (ML) analysis of SenCat identified robust senescence scores C_LIO_LIML-derived senescence scores uncovered senescent-like cell dynamics in vivo C_LI

molecular biology↗