Back

Time-resolved transcriptomic profiling of senescence-associated secretory phenotype (SASP) in multiple senescent cell subtypes

Razali, N. B.; Moriyama, Y.; Chiu, Y.; Suda, K.; Kono, K.

2022-06-30 bioinformatics
10.1101/2022.06.27.497690 bioRxiv
Show abstract

Cellular senescence, irreversible cell cycle arrest, is induced by various triggers including telomere shortening, oncogene activation, and DNA damage. Senescent cells exhibit the senescence-associated secretory phenotype (SASP), a pathological feature that contributes to organismal aging. We previously showed that transient plasma membrane damage (PMD) induces a novel subtype of cellular senescence (PMDS) accompanied by SASP, but the overall expression profiles of SASP during PMDS induction was unknown. Here, using mRNA-seq, qPCR, and bioinformatics, we revealed the time-resolved SASP transcriptomic profile in PMDS in comparison with calcium influx-induced senescence, DNA damage response-induced senescence, and replicative senescence. Although the expression of SASP factors was postulated to increase steadily during senescence, we counterintuitively found that the variety of SASP peaks in early PMDS. The pathway comparison analyses and Ingenuity Pathway Analysis suggest that, in early PMDS, wound-healing SASP factors, namely Il-6, Mmp1, and Mmp3, inhibit the GPVI collagen signaling pathway, which in turn further upregulates the same SASP factors, forming a feedback loop. At late senescence, common SASP factors including Il-6 and Ccl2 are upregulated in all senescent cell subtypes. Thus, SASP is diverse at early senescence and becomes relatively uniform at late senescence. Diverse SASP may contribute to senescent cell subtype-specific paracrine/autocrine functions in vivo.

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.