A multidimensional systems biology analysis of cellular senescence in aging and disease
Avelar RA, Ortega JG, Tacutu R, Tyler EJ, Bennett D, Binetti P, Budovsky A, Chatsirisupachai K, Johnson E, Murray A, Shields S, Tejada-Martinez D, Thornton D, Fraifeld VE, Bishop CL, de Magalhães JP. A multidimensional systems biology analysis of cellular senescence in aging and disease. Genome Biol. 2020 Apr 7;21(1):91. doi: 10.1186/s13059-020-01990-9. PMID: 32264951; PMCID: PMC7333371.
Introduction
Hayflick and Moorhead demonstrated replicative senescence in fibroblasts that reached a stable proliferative growth arrest. The cells remained metabolically active and had a distinct vacuolated morphology.
Replicative senescence is driven by telomere attrition that exposes the chromosome end and triggers the DNA damage response. Other types of senescence occur with exposure to stressors like oncogenic factors, oxidative stress or radiation. It is a pleiotropic phenomenon vital to embryogenesis, tumour suppression, and wound healing.
The persistence of senescence cells without clearance by the immune system can lead to a chronic state of senescence and inflammation through the senescence-associated secretory phenotype - a state that can spread senescence to neighbouring healthy cells.
It can be hard to determine what cellular senescence is since there is no one marker to identify it. β-galactosidase activity and p16 expression are usually used to identify senescent cells, but these markers are not unique or always found in senescent cells. The CellAge database uses existing data on human ageing to provide data on senescence.
Results
CellAge has 279 senescence genes in different senescence pathways. They ran a meta-analysis to get a molecular signature of replicative senescence and found 526 overexpressed and 734 underexpressed genes. 44 of the 279 CellAge genes were found in cellular senescence signatures, a significant overlap.
CellAge gene functions
Functional enrichment analyses showed that CellAge genes are enriched with several clusters associated with Protein Kinase Activity, transcription regulation DNA-binding, DNA damage repair, and cell cycle regulation in cancer. Genes inducing senescence were more associated with promoting transcription and genes inhibiting senescence were associated with transcription repression.
Clustering related processes resulted in 298 categories that were significantly enriched.
Evolutionary conservation of CellAge genes in model organisms
There were significantly more orthologues for human CellAge genes in mice, rats, and monkeys than for other genes. Senescence inducers were more conserved than inhibitors.
22 orthogroups were conserved in the 24 mammals studied.
CellAge vs human orthologues of longevity-associated model organism genes
Senescence inducers overlapped with anti-longevity genes and not with pro-longevity genes and vice-versa with senescence inhibitors. They did however find an overrepresentation of senescence inhibitors in the anti-longevity genes set. There is a significant association between cellular senescence and the ageing process and there may be an inverse relationship between senescence and longevity for some pathways.
CellAge genes differentially expressed with age
Genes overexpressed with age overlapped with the CellAge genes and genes underexpressed with age did not show any significant overlap. Overexpressed signatures of ageing overlapped with overexpressed signatures of replicative senescence, but not underexpressed signatures of ageing.
Underexpressed signatures of replicative senescence did not overlap with overexpressed or underexpressed ageing signatures.
91 inducers and 72 inhibitors overlapped with ageing signatures in fibroblasts.
Functional enrichment analysis showed that 71 GO terms were enriched for the overlap between CellAge senescence inducers and age upregulated genes.
Tissue specific senescence gene expression and differential expression of
They asked if CellAge genes and differentially expressed signatures of senescence were tissue-specific. CellAge genes were generally expressed across tissue types. Only 10% of CellAge genes and 11% of senescence signatures had tissue-specific expression.
Overexpressed senescence signatures were across tissue types with age. 64% of protein-coding genes did not significantly change expression with age in any tissues. The number of CellAge senescence inducers overexpressed with age was higher than the genome average.
Overexpressed signatures of senescence were significantly differentially expressed with age compared to all protein-coding genes. The number of underexpressed signatures of senescence was underexpressed more than expected by chance.
The expression of most of the senescence genes did not change with age, but a significant number tend to be differentially expressed with age across multiple tissues in humans.
Are CS genes associated with cancer genes?
There was a significant overlap between CellAge genes and cancer, with both oncogenes and tumour suppression genes.
Senescence inducers tended to be tumour suppressors, while inhibitors were oncogenes which is consistent with the view that senescence is a tumour-suppressing mechanism.
Network analysis
RNA-Seq Unweighted Co-expression network:
There were a number of clusters found and one was related to immunity.