Using the CellAge Database to Find Genes Associated with Inhibition of Cellular Senescence
The CellAge database was announced last year, a repository of information on genes linked to cellular senescence. Cells become senescent in response to a variety of stresses, or upon reaching the Hayflick limit. A senescent cell ceases replication and secretes inflammatory and pro-growth signals. The process serves a useful function when such cells are present for a short time and then destroyed, aiding in suppression of cancer and in wound healing. When senescent cells linger, they cause chronic inflammation and significant disruption to tissue function, however. This is one of the contributing causes of aging, and selective removal of these cells via senolytic therapies will likely be the first form of rejuvenation therapy to see widespread use. Meanwhile, some research groups are instead looking for ways to inhibit entry into the senescent state, a task that starts by identifying relevant mechanisms that might be points of intervention.
Research has sought to ascertain the genetic program and prodrome underlying the development and phenotype of senescent cells. Expedited by recent advances in genomic and transcriptomic sequencing, alongside high-throughput genetic screens, a wealth of publicly available data now exists which has furthered the understanding of senescence regulation. Unfortunately, despite our increasing knowledge of cellular senescence (CS), determining whether a cell has senesced is not clear-cut.
Common senescence markers used to identify CS in vitro and in vivo include senescence-associated β-galactosidase (SA-β-gal) and p16INK4A (p16). However, β-galactosidase activity has been detected in other cell types such as macrophages, osteoclasts, and cells undergoing autophagy. Furthermore, some forms of senescence are not associated with p16 expression, while p16 has been detected in non-senescent cells. As such, there are now over 200 genes implicated in CS in humans alone.
Biological networks can be built using protein interaction and gene co-expression data. Here, we present the network of proteins and genes co-expressed with the CellAge senescence genes. Assaying the networks, we find links between senescence and immune system functions and find genes highly connected to CellAge genes under the assumption that a guilt-by-association approach will reveal genes with similar functions. In this study, we look at the broad context of CS genes - their association with aging and aging-related diseases, functional enrichment, evolutionary conservation, and topological parameters within biological networks - to further our understanding of the impact of CS in aging and diseases. Using our networks, we generate a list of potential novel CS regulators and experimentally validate 26 genes using siRNAs, identifying 13 new senescence inhibitors.