New benchmark for senescence developed from CellAge database

Comprehensive cellular senescence database will provide benchmark for the development of senolytic therapies.

Our cells are intrinsically programmed for senescence – a gradual aging and diminishing of ability – and replicative telomere erosion provides the countdown.

Longevity.Technology: A hallmark of aging, cellular senescence has been linked to aging-related diseases. Finding out what causes it at a genetic level has been a key driver for the Longevity research community. A manually-curated database of 279 human genes that drive cellular senescence, CellAge, could provide the answers to some vital questions about the origins of aging by shedding light on its pathways.

Human Ageing Genomic Resources (HAGR) is a collection of databases and tools designed to help researchers study the genetics of human aging. It uses functional genomics, network analyses, systems biology and evolutionary analyses to provide data on various aspects of aging.

One of its databases – CellAge – contains the details of 279 human genes associated with cell senescence. Senescent cells secrete a cocktail of inflammatory chemicals, called senescence-associated secretory phenotype, or SASP. These chemicals adversely affect neighbouring cells, the surrounding extracellular matrix and other structural components, causing a cascade of negative effects which can include chronic inflammation and causing senescence in healthy cells.

A team including João Pedro de Magalhães of the Institute of Ageing and Chronic Disease at the University of Liverpool, used the CellAge database to effect a systems biology analysis of cellular senescence and gain a better understanding of its pathways.

“In order to tackle the complexity of biological processes we need to study genes as integrated networks. Our work and database allows us to study the human senescence network and identify candidate regulators of the network, which we can then test experimentally, and discover new candidate targets for pharmacological interventions that may potentially be used to target aging,” de Magalhães told us earlier today.

Genes that drive cellular senescence tend to be over-expressed with age in human tissue, so the team built cellular senescence protein-protein interaction and co-expression networks, enriched clusters in the networks for cell cycle and immunological processes and used network topological parameters to reveal novel potential cellular senescence regulators [1].

“CellAge database is the first comprehensive cellular senescence database…”

The team conclude that their “CellAge database is the first comprehensive cellular senescence database, which will be a major resource for researchers to understand the role of senescence in aging and disease … Using network biology, we implicated the CellAge genes in various processes, particularly cell division and immune system processes. We used network topology to identify potential regulators of CS and bottlenecks that could impact various downstream processes if deregulated.

“Indeed, we identified 11 genes that have already been shown to contribute towards CS, which will be added to future versions of CellAge. Finally, we experimentally verified 26 genes that induce CS morphology or biomarkers when knocked down in human mammary fibroblasts. Of these, 13 genes (C9orf40, CDC25A, CDCA4, CKAP2, GTF3C4, HAUS4, IMMT, MCM7, MTHFD2, MYBL2, NEK2, NIPA2, and TCEB3) were strong hits in inducing a senescent phenotype.”

Senolytic drugs are are an area of keen pharmacological research; this new benchmark could result in progress towards new human clinical applications. Diseases of aging can only be properly mitigated if the pathways of cellular senescence are properly deconstructed and understood and CellAge would appear to be an important tool in acquiring this understanding.

Members of the Gerontology Research Group discussion forum posited that a next step might involve considering the work of Douglas Lauffenburger at MIT who reduces signalling networks to their essentials by using Boolean logic and Fuzzy logic [2].

Image courtesy of Institute of Ageing and Chronic Disease