- Finding Genetic Signatures of Aging
A new study uses a mouse cell atlas to identify both general and tissue-specific genetic aging signatures
In the quest to fight age-related decline, there are many avenues we are exploring: drugs (such as metformin or rapamycin), supplements (such as resveratrol), blood exchange and/or dilution, custom-made molecules, stem cells,…. Or why not try some fasting or calorie restriction (which may not work as well as you think)? Or have some red wine.
But to truly get a grip on treating age-related decline in various bodily functions, we should understand the various complex processes that underlie aging.
We can study metabolism, physiology, cognitive function… We can also drill down into the genetic roots of aging (environment and lifestyle matter too, of course). To untangle those genetic roots, though, we need to know which genes of the many thousands to focus on.
This is a tricky problem because aging is a systemic process that leaves no bodily function unaffected. It is a multi-factorial problem and in a previous post, we looked at how machine learning can be beneficial in aging research, for example to develop lifespan ‘clocks’. But aging is not only a whole-body phenomenon, different parts of our body age in a different way.
Some interventions that are being studied (for example, caloric restriction — the effects of which on human longevity are still very much unclear, so don’t starve yourself just yet) appear to have a positive effect on many tissues in the body. So perhaps the different ways in which different tissues age share some underlying pathways. Previous work indeed suggests that there may be genetic ‘master regulators’ of aging.
A new study now expands the genetic atlas that might help us dig up aging genetic’s roots.
Based on the Tabula Muris Senis dataset (a resource that aims to comprehensively capture aging dynamics across the lifespan in mice), the researchers sought to characterize changes in gene expression with aging, both at a general and tissue-specific level.
The Tabula Muris Senis dataset is a collation of single-cell RNA-sequencing data, curated by experts. This means that it gives scientists a cellular resolution view of gene activity by quantifying how much of which gene is being transcribed at which moments.
The data for this specific study was:
…collected from 16 C57BL/6JN mice (10 males, 6 females) with ages ranging from 3 months (20-year-old human equivalent) to 24 months (70-year-old human equivalent). It contains 120 cell types from 23 tissues, totaling 110,096 cells.
Looking at gene expression signatures across tissues and cell types, the authors found:
- 330 genes that changed expression levels with age in >50% of all studied tissue types. They named these global aging genes (GAGs).
These GAGs showed strong overlap with genes related to Alzheimer’s disease, neuroblastoma, fibrosarcoma, and osteoporosis.
The GAGs were generally associated with apoptosis, translation, biosynthesis, metabolism, and cellular organization, all of which play an important role in aging and age-related problems.
The authors aggregated GAGs in a GAG score, which reflected both chronological age and tissue-cell-specific aging effects.
This GAG score was positively correlated with cell turnover rate, meaning that cells that divide quickly (e.g. skin cells) scored higher than slow-dividing cells (e.g. heart muscle cells).
The GAG score could be validated on external data (also from mice).
Beyond the GAGs, the authors identified aging genes specific for different tissues and even specific for certain cell types, such as B cells, endothelial cells, muscle satellite cells, and so on.
To conclude, the authors write:
Overall, our study provides a comprehensive characterization of aging genes across a wide range of tissue-cell types in mice. In addition to the biological insights, it also serves as a comprehensive reference for researchers working on related topics.
In other words, a map outline that needs to be refined by further research.
- Mice are not human.
A list of genes is a great starting point. But not the endpoint. How do these genes interact with each other? How does their regulation respond to environmental influences (including lifestyle, diet, disease status…)?
It’s not yet clear if there is a connection between the GAGs and tissue-/cell-specific genes. That would be an interesting avenue to pursue (as the authors note as well).
The road from gene(s) to mechanism to intervention is a long and difficult one.
But at least we’re making progress on the map.
- Date of publication:
- Thu, 04/22/2021 - 15:53
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