How senescent cells drive skin aging

Cellular senescence is a fascinating process (which is why I chose a lab that researches it for my PhD), with both beneficial and detrimental consequences. A recent study shows a connection between melanocyte senescence and the induction of telomere dysfunction in nearby keratinocytes which is associated with epidermal atrophy. This increased with age. Using a senolytic in a human 3D culture model they were able to reduce this effect.

Reversing (cellular) aging


Aging is characterized by a gradual loss of function occurring at the molecular, cellular, tissue and organismal levels. At the chromatin level, aging associates with progressive accumulation of epigenetic errors that eventually lead to aberrant gene regulation, stem cell exhaustion, senescence, and deregulated cell/tissue homeostasis.

Nuclear reprogramming to pluripotency can revert both the age and the identity of any cell to that of an embryonic cell. Recent evidence shows that transient reprogramming can ameliorate age-associated hallmarks and extend lifespan in progeroid mice. However, it is unknown how this form of rejuvenation would apply to naturally aged human cells.

Here we show that transient expression of nuclear reprogramming factors, mediated by expression of mRNAs, promotes a rapid and broad amelioration of cellular aging, including resetting of epigenetic clock, reduction of the inflammatory profile in chondrocytes, and restoration of youthful regenerative response to aged, human muscle stem cells, in each case without abolishing cellular identity.

EDITOR’S NOTE: brief exposure to Yamakana factors (proteins that are used to convert cells to stem cells) somehow reversed many of the epigenetic changes (errors) that accumulate with age, making ‘old’ cells ‘young’ again.

Human gut microbiome aging clock

cup and alarm clock


The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host’s age based on the gut community composition.

Here we developed a method of predicting hosts’ age based on microflora taxonomic profiles using a cross-study dataset and deep learning. Our best model has an architecture of a deep neural network that achieves the mean absolute error of 5.91 years when tested on external data. We further advance a procedure for inferring the role of particular microbes during human aging and defining them as potential aging biomarkers.

The described intestinal clock represents a unique quantitative model of gut microflora aging and provides a starting point for building host aging and gut community succession into a single narrative.

FULL TEXT (PDF): iScience