Age is not just a number: Naive T cells increase their ability to persist in the circulation over time.
Sanket RaneThea HoganBenedict SeddonAndrew J YatesPublished in: PLoS biology (2018)
The processes regulating peripheral naive T-cell numbers and clonal diversity remain poorly understood. Conceptually, homeostatic mechanisms must fall into the broad categories of neutral (simple random birth-death models), competition (regulation of cell numbers through quorum-sensing, perhaps via limiting shared resources), adaptation (involving cell-intrinsic changes in homeostatic fitness, defined as net growth rate over time), or selection (involving the loss or outgrowth of cell populations deriving from intercellular variation in fitness). There may also be stably maintained heterogeneity within the naive T-cell pool. To distinguish between these mechanisms, we confront very general models of these processes with an array of experimental data, both new and published. While reduced competition for homeostatic stimuli may impact cell survival or proliferation in neonates or under moderate to severe lymphopenia, we show that the only mechanism capable of explaining multiple, independent experimental studies of naive CD4+ and CD8+ T-cell homeostasis in mice from young adulthood into old age is one of adaptation, in which cells act independently and accrue a survival or proliferative advantage continuously with their post-thymic age. However, aged naive T cells may also be functionally impaired, and so the accumulation of older cells via 'conditioning through experience' may contribute to reduced immune responsiveness in the elderly.
Keyphrases
- single cell
- hiv infected
- induced apoptosis
- physical activity
- cell therapy
- middle aged
- body composition
- cell cycle arrest
- stem cells
- signaling pathway
- systematic review
- type diabetes
- machine learning
- metabolic syndrome
- adipose tissue
- high resolution
- antiretroviral therapy
- insulin resistance
- cell death
- mesenchymal stem cells
- drug induced
- preterm infants
- high fat diet induced
- neural network