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. 2018 Apr 11;16(4):e2003949.
doi: 10.1371/journal.pbio.2003949. eCollection 2018 Apr.

Age is not just a number: Naive T cells increase their ability to persist in the circulation over time

Sanket Rane  1   2 , Thea Hogan  3 , Benedict Seddon  3 , Andrew J Yates  1   2
Affiliations

Age is not just a number: Naive T cells increase their ability to persist in the circulation over time

Sanket Rane et al. PLoS Biol. .

Abstract

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.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Models of naive T-cell homeostasis.
(A) A simple random birth–death model, with both processes occuring at rates that are constant over time and identical for all cells. (B) A model of density-dependent homeostasis in which every cell’s homeostatic fitness declines equally with total population size, due to resource competition or other forms of quorum-sensing. (C) A model of adaptation in which every cell’s fitness increases progressively with its post-thymic age. We assume that post-thymic age is inherited by a dividing cell’s offspring. (D) A model in which T cells are generated with a distribution of intrinsic, constant, and heritable fitnesses. Over time, selection acts on this distribution. (E) A variant of the selection model, in which the naive pool comprises numerically stable, self-renewing ‘incumbent’ cells generated early in life and ‘displaceable’ cells that are continually replaced by new emigrants from the thymus.
Fig 2
Fig 2. Comparison of the ability of models to explain naive T-cell dynamics in euthymic and Tx mice.
(A) The neutral model describes replete kinetics well but fails to capture the slow loss of cells following thymectomy. (B) Comparing the density-dependent, adaptation, selection, and incumbent models using data from thymectomy onwards. (C) The same comparisons but using data from birth. Insets detail the growth of naive T-cell numbers in young mice. Data are provided in S1 Data. Tx, thymectomised; WT, wild-type.
Fig 3
Fig 3. Comparison of models explaining naive T-cell dynamics in busulfan chimeras.
(A) Generating busulfan chimeras. (B) Fitting the incumbent, adaptation, and selection models to naive CD4 and CD8 counts from busulfan chimeras made in recipients of different ages. (C) Simultaneous fits to the normalised chimerism in the peripheral naive CD4 and CD8 pools. Colours indicate different age groups of recipient mice. The predictions shown were generated using the mode of the age within each group. Data are provided in S2 Data. BMT, bone marrow transplant.
Fig 4
Fig 4. Discriminating between models of heterogeneous turnover by simulating the adoptive transfer experiments reported in ref. [14].
(A) Observed and predicted kinetics of loss of naive CD4 cells taken from young (2 mo) (solid circles and solid lines) and aged (24 mo) (open circles and dashed lines) donors, using the adaptation (blue line), incumbent (red lines), and selection (green lines) models. Data were taken from Fig 2C in ref. [14]. Parameters were taken from the fits to the data from WT and Tx mice reported in ref. [8] and from our busulfan chimera data (left and right panels, respectively). (B) Predicting the trend in the loss of donor cells from the naive CD4 pool over 15 d, using AND TCR transgenic naive T cells taken from donors of four different ages (left panel, data reproduced from Fig. 4E in ref. [14]). The three panels to the right show the models’ predicted fold loss of polyclonal naive CD4 and CD8 T cells 15 d after adoptive transfer, from donors of the same ages used in the AND adoptive transfer experiment. Due to intrinsic differences in the survival rates of AND and polyclonal naive cells, the absolute levels of recovery differ substantially, but the predicted trends show that only the adaptation model is able to reproduce a consistent increase in recovery with donor age. As in panel A, these predictions were generated using model parameters obtained from fits to the data from the busulfan chimeras (solid circles) and from ref. [8] (open circles). Data are provided in S3 Data. TCR, T-cell receptor; Tx, thymectomised; WT, wild-type.
Fig 5
Fig 5. Predicted post-thymic cell age distributions, for different host ages, using the adaptation model.
The normalised age distributions of naive CD4 and CD8 T cells were generated using parameters obtained from fits to the data from den Braber et al. [8] (upper panels) and from the busulfan chimeras (lower panels). Different coloured curves denote different host ages. Distributions illustrate the preferential accumulation with longer-lived and/or more proliferative cell populations with host age. The discontinuity in the gradients at older cell ages derives from uncertainty in the precise form of the age distribution of cells at the beginning of the experiment. We proposed various forms for this distribution, and the parameters of each were estimated from the data. However, for all distributions we explored, the same U-shaped trend emerges over time.
Fig 6
Fig 6. Simulations of recovery of naive CD4 T-cell numbers following depletion.
Under the assumptions of pure density-dependent regulation of naive T-cell numbers (left panels), or the adaptation model (right panels), we simulated recovery from different levels of naive CD4 T-cell depletion at age 200 d (upper panels) and recovery from 50% depletion at different host ages (lower panels). Simulations were performed using the model parameters estimated from the data in ref. [8].
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