Natural selection on fecundity variance in subdivided populations: kin selection meets bet hedging.

In a series of seminal articles in 1974, 1975, and 1977, J. H. Gillespie challenged the notion that the "fittest" individuals are those that produce on average the highest number of offspring. He showed that in small populations, the variance in fecundity can determine fitness as much as mean fecundity. One likely reason why Gillespie's concept of within-generation bet hedging has been largely ignored is the general consensus that natural populations are of large size. As a consequence, essentially no work has investigated the role of the fecundity variance on the evolutionary stable state of life-history strategies. While typically large, natural populations also tend to be subdivided in local demes connected by migration. Here, we integrate Gillespie's measure of selection for within-generation bet hedging into the inclusive fitness and game theoretic measure of selection for structured populations. The resulting framework demonstrates that selection against high variance in offspring number is a potent force in large, but structured populations. More generally, the results highlight that variance in offspring number will directly affect various life-history strategies, especially those involving kin interaction. The selective pressures on three key traits are directly investigated here, namely within-generation bet hedging, helping behaviors, and the evolutionary stable dispersal rate. The evolutionary dynamics of all three traits are markedly affected by variance in offspring number, although to a different extent and under different demographic conditions.

Clonotype selection and composition of human CD8 T cells specific for persistent herpes viruses varies with differentiation but is stable over time.

Protection from reactivation of persistent herpes virus infection is mediated by Ag-specific CD8 T cell responses, which are highly regulated by still poorly understood mechanisms. In this study, we analyzed differentiation and clonotypic dynamics of EBV- and CMV-specific T cells from healthy adults. Although these T lymphocytes included all subsets, from early-differentiated (EM/CD28(pos)) to late-differentiated (EMRA/CD28(neg)) stages, they varied in the sizes/proportions of these subsets. In-depth clonal composition analyses revealed TCR repertoires, which were highly restricted for CMV- and relatively diverse for EBV-specific cells. Virtually all virus-specific clonotypes identified in the EMRA/CD28(neg) subset were also found within the pool of less differentiated "memory" cells. However, striking differences in the patterns of dominance were observed among these subsets, because some clonotypes were selected with differentiation while others were not. Late-differentiated CMV-specific clonotypes were mostly characterized by TCR with lower dependency on CD8 coreceptor interaction. Yet all clonotypes displayed similar functional avidities, suggesting a compensatory role of CD8 in the clonotypes of lower TCR avidity. Importantly, clonotype selection and composition of each virus-specific subset upon differentiation was highly preserved over time, with the presence of the same dominant clonotypes at specific differentiation stages within a period of 4 years. Remarkably, clonotypic distribution was stable not only in late-differentiated but also in less-differentiated T cell subsets. Thus, T cell clonotypes segregate with differentiation, but the clonal composition once established is kept constant for at least several years. These findings reveal novel features of the highly sophisticated control of steady state protective T cell activity in healthy adults.

Resting-State Functional Connectivity Emerges from Structurally and Dynamically Shaped Slow Linear Fluctuations.

Brain fluctuations at rest are not random but are structured in spatial patterns of correlated activity across different brain areas. The question of how resting-state functional connectivity (FC) emerges from the brain's anatomical connections has motivated several experimental and computational studies to understand structure-function relationships. However, the mechanistic origin of resting state is obscured by large-scale models' complexity, and a close structure-function relation is still an open problem. Thus, a realistic but simple enough description of relevant brain dynamics is needed. Here, we derived a dynamic mean field model that consistently summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network, in which connectivity is constrained by diffusion imaging data from human subjects. The dynamic mean field approximates the ensemble dynamics, whose temporal evolution is dominated by the longest time scale of the system. With this reduction, we demonstrated that FC emerges as structured linear fluctuations around a stable low firing activity state close to destabilization. Moreover, the model can be further and crucially simplified into a set of motion equations for statistical moments, providing a direct analytical link between anatomical structure, neural network dynamics, and FC. Our study suggests that FC arises from noise propagation and dynamical slowing down of fluctuations in an anatomically constrained dynamical system. Altogether, the reduction from spiking models to statistical moments presented here provides a new framework to explicitly understand the building up of FC through neuronal dynamics underpinned by anatomical connections and to drive hypotheses in task-evoked studies and for clinical applications.

Closing the gap between T-cell life span estimates from stable isotope-labeling studies in mice and humans.

Quantitative knowledge of the turnover of different leukocyte populations is a key to our understanding of immune function in health and disease. Much progress has been made thanks to the introduction of stable isotope labeling, the state-of-the-art technique for in vivo quantification of cellular life spans. Yet, even leukocyte life span estimates on the basis of stable isotope labeling can vary up to 10-fold among laboratories. We investigated whether these differences could be the result of variances in the length of the labeling period among studies. To this end, we performed deuterated water-labeling experiments in mice, in which only the length of label administration was varied. The resulting life span estimates were indeed dependent on the length of the labeling period when the data were analyzed using a commonly used single-exponential model. We show that multiexponential models provide the necessary tool to obtain life span estimates that are independent of the length of the labeling period. Use of a multiexponential model enabled us to reduce the gap between human T-cell life span estimates from 2 previously published labeling studies. This provides an important step toward unambiguous understanding of leukocyte turnover in health and disease.