T cell populations are regulated both by signals specific to the T-cell receptor (TCR) and by signals and resources, such as cytokines and space, that act independently of TCR specificity. Although it has been demonstrated that disruption of either of these pathways has a profound effect on T-cell development, we do not yet have an understanding of the dynamical interactions of these pathways in their joint shaping of the T cell repertoire. Complete DiGeorge Anomaly is a developmental abnormality that results in the failure of the thymus to develop, absence of T cells, and profound immune deficiency. After receiving thymic tissue grafts, patients suffering from DiGeorge anomaly develop T cells derived from their own precursors but matured in the donor tissue. We followed three DiGeorge patients after thymus transplantation to utilize the remarkable opportunity these subjects provide to elucidate human T-cell developmental regulation. Our goal is the determination of the respective roles of TCR-specific vs. TCR-nonspecific regulatory signals in the growth of these emerging T-cell populations. During the course of the study, we measured peripheral blood T-cell concentrations, TCRβ V gene-segment usage and CDR3-length spectratypes over two years or more for each of the subjects. We find, through statistical analysis based on a novel stochastic population-dynamic T-cell model, that the carrying capacity corresponding to TCR-specific resources is approximately 1000-fold larger than that of TCR-nonspecific resources, implying that the size of the peripheral T-cell pool at steady state is determined almost entirely by TCR-nonspecific mechanisms. Nevertheless, the diversity of the TCR repertoire depends crucially on TCR-specific regulation. The estimated strength of this TCR-specific regulation is sufficient to ensure rapid establishment of TCR repertoire diversity in the early phase of T cell population growth, and to maintain TCR repertoire diversity in the face of substantial clonal expansion-induced perturbation from the steady state.
The steady state T-cell population size arises in the balance among several phenomena, including the rapid and extensive expansion of rare clones through activation-induced peripheral division and their subsequent contraction (which constitute, in part, the T-cell immune response) and the relatively slow turnover of a diverse pool of naive cells through continuous thymic emigration and cell death. The specific memory T cells that arise as the result of clonal expansion appear to be regulated largely independently of the naive cells [11]. T cells arising through lymphopenia-induced proliferation acquire markers that ordinarily indicate a memory phenotype and may be regulated as memory cells, though this hypothesis has not been definitively tested. Both the size and diversity of peripheral T cell populations are controlled through competition for limiting resources. (In the interest of simplicity, we will use the term “resources” familiar from ecological population studies to refer to all of the factors that mediate growth regulation. We do not intend by this to exclude factors that are more accurately referred to as “signals”) [5], [13], and may also be controlled directly by the activities of regulatory T cells [14]. It has been shown, for example, that normal T-cell population growth is dependent on stimulation by self-peptide major histocompatibility complex (spMHC) complexes through the TCR [15]–[18], requiring a TCR that is specific for the spMHC complex. But T-cell population growth also depends on cytokines such as IL7 and IL15 that act independently of TCR specificity [5], [19]–[21]. Furthermore, growth and survival in all cells require adequate space and nutrients, the utilization of which is independent of TCR specificity [22], [23]. Our current understanding of lymphopenia-induced proliferation is due to studies in mice demonstrating that T cells divide rapidly after transfer into T cell-deficient (usually due to RAG or CD3 deficiency) or irradiated mice, but not after transfer to normal mice [15], [16]. Moreover, overall T cell numbers in T cell-deficient animals after transfer and clonal expansion are similar to T cell numbers in normal animals, suggesting control mechanisms acting on total T cell numbers, rather than in a clone-specific manner. The importance of TCR specific signals has been studied at length, showing that competition within T cell clones is important in maintaining TCR repertoire diversity. It has been shown, for example, that in a T-cell-deficient host, a T cell must interact with antigen-presenting cells bearing the MHC allele responsible for that cell' s thymic selection in order to proliferate [24]. In a T-cell sufficient host, such TCR-spMHC interaction is necessary for T cell survival [24], [25]. Furthermore, naive polyclonal T cells divide when transferred to TCR-transgenic hosts, as do monoclonal T cells transferred to TCR-transgenic hosts of differing clonotype. T cells do not divide, however, in hosts of identical clonotype [26]. Mice lacking MHC class II expression do not repopulate the periphery with CD4 T cells at all, suggesting that peripheral MHC class II expression is needed for the survival of CD4 T cells [25]. In the present context it is important to note that MHC class II matching in thymic grafts for complete DiGeorge subjects is not necessary for the development of CD4 T cells [27]. TCR-nonspecific signals include cytokines such as the cytokine interleukin-7 (IL7), which is necessary for the survival of nave T cells [28]–[32]. Lymphopenia-induced proliferation of memory cells requires IL7 or IL15 [13], [33]. T cells that have lost the ability to respond to IL7 after leaving the thymus are no longer able to proliferate, produce cytokines, or acquire memory cell phenotype [30]. In mice in which IL7 signaling has been completely abrogated, the few mature T cells found in the peripheral blood behave abnormally [30], [34], [35]. Experiments in IL7-receptor () -deficient mice (IL7R−/−) have shown a reduction in T-cell capacity to proliferate upon stimulation, leading to a six- to seven-fold reduction in the frequency of clonogenic T cells compared with T cells from IL7R-sufficient mice (IL7R+/+), as well as a 50% reduction in the average clone size of single IL7R−/− T cells compared with the IL7R+/+ T cells [34]. In another study, mice lacking the interleukin 2 (IL2) receptor chain () and/or the Jak family tyrosine kinase (Jak-3) had severe combined immune defects with lack of T lymphocyte maturation and function. This phenomenon is presumably attributable to the fact that is part of the receptor for IL7 and IL15 [36]; its loss leads to the abrogation of both of these cytokine signaling pathways [30], [35], and others as well. Moreover, in humans, in the absence of of or of the Jak-3 family, the periphery lacks T cells completely [37], [38]. To assess the contributions of thymic emigration rate on the steady-state T-cell population size, Berzins et al [39] engrafted variable numbers of thymuses into mice and observed that the size of the naive T-cell population increased in proportion to the number of thymic grafts, while the size of the memory population remained unchanged. It may be of importance to note that the thymus grafts themselves produced IL7. In humans, transplantation of thymic tissue at varying doses into complete DiGeorge anomaly subjects showed no significant effect on the nave CD4 or CD8 T cell numbers [27]. Complete DiGeorge Anomaly (cDGA) is a congenital condition, the hallmark of which is profound immunodeficiency arising from abnormal development of the third and fourth pharyngeal pouches resulting in thymic aplasia (complete absence of the thymus). This developmental irregularity may also cause various other anatomical abnormalities including heart defects, hypoparathyroidism, and craniofacial malformations [40]–[42]. In the absence of a thymus, cDGA subjects lack thymic-derived T cells and are consequently profoundly immunocompromised. Postnatal thymus transplantation can lead to a restoration of T-cell function and the development of a peripheral T-cell population with an apparently normal T-cell receptor repertoire [43]–[45]. Thymus transplantation is emerging as a valuable treatment for athymia generally. There is therefore substantial medical interest in elucidating the immunological recovery of thymus transplant patients quite apart from the basic immunology these patients may reveal. To illustrate the connection between these alternative homeostatic mechanisms and the observations one might make on the DiGeorge subjects, consider the following two scenarios as a thought experiment. First, suppose that TCR-specific resources such as spMHC are not limiting, either because they are produced in great excess or because they are rendered unnecessary. Under these conditions, homeostasis will be due exclusively to competition for TCR-nonspecific resources. The first T cells leaving the thymus will expand rapidly, consuming the TCR-nonspecific resources required for growth of their own growth and for the growth of all other clones. Subsequent T cells leaving the thymus will encounter a more impoverished environment and will grow more slowly, leading to early dominance of one or a small number of early clones and therefore a limited TCR repertoire (Figure 1). Conversely, if TCR-nonspecific resources such as cytokines were not limiting, and that homeostasis therefore depended solely on competition for TCR-specific signaling, the only cellular competition would be among T cells of the same clonotype. In this case, each clone will grow to roughly the same self-limiting size regardless of when its founder emigrates from the thymus. In this case, TCR repertoire diversity will grow at the greatest possible rate, all other things being equal (Figure 1). Reality will lie somewhere between these two limiting cases: TCR repertoire diversity will be shaped by both TCR-specific and TCR-nonspecific resources. Our goal is to explore, quantitatively, how these two sets of signals interact dynamically to shape the mature functional T cell population. The quantitative contributions of different signals in the regulation of T-cell number and the diversity of the TCR repertoire are very difficult to determine experimentally in the context of an intact system. To examine the interplay among the mechanisms responsible for the heterogeneity of the T-cell repertoire, we have developed a mathematical model for the effective interactions among T-cell clones. We model the temporal evolution of T-cell clones and their dynamics under the combined effect of TCR-specific and TCR-nonspecific signals. In particular, we consider competition within clones for spMHC complexes presented by antigen-presenting cells as well as competition among cells in all clones for cytokines, space and other TCR-non-specific resources. Such mathematical models have been used extensively to study the dynamical interactions between different branches of the immune system [46] and their effect on T cell [47]–[51] and B cell population development [52]. In this paper, we develop a model to examine competitive interactions among homo- and heterospecific T cells under limiting spMHC and TCR-nonspecific resources. This model then provides the basis for the analysis of clinical data from complete DiGeorge subjects following thymic transplantation to estimate the extent to which regulatory mechanisms working through these distinct pathways contribute to shaping the T cell population. The distinguishing feature of our model is its explicit representation of individual T cell clones, and the direct focus on the interplay between T-cell population size and TCR repertoire diversity this strategy allows. All model parameters are fit to data gathered within the present study; there are no parameters estimated from external sources.
The Kullback-Leibler divergence () is a measure of the difference between two probability mass functions that arises naturally in the context of likelihood ratio tests for multinomial distributions. The diversity of patient TCR repertoires can be quantified via -based comparison of patient spectratypes to mean spectratypes averaged over healthy controls [55], and -based comparison of TCRBV frequencies to those in healthy controls. Furthermore, can be decomposed hierarchically into components attributable, respectively, to variation in TCRBV family usage, CDR3 length variation within TCRBV family, and protein sequence variation within CDR3 length. For this work, we measure TCRBV family usage by flow cytometry and distributions of CDR3 lengths by spectratyping. The fact that we are not analyzing sequence data implies that we are not making the highest-resolution diversity measurements possible. We will show, however, that this potential limitation does not impede our ability to perform the measurements we set out to make. The data obtained in each of these assays represents the relative frequency of TCR counts in a subclass conditional on the parent class: is the fraction of cells using a gene segment from TCRBV family in a given DiGeorge subject and is the mean usage in our collection of healthy controls. , and are the relative numbers of T cells of CDR3 lengths within those cells using TCRBV family in a given patient, and among controls, respectively. The at the level of TCRBV is (6) where is the number of TCRBV families considered, which in our case is 23. The CDR3-length divergence within the family is (7) where is the number of CDR3 lengths, in our case 18. The total divergence is (8) The inverse of the can be regarded as the “completeness” of a TCR repertoire with respect to some standard [55]. Under very reasonable assumptions, the completeness may be regarded as a measure of the diversity, so we will refer to the diversity of the TCR repertoire, and quantify it by the sample completeness, . Note that diversity increases as the the decreases toward its minimum value, zero. The sample, like the sample entropy, is a biased estimator of the population: the expected value of the sample differs from the population by an additive term inversely proportional to the sample size. We account for this fact by including an additive bias as a parameter in the estimation procedure. We can then compare the estimated bias against the sample sizes (Table 1) for each datum. The model is based on absolute T-cell numbers, but the data are blood T cell concentrations. We must use a conversion factor that converts total T cell numbers to T-cell blood concentration. This factor is not easy to estimate with any precision, but our results are quite insensitive to large variation in. Assuming that a 10 kg subject has 600 ml of blood, and that there are 45 times more lymphocytes in the tissues than in the blood, based on adult data [56], we have. This is the conversion factor we use in the rest of the paper. Furthermore, some subjects have a relatively small number of anomalous T cells at the time of transplantation. We denote the concentration of such cells and treat them as a separate non-functional family. The model quantity ultimately used for comparison to patient T-cell concentration data is. We estimate the remaining parameters, and, as well as the measurement error variances and, and the measurement bias β, by fitting the model specified by Eq. (1) to the patient T cell concentration and TCR diversity data simultaneously using Markov Chain Monte Carlo (MCMC; see, eg, [57]). We estimate parameters by using the likelihood function given by (9) where and are the estimated T cell concentrations and diversities, respectively, under the model specified by Eqs. (1,4); and are the corresponding patient data observed at sampling times and, respectively. and are the total number of T cell concentration and diversity data points, respectively; and are the error variances. We compute the posterior joint density of all model parameters using a block-sampled Metropolis-Hastings MCMC. The parameters are all manifestly positive, and. Therefore, we transformed, and using natural logs. was treated using a logistic transformation. represents the maximum growth rate of T cell populations. The maximum possible rate is set by the minimum cell-cycle time for lymphocytes of about 6 hours, so we likewise used a logistic transformation on. The prior distributions on the parameters were uniform in the transformed variables.
The maximum-likelihood parameters estimates are shown in Table 2, along with the 95% credible intervals. The estimate for was much larger than the largest observation time, so we subsequently fixed, thereby eliminating one modeling degree of freedom and refit the model. We found that this simplification entailed no significant decrease in likelihood for any data set. This result implies that our experimental design is not capable of resolving the full time-course of the grafts' becoming functional, not necessarily that the emigration rate continues to increase indefinitely. The estimate of greatest interest for us is, which we find lies between about 10−4 and 10−3 in these three subjects. In subject one, who has the most complete data available and the tightest posterior marginal distribution on, produces an estimate midway between the other two. The parameter governing total thymic emigration, , does not have a simple direct interpretation, but one can estimate the thymic emigration rate at any time post-transplantation. These rates, estimated for the three subjects at one year post-transplantation, are 93,44, and 198 cells per day, respectively. These rates are several orders of magnitude smaller than those estimated for healthy humans infants. We can examine the estimated values of the the bias and use the known expression for this bias to compare to the actual sample sizes. Where the underlying distribution is multinomial, the theoretical bias is given by where is the number of classes in the multinomial, and is the sample size. Using this formula to estimate the sample size leads to the conclusion that the effective sizes of the samples used to estimate diversity are between 103 and 105 cells, consistent with the measured sample sizes (Table 1). Figure 3 provides a visual assessment of the quality of the model fits to these data. In order to gain a deeper understanding of the role of on the dynamics of both T cell population size and diversity, we performed two additional analyses. We use the data from subject one to illustrate these points. Analyses on the other two subjects yielded comparable results and lead to the same conclusions. A reasonable concern is that thymic emigration rate and may be confounded–that it may be possible to compensate for smaller by making larger. We addressed that concern by performing a numerical experiment to determine the impact on the model fit of fixing outside the inferred credible bounds. We fix to be 10-fold smaller or larger than its maximum-likelihood estimate for subject 1. The fits are significantly worse (compare Figures 3 and 4), with log-likelihood ratios of about 8 in both cases (Table 3). A focused analysis of the time-dependent sensitivity of the model trajectories to parameter variation may provide further insight into the mechanisms of regulation. We examined the sensitivity of our model to changes in [58], [59] and as follows. Define the sensitivity functions, and. The sensitivity equations are obtained by differentiating both sides of equation (1) with respect to. The are computed as the solutions of (10) with for all i. The sensitivity function for D is given by. The relative sensitivities are calculated by multiplying the absolute sensitivity by and dividing by the value of the relevant response variable. The sensitivity to is due largely to the changed timing of discrete events, and so does not lend itself easily to differential sensitivity analysis in the form outlined in Eq (10). Sensitivity to was therefore estimated directly by generating sample paths under different values of. We know that mean sensitivity to is transient because the steady-state population size and diversity do not depend on in the deterministic limit (Eqs. 2,3). We find, however, that the diversity remains sensitive to changes in throughout the two-year period of the study (Figure 5). It should be noted, however, that this assessment is based on the true diversity rather than the sample diversity, whose resolution is limited by sample size. At the sample sizes available, the sensitivity of the measurable diversity to vanishes between 6 and 12 months. The population size is very sensitive to in the first 3–4 months, becoming less so rapidly after that time. As increases, the population size decreases during the sensitive period. Increases in the thymic emigration rate have a positive effect on both the population size and the diversity, as expected. In this case, the sensitivity of the diversity is greatest earlier than in the case of ρ. It should be noted that diversity at the level of amino acid sequence differences is not resolved by our assays, regardless of sample size. If it were, we would expect the diversity to remain sensitive to both of these parameters for a longer period of time. The model does not account for the micro-environmental perturbations invariably encountered by people, including our study subjects. Infection produces a transient change in the steady state of the T cell repertoire, typified by a decrease in the diversity. Once the infection has been resolved, the T-cell population returns to its steady state, and the diversity relaxes back to its original value. The rate at which the return to steady state values occurs depends on ρ. To examine this dependence, we carried out the following numerical experiment. We start with a system at steady state, and suddenly increase the size of a single clone by a factor of 104, allowing it to consume the non-TCR specific resources its new size requires. The diversity decreases as a result. Over a few days, the other clones adjust to the new steady state. After ten days we remove the artificial support for the enlarged clone and allow the system to return to steady state (Figure 6). We ran this experiment using models with four values of ρ differing over four orders of magnitude. For high ρ, corresponding to a high competition for specific signals, the diversity decreases less than in the absence of intra-clonal competition. Moreover, after the perturbation is resolved, the diversity increases back to its steady-state, pre-perturbation value at a rate that depends very sensitively on. The return to steady-state diversity is more rapid the greater the competition for TCR-specific signals. The estimated values for obtained from the DiGeorge patients implies a diversity-return time on the order of a few days, rather than weeks or more.
The functional integration of the manifold processes involved in the development and maintenance of the mature T cell repertoire has not yet been fully elucidated. These processes include thymic export, competition for TCR ligands, and competition for non-specific stimulatory factors. Here, we study T cell homeostasis in complete DiGeorge Anomaly patients during the establishment of T cells following thymic transplantation. To quantify the balance between TCR-specific and TCR-nonspecific factors that act to limit T-cell population growth, we developed a mathematical model that accounts for intra- and inter-clonal competition. The key parameter in this model for the present purpose is ρ. This parameter can be given a functional interpretation by considering the case of an individual capable of rearranging just a single TCR. The size of this hypothetical sole clone, at equilibrium, must be larger than the size of the same clone in the presence of a T cell population with a complete repertoire. The ratio of these two sizes is 1∶ρ. We have estimated to be about 10−3; the carrying capacity for a single isolated clone is about 1000 times larger that that for the same clone under normal heterogeneous conditions. This result suggests that TCR-specific resource limitation is relatively unimportant, but this is true only for the total T-cell population size near steady state; away from steady state, TCR-specific regulation may is crucial to the establishment and maintenance of repertoire diversity. At steady-state, TCR diversity is maintained through competition for self peptide MHC [60], and population numbers are maintained through competition for cytokines and other resources not specific to the TCR [5], [19]. Our models fit the data adequately only when the thymic emigration is an accelerating function of time through the initial post-transplantation phase, as would be expected from the biology of thymus transplantation [61], [62]. The slices of cultured thymus tissue used for transplantation are cultured from 2–3 weeks prior to transplantation. There is dramatic depletion of thymocytes from the tissue, thymic epithelium becomes condensed, and the cortico-medullary distinction is lost. In the first months after transplantation, the epithelium differentiates so that cortex and medulla again are distinct and the epithelium develops its characteristic lacy appearance. During this process, the numbers of thymocytes in the transplanted tissue increases dramatically, from small numbers of scattered thymocytes to densely packed thymocytes throughout the allograft. Thus, the biology predicts an increase in emigration with time after transplantation as normal thymic architecture is reestablished. The current study was not able to treat naive and memory T cells separately, but doing so is clearly of great interest given the indications that these pools are regulated independently of each other [11]. Furthermore, we were not able to measure thymic emigration directly, though these measurements would be of significant interest given the surprisingly small rates inferred here, and the impact that the reconciliation of this implied inconsistency would have on our understanding of the treatment of athymia and thymic dysfunction. Finally, our model contains terms for the density-dependent competition among T cells (Eq. 1) whose forms are commonly accepted in population modeling but do not have direct empirical justification in the present context. Although we believe that our conclusions are robust against reasonable variation in these terms, it would be of great utility to explore this issue explicitly. Our aim has been to study T cell homeostasis in a more natural human system than has otherwise been available thus far. It remains to be shown just how natural the post-transplantation environment is in this regard, and therefore how broadly applicable our findings are. We are encouraged, however, by the fact that complete DiGeorge patients receiving thymus transplants recover adaptive immune function. It is important to note that total T-cell counts in these recovered patients remains below normal, typically at about the 10th percentile for children of the same age [45]. Although specific reasons for this condition have not yet been determined, no molecular defects in T-cell function have been reported. We regard it as possible that the sole immunological lesion in DiGeorge anomaly is athymia, and that, once ameliorated by transplantation, the emergence of the T cell population proceeds as it would in a non-affected T-cell lymphopenic subject. We expect, therefore, that our findings will be generally applicable. Our study enabled us to discriminate between two qualitatively different forms of population regulation: TCR-specific regulation and TCR-nonspecific regulation, and to elucidate their roles in jointly maintaining the dynamic homeostasis of the T cell population. The parameters estimates obtained by analyzing data from three DiGeorge Anomaly patients suggest that T-cell population size is maintained by TCR-nonspecific mechanisms, and TCR repertoire diversity is maintained by TCR-specific mechanisms.
Protective adaptive immunity depends crucially on the enormous diversity of the T-cell receptor repertoire, the antigen receptors expressed collectively on T-cell populations. T cells develop from T-cell precursors that originate in the bone marrow and migrate to the thymus, where their T cell receptors are constructed stochastically, and tested for autoreactivity against a host of self antigens. Complete DiGeorge anomaly is a rare congenital disease in which the thymus fails to develop, blocking all T cell development and causing profound immunodeficiency. Thymus transplantation, performed in the first two post-natal years, allows the patient' s own T cell precursors to develop in the engrafted thymus tissue into normal, functioning T cells. In addition to saving patients' lives, this procedure provides an extraordinary opportunity to study the de novo development of human T cell populations. We have developed a mathematical model to aid in the statistical analysis of the precious data from these patients. In addition to helping elucidate the means by which the size and diversity of T cell populations are jointly regulated, the insights gained from this study hold promise for the development of therapies to promote immune recovery after transplantation.
mathematics/statistics
immunology/leukocyte development
The Dynamics of T-Cell Receptor Repertoire Diversity Following Thymus
                    Transplantation for DiGeorge Anomaly