The Dynamics of T-Cell Receptor Repertoire Diversity Following Thymus Transplantation for DiGeorge Anomaly

Abstract

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.

Modeling rejection immunity

“…The possibility of representing a pathophysiological process by means of a mathematical model allows the investigator to formalize beliefs, compare interpretations and simulate hypothetical scenarios of interest. Mathematical models have in particular already been introduced into several areas of immunology [3-8]. So far, however, no mathematical model has yet been presented describing allograft rejection in order to support the evaluation of therapies.…”

“…Mathematical models have been used to describe immunological behavior for a long time, beginning with the classical SIR model (Susceptible-Infectious-Recovered) first created for investigating the progress of an epidemic [2]. Several models describing the immune response in a number of diseases exist (HIV infection, tuberculosis, tumors..) [3-7] but, at present, only one model representing immune system dynamics during transplantation [8] has been published, which has the important aim to investigate T-cell population growth mechanisms, using thymus transplantation to follow the development of T-cells and their regulatory signals. The goal of the present work is somewhat different, in that we attempt to describe the main features of the immune system dynamics during general solid organ rejection.…”

Quantification of total T-cell receptor diversity by flow cytometry and spectratyping

“…This generates information about the heterogeneity of the relative frequencies of different CDR3 length products within a functional TCR BV family. Because different T-cell clones have different sequences or lengths of CDR3, analysis of the CDR3 length distributions can be used to determine the overall TCR repertoire diversity [7-11]. Spectratyping has the advantage of providing a finer level of resolution than just analyzing BV gene family expression on the T-cells of flow cytometry.…”

“…Traditionally, spectratyping data is quantified using a wide range of methods from visual [13,14] to quantitative scoring [15-17]. Our group previously described the use of a likelihood method for measuring deviation from a normal TCR repertoire [9,11]. For each observed CDR3 length distribution by spectratyping, we compute the Kullback-Leibler divergences between the patient CDR3 length distribution and a known reference distribution [9,11].…”

“…Our group previously described the use of a likelihood method for measuring deviation from a normal TCR repertoire [9,11]. For each observed CDR3 length distribution by spectratyping, we compute the Kullback-Leibler divergences between the patient CDR3 length distribution and a known reference distribution [9,11]. We have modified the Kullback-Leibler divergence to measure the deviation of T-cell receptor diversity from normal.…”