Comprehensive assessment and mathematical modeling of T cell population dynamics and homeostasis

High-resolution mapping of cell cycle dynamics during steady-state T cell development and regeneration in vivo

Control of cell proliferation is critical for the lymphocyte life cycle. However, little is known about how stage-specific alterations in cell cycle behavior drive proliferation dynamics during T cell development. Here, we employed in vivo dual-nucleoside pulse labeling combined with the determination of DNA replication over time as well as fluorescent ubiquitination-based cell cycle indicator mice to establish a quantitative high-resolution map of cell cycle kinetics of thymocytes. We developed an agent-based mathematical model of T cell developmental dynamics. To generate the capacity for proliferative bursts, cell cycle acceleration followed a "stretch model" characterized by the simultaneous and proportional contraction of both G1 and S phases. Analysis of cell cycle phase dynamics during regeneration showed tailored adjustments of cell cycle phase dynamics. Taken together, our results highlight intrathymic cell cycle regulation as an adjustable system to maintain physiologic tissue homeostasis and foster our understanding of dysregulation of the T cell developmental program.

Modeling BK Virus Infection in Renal Transplant Recipients

Kidney transplant recipients require a lifelong protocol of immunosuppressive therapy to prevent graft rejection. However, these same medications leave them susceptible to opportunistic infections. One pathogen of particular concern is human polyomavirus 1, also known as BK virus (BKPyV). This virus attacks kidney tubule epithelial cells and is a direct threat to the health of the graft. Current standard of care in BK virus-infected transplant recipients is reduction in immunosuppressant therapy, to allow the patient's immune system to control the virus. This requires a delicate balance; immune suppression must be strong enough to prevent rejection, yet weak enough to allow viral clearance. We seek to model viral and immune dynamics with the ultimate goal of applying optimal control methods to this problem. In this paper, we begin with a previously published model and make simplifying assumptions that reduce the number of parameters from 20 to 14. We calibrate our model using newly available patient data and a detailed sensitivity analysis. Numerical results for multiple patients are given to show that the newer model reflects observed dynamics well.

Primary and secondary defects of the thymus

The thymus is the primary site of T-cell development, enabling generation, and selection of a diverse repertoire of T cells that recognize non-self, whilst remaining tolerant to self- antigens. Severe congenital disorders of thymic development (athymia) can be fatal if left untreated due to infections, and thymic tissue implantation is the only cure. While newborn screening for severe combined immune deficiency has allowed improved detection at birth of congenital athymia, thymic disorders acquired later in life are still underrecognized and assessing the quality of thymic function in such conditions remains a challenge. The thymus is sensitive to injury elicited from a variety of endogenous and exogenous factors, and its self-renewal capacity decreases with age. Secondary and age-related forms of thymic dysfunction may lead to an increased risk of infections, malignancy, and autoimmunity. Promising results have been obtained in preclinical models and clinical trials upon administration of soluble factors promoting thymic regeneration, but to date no therapy is approved for clinical use. In this review we provide a background on thymus development, function, and age-related involution. We discuss disease mechanisms, diagnostic, and therapeutic approaches for primary and secondary thymic defects.

An integrative mechanistic model of thymocyte dynamics

Background

The thymus plays a central role in shaping human immune function. A mechanistic, quantitative description of immune cell dynamics and thymic output under homeostatic conditions and various patho-physiological scenarios are of particular interest in drug development applications, e.g., in the identification of potential therapeutic targets and selection of lead drug candidates against infectious diseases.

Methods

We here developed an integrative mathematical model of thymocyte dynamics in human. It incorporates mechanistic features of thymocyte homeostasis as well as spatial constraints of the thymus and considerations of age-dependent involution. All model parameter estimates were obtained based on published physiological data of thymocyte dynamics and thymus properties in mouse and human. We performed model sensitivity analyses to reveal potential therapeutic targets through an identification of processes critically affecting thymic function; we further explored differences in thymic function across healthy subjects, multiple sclerosis patients, and patients on fingolimod treatment.

Results

We found thymic function to be most impacted by the egress, proliferation, differentiation and death rates of those thymocytes which are most differentiated. Model predictions also showed that the clinically observed decrease in relapse risk with age, in multiple sclerosis patients who would have discontinued fingolimod therapy, can be explained mechanistically by decreased thymic output with age. Moreover, we quantified the effects of fingolimod treatment duration on thymic output.

Conclusions

In summary, the proposed model accurately describes, in mechanistic terms, thymic output as a function of age. It may be further used to perform predictive simulations of clinically relevant scenarios which combine specific patho-physiological conditions and pharmacological interventions of interest.

Distinguishing between monozygotic twins' blood samples through immune repertoire sequencing

Monozygotic (MZ) twins with highly similar genomic DNA sequences can not be distinguished by conventional forensic DNA testing. The immune repertoire (IR) reflects an individual's immune history, which is unique between individuals, has been applied to individualized treatment in precision medicine. However, the application of IR in forensic genetics has not been reported to date. In this study, the diversity in the complementary determining region 3 (CDR3) of both the T-cell receptor β chain (TCRβ) and B-cell receptor heavy chain (also known as immunoglobulin heavy chain, IGH) in four pairs of MZ twins were analyzed. The results showed that the amino acid sequences length distribution frequency of TCRβ CDR3 had 4-10 differences, and the nucleic acid sequences length distribution frequency of TCRβ CDR3 had 2-7 differences between MZ twins. The shared difference of four pairs of MZ twins focused on the length distribution frequency of 34 bp nucleotide sequences in TCRβ. By analyzing the usage frequency of V and J genes in TCRβ and IGH CDR3 DNA sequence rearrangements, we also found that there were biases between each pair of MZ twins, and the usage frequency of TRBJ2-3 showed common differences between each pair of MZ twins. Furthermore, each pair of MZ twins had its own unique V-J genes combination mode in TCRβ and IGH CDR3 DNA sequences. This study, for the first time, suggested that IR can be used as a potential biological marker to distinguish MZ twins.

Why are cell populations maintained via multiple compartments?

We consider the maintenance of 'product' cell populations from 'progenitor' cells via a sequence of one or more cell types, or compartments, where each cell's fate is chosen stochastically. If there is only one compartment then large amplification, that is, a large ratio of product cells to progenitors comes with disadvantages. The product cell population is dominated by large families (cells descended from the same progenitor) and many generations separate, on average, product cells from progenitors. These disadvantages are avoided using suitably constructed sequences of compartments: the amplification factor of a sequence is the product of the amplification factors of each compartment, while the average number of generations is a sum over contributions from each compartment. Passing through multiple compartments is, in fact, an efficient way to maintain a product cell population from a small flux of progenitors, avoiding excessive clonality and minimizing the number of rounds of division en route. We use division, exit and death rates, estimated from measurements of single-positive thymocytes, to choose illustrative parameter values in the single-compartment case. We also consider a five-compartment model of thymocyte differentiation, from double-negative precursors to single-positive product cells.

Modeling the Dynamics of T-Cell Development in the Thymus

The thymus hosts the development of a specific type of adaptive immune cells called T cells. T cells orchestrate the adaptive immune response through recognition of antigen by the highly variable T-cell receptor (TCR). T-cell development is a tightly coordinated process comprising lineage commitment, somatic recombination of Tcr gene loci and selection for functional, but non-self-reactive TCRs, all interspersed with massive proliferation and cell death. Thus, the thymus produces a pool of T cells throughout life capable of responding to virtually any exogenous attack while preserving the body through self-tolerance. The thymus has been of considerable interest to both immunologists and theoretical biologists due to its multi-scale quantitative properties, bridging molecular binding, population dynamics and polyclonal repertoire specificity. Here, we review experimental strategies aimed at revealing quantitative and dynamic properties of T-cell development and how they have been implemented in mathematical modeling strategies that were reported to help understand the flexible dynamics of the highly dividing and dying thymic cell populations. Furthermore, we summarize the current challenges to estimating in vivo cellular dynamics and to reaching a next-generation multi-scale picture of T-cell development.

Impaired Activated/Memory Regulatory T Cell Clonal Expansion Instigates Diabetes in NOD Mice

Regulatory T cell (Treg) insufficiency licenses the destruction of insulin-producing pancreatic β-cells by autoreactive effector T cells (Teffs), causing spontaneous autoimmune diabetes in NOD mice. We investigated the contribution to diabetes of the T-cell receptor (TCR) repertoires of naive regulatory T cells (nTregs), activated/memory Tregs (amTregs), and CD4⁺ Teffs from prediabetic NOD mice and normal C57BL/6 (B6) mice. NOD mice amTreg and Teff repertoire diversity was unexpectedly higher than that of B6 mice. This was due to the presence of highly expanded clonotypes in B6 amTregs and Teffs that were largely lost in their NOD counterparts. Interleukin-2 (IL-2) administration to NOD mice restored such amTreg clonotype expansions and prevented diabetes development. In contrast, IL-2 administration only led to few or no clonotype expansions in nTregs and Teffs, respectively. Noteworthily, IL-2-expanded amTreg and nTreg clonotypes were markedly enriched in islet-antigen specific TCRs. Altogether, our results highlight the link between a reduced clonotype expansion within the activated Treg repertoire and the development of an autoimmune disease. They also indicate that the repertoire of amTregs is amenable to rejuvenation by IL-2.

Influenza A virus-induced thymus atrophy differentially affects dynamics of conventional and regulatory T-cell development in mice

Foxp3⁺ Treg cells, which are crucial for maintenance of self-tolerance, mainly develop within the thymus, where they arise from CD25⁺ Foxp3^- or CD25^- Foxp3⁺ Treg cell precursors. Although it is known that infections can cause transient thymic involution, the impact of infection-induced thymus atrophy on thymic Treg (tTreg) cell development is unknown. Here, we infected mice with influenza A virus (IAV) and studied thymocyte population dynamics post infection. IAV infection caused a massive, but transient thymic involution, dominated by a loss of CD4⁺ CD8⁺ double-positive (DP) thymocytes, which was accompanied by a significant increase in the frequency of CD25⁺ Foxp3⁺ tTreg cells. Differential apoptosis susceptibility could be experimentally excluded as a reason for the relative tTreg cell increase, and mathematical modeling suggested that enhanced tTreg cell generation cannot explain the increased frequency of tTreg cells. Yet, an increased death of DP thymocytes and augmented exit of single-positive (SP) thymocytes was suggested to be causative. Interestingly, IAV-induced thymus atrophy resulted in a significantly reduced T-cell receptor (TCR) repertoire diversity of newly produced tTreg cells. Taken together, IAV-induced thymus atrophy is substantially altering the dynamics of major thymocyte populations, finally resulting in a relative increase of tTreg cells with an altered TCR repertoire.

Immune fingerprinting through repertoire similarity

Immune repertoires provide a unique fingerprint reflecting the immune history of individuals, with potential applications in precision medicine. However, the question of how personal that information is and how it can be used to identify individuals has not been explored. Here, we show that individuals can be uniquely identified from repertoires of just a few thousands lymphocytes. We present "Immprint," a classifier using an information-theoretic measure of repertoire similarity to distinguish pairs of repertoire samples coming from the same versus different individuals. Using published T-cell receptor repertoires and statistical modeling, we tested its ability to identify individuals with great accuracy, including identical twins, by computing false positive and false negative rates < 10-6 from samples composed of 10,000 T-cells. We verified through longitudinal datasets that the method is robust to acute infections and that the immune fingerprint is stable for at least three years. These results emphasize the private and personal nature of repertoire data.

Individuation and the Organization in Complex Living Ecosystem: Recursive Integration and Self-assertion by Holon-Lymphocytes

Individuation and organization in complex living multi-level ecosystem occurs as dynamical processes from early ontogeny. The notion of living "holon" displaying dynamic self-assertion and integration is used here to explain the ecosystems dynamic processes. The update of the living holon state according to the continuous change of the dynamic system allows for its viability. This is interpreted as adaptation, selection and organization by the human that observes the system a posteriori from its level. Our model concerns the complex dynamics of the adaptive immune system, integrating holon-lymphocytes that collectively preserve the identity and integrity of the organism. Each lymphocyte individualizes as a dynamic holon-lymphocyte, with somatic gene individuation leading to an individual, singular antigen immunoreceptor type, promoting the self-assertion. In turn, the "Immunoception" allows for perception of the environmental antigenic context, thus integration of the holon in its environment. The self-assertion/integration of holon-lymphocyte starts from fetal stages and is influenced by mother Lamarckian acquired historicity transmissions, a requisite for the integrity of the holobiont-organism. We propose a dynamic model of the perception by holon-lymphocyte, and at the supra-clonal level of the immune system functions that sustain the identity and integrity of the holon-holobiont organism.

Quantitative analysis reveals reciprocal regulations underlying recovery dynamics of thymocytes and thymic environment in mice

Thymic crosstalk, a set of reciprocal regulations between thymocytes and the thymic environment, is relevant for orchestrating appropriate thymocyte development as well as thymic recovery from various exogenous insults. In this work, interactions shaping thymic crosstalk and the resultant dynamics of thymocytes and thymic epithelial cells are inferred based on quantitative analysis and modeling of the recovery dynamics induced by irradiation. The analysis identifies regulatory interactions consistent with known molecular evidence and reveals their dynamic roles in the recovery process. Moreover, the analysis also predicts, and a subsequent experiment verifies, a previously unrecognized regulation of CD4+CD8+ double positive thymocytes which temporarily increases their proliferation rate upon the decrease in their population size. Our model establishes a pivotal step towards the dynamic understanding of thymic crosstalk as a regulatory network system.

Intrathymic adeno-associated virus gene transfer rapidly restores thymic function and long-term persistence of gene-corrected T cells

BACKGROUND

Patients with T-cell immunodeficiencies are generally treated with allogeneic hematopoietic stem cell transplantation, but alternatives are needed for patients without matched donors. An innovative intrathymic gene therapy approach that directly targets the thymus might improve outcomes.

OBJECTIVE

We sought to determine the efficacy of intrathymic adeno-associated virus (AAV) serotypes to transduce thymocyte subsets and correct the T-cell immunodeficiency in a zeta-associated protein of 70 kDa (ZAP-70)-deficient murine model.

METHODS

AAV serotypes were injected intrathymically into wild-type mice, and gene transfer efficiency was monitored. ZAP-70^-/- mice were intrathymically injected with an AAV8 vector harboring the ZAP70 gene. Thymus structure, immunophenotyping, T-cell receptor clonotypes, T-cell function, immune responses to transgenes and autoantibodies, vector copy number, and integration were evaluated.

RESULTS

AAV8, AAV9, and AAV10 serotypes all transduced thymocyte subsets after in situ gene transfer, with transduction of up to 5% of cells. Intrathymic injection of an AAV8-ZAP-70 vector into ZAP-70^-/- mice resulted in a rapid thymocyte differentiation associated with the development of a thymic medulla. Strikingly, medullary thymic epithelial cells expressing the autoimmune regulator were detected within 10 days of gene transfer, correlating with the presence of functional effector and regulatory T-cell subsets with diverse T-cell receptor clonotypes in the periphery. Although thymocyte reconstitution was transient, gene-corrected peripheral T cells harboring approximately 1 AAV genome per cell persisted for more than 40 weeks, and AAV vector integration was detected.

CONCLUSIONS

Intrathymic AAV-transduced progenitors promote a rapid restoration of the thymic architecture, with a single wave of thymopoiesis generating long-term peripheral T-cell function.

The natural history of naive T cells from birth to maturity

Generating and maintaining a diverse repertoire of naive T cells is essential for protection against pathogens, and developing a mechanistic and quantitative description of the processes involved lies at the heart of our understanding of vertebrate immunity. Here, we review the biology of naive T cells from birth to maturity and outline how the integration of mathematical models and experiments has helped us to develop a full picture of their life histories.

Modeling cell population dynamics

 Quantitative modeling is quickly becoming an integral part of biology, due to the ability of mathematical models and computer simulations to generate insights and predict the behavior of living systems. Single-cell models can be incapable or misleading for inferring population dynamics, as they do not consider the interactions between cells via metabolites or physical contact, nor do they consider competition for limited resources such as nutrients or space. Here we examine methods that are commonly used to model and simulate cell populations. First, we cover simple models where analytic solutions are available, and then move on to more complex scenarios where computational methods are required. Overall, we present a summary of mathematical models used to describe cell population dynamics, which may aid future model development and highlights the importance of population modeling in biology.

Modeling cell population dynamics

 Quantitative modeling is quickly becoming an integral part of biology, due to the ability of mathematical models and computer simulations to generate insights and predict the behavior of living systems. Single-cell models can be incapable or misleading for inferring population dynamics, as they do not consider the interactions between cells via metabolites or physical contact, nor do they consider competition for limited resources such as nutrients or space. Here we examine methods that are commonly used to model and simulate cell populations. First, we cover simple models where analytic solutions are available, and then move on to more complex scenarios where computational methods are required. Overall, we present a summary of mathematical models used to describe cell population dynamics, which may aid future model development and highlights the importance of population modeling in biology.

Four domains: The fundamental unicell and Post-Darwinian Cognition-Based Evolution

Contemporary research supports the viewpoint that self-referential cognition is the proper definition of life. From that initiating platform, a cohesive alternative evolutionary narrative distinct from standard Neodarwinism can be presented. Cognition-Based Evolution contends that biological variation is a product of a self-reinforcing information cycle that derives from self-referential attachment to biological information space-time with its attendant ambiguities. That information cycle is embodied through obligatory linkages among energy, biological information, and communication. Successive reiterations of the information cycle enact the informational architectures of the basic unicellular forms. From that base, inter-domain and cell-cell communications enable genetic and cellular variations through self-referential natural informational engineering and cellular niche construction. Holobionts are the exclusive endpoints of that self-referential cellular engineering as obligatory multicellular combinations of the essential Four Domains: Prokaryota, Archaea, Eukaryota and the Virome. Therefore, it is advocated that these Four Domains represent the perpetual object of the living circumstance rather than the visible macroorganic forms. In consequence, biology and its evolutionary development can be appraised as the continual defense of instantiated cellular self-reference. As the survival of cells is as dependent upon limitations and boundaries as upon any freedom of action, it is proposed that selection represents only one of many forms of cellular constraint that sustain self-referential integrity.

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

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.

The body maintains large populations of naive T cells, a type of white blood cell that is able to respond specifically to pathogens. This arsenal is essential for our capacity to fight novel infections throughout our lifespan, and their numbers remain quite stable despite a gradual decline in the production of new naive T cells as we age. However, the mechanisms that underlie this stability are not well understood. In this study, we address this problem by testing a variety of potential mechanisms, each framed as a mathematical model, against multiple datasets obtained from experiments performed in mice. Our analysis supports a mechanism by which naïve T cells gradually increase their ability to survive the longer they reside in the circulation. Paradoxically, however, naïve T cells may also lose their ability to respond effectively to infections as they age. Together, these processes may drive the accumulation of older, functionally impaired T cells, potentially at the expense of younger and more immunologically potent cells, as we age.

Modelling T cell proliferation: Dynamics heterogeneity depending on cell differentiation, age, and genetic background

Cell proliferation is the common characteristic of all biological systems. The immune system insures the maintenance of body integrity on the basis of a continuous production of diversified T lymphocytes in the thymus. This involves processes of proliferation, differentiation, selection, death and migration of lymphocytes to peripheral tissues, where proliferation also occurs upon antigen recognition. Quantification of cell proliferation dynamics requires specific experimental methods and mathematical modelling. Here, we assess the impact of genetics and aging on the immune system by investigating the dynamics of proliferation of T lymphocytes across their differentiation through thymus and spleen in mice. Our investigation is based on single-cell multicolour flow cytometry analysis revealing the active incorporation of a thymidine analogue during S phase after pulse-chase-pulse experiments in vivo, versus cell DNA content. A generic mathematical model of state transition simulates through Ordinary Differential Equations (ODEs) the evolution of single cell behaviour during various durations of labelling. It allows us to fit our data, to deduce proliferation rates and estimate cell cycle durations in sub-populations. Our model is simple and flexible and is validated with other durations of pulse/chase experiments. Our results reveal that T cell proliferation is highly heterogeneous but with a specific “signature” that depends upon genetic origins, is specific to cell differentiation stages in thymus and spleen and is altered with age. In conclusion, our model allows us to infer proliferation rates and cell cycle phase durations from complex experimental 5-ethynyl-2'-deoxyuridine (EdU) data, revealing T cell proliferation heterogeneity and specific signatures.

We assess the impact of genetics and aging on immune system dynamics by investigating the dynamics of proliferation of T lymphocytes across their differentiation through thymus and spleen in mice. Understanding cell proliferation dynamics requires specific experimental methods and mathematical modelling. Our investigation is based upon single-cell multicolour flow cytometry analysis thereby revealing the active incorporation in DNA of a thymidine analogue during S phase after pulse-chase experiments in vivo, versus cell DNA content. A generic mathematical model that simulates the evolution of single cell behaviour during the experiment allows us to fit our data, to deduce proliferation rates and mean cell cycle phase durations in sub-populations. This reveals that T cell proliferation is constrained by genetic influences, declines with age, and is specific to cell differentiation stage, revealing a specific “signature” of cell proliferation. Our model is simple and flexible and can be used with other pulse/chase experiments.

Identifying T Cell Receptors from High-Throughput Sequencing: Dealing with Promiscuity in TCRα and TCRβ Pairing

Characterisation of the T cell receptors (TCR) involved in immune responses is important for the design of vaccines and immunotherapies for cancer and autoimmune disease. The specificity of the interaction between the TCR heterodimer and its peptide-MHC ligand derives largely from the juxtaposed hypervariable CDR3 regions on the TCRα and TCRβ chains, and obtaining the paired sequences of these regions is a standard for functionally defining the TCR. A brute force approach to identifying the TCRs in a population of T cells is to use high-throughput single-cell sequencing, but currently this process remains costly and risks missing small clones. Alternatively, CDR3α and CDR3β sequences can be associated using their frequency of co-occurrence in independent samples, but this approach can be confounded by the sharing of CDR3α and CDR3β across clones, commonly observed within epitope-specific T cell populations. The accurate, exhaustive, and economical recovery of TCR sequences from such populations therefore remains a challenging problem. Here we describe an algorithm for performing frequency-based pairing (alphabetr) that accommodates CDR3α- and CDR3β-sharing, cells expressing two TCRα chains, and multiple forms of sequencing error. The algorithm also yields accurate estimates of clonal frequencies.

Limiting Thymic Precursor Supply Increases the Risk of Lymphoid Malignancy in Murine X-Linked Severe Combined Immunodeficiency

In early gene therapy trials for SCID-X1, using γ-retroviral vectors, T cell leukemias developed in a subset of patients secondary to insertional proto-oncogene activation. In contrast, we have reported development of T cell leukemias in SCID-X1 mice following lentivirus-mediated gene therapy independent of insertional mutagenesis. A distinguishing feature in our study was that only a proportion of transplanted γc-deficient progenitors were transduced and therefore competent for reconstitution. We hypothesized that reconstitution of SCID-X1 mice with limiting numbers of hematopoietic progenitors might be a risk factor for lymphoid malignancy. To test this hypothesis, in the absence of transduction, SCID-X1 mice were reconstituted with serially fewer wild-type hematopoietic progenitors. A robust inverse correlation between hematopoietic progenitor cell dose and T-lymphoid malignancy was observed, with earlier disease onset at lower cell doses. Malignancies were of donor origin and carried activating Notch1 mutations. These findings align with emerging evidence that thymocyte self-renewal induced by progenitor deprivation carries an oncogenic risk that is modulated by intra-thymic competition from differentiation-committed cells. Although insertional proto-oncogene activation is required for the development of malignancy in humans, failure of γc-deficient thymocytes to effectively compete with this at-risk cell population may have also contributed to oncogenesis observed in early SCID-X1 trials.

Modeling the development of the post-natal mouse thymus in the absence of bone marrow progenitors

Many mathematical models have been published with the purpose of explaining aspects of T-cell development in the thymus. In this manuscript we adapted a four-compartment model of the thymus and used a range of mathematical approaches with the aim of explaining the dynamics of the four main thymocyte populations in the mouse thymus, from the emergence of the first fetal thymocyte until the death of the animal. At various pre-natal and post-natal stages we investigated experimentally the number and composition of thymocytes populations, their apoptosis and proliferation, along with data from literature, to create and validate the model. In our model the proliferation processes are characterized by decreasing proliferation rates, which allows us to model the natural involution of the thymus. The best results were obtained when different sets of parameters were used for the fetal and post-natal periods, suggesting that birth may induce a discontinuity in the modeled processes. Our model is able to model the development of both pre-natal and post-natal thymocyte populations. Also, our findings showed that the post-natal thymus is able to develop in the absence of the daily input of bone marrow progenitors, providing more evidence to support the autonomous development of the post-natal thymus.

Population pharmacokinetic modeling of Thymoglobulin(®) in children receiving allogeneic-hematopoietic cell transplantation: towards improved survival through individualized dosing

BACKGROUND AND OBJECTIVES

To prevent graft-versus-host disease and rejection in hematopoietic cell transplantation (HCT), children receive Thymoglobulin(®), a polyclonal antibody acting mainly by depleting T cells. The therapeutic window is critical as over-exposure may result in delayed immune reconstitution of donor T cells. In this study, we describe the population pharmacokinetics of Thymoglobulin(®) as a first step towards an evidence-based dosing regimen of Thymoglobulin(®) in pediatric HCT.

METHODS

Serum active Thymoglobulin(®) concentrations were measured in all pediatric HCTs performed between 2004 and 2012 in two pediatric HCT centers in The Netherlands. Population pharmacokinetic analysis was performed using NONMEM(®) version 7.2.

RESULTS

A total of 3,113 concentration samples from 280 pediatric HCTs were analyzed, with age ranging from 3 months to 23 years old. The cumulative Thymoglobulin(®) dose was 10 mg/kg in 94 % of the patients given in 4 consecutive days. A model incorporating parallel linear and concentration-dependent clearance of Thymoglobulin(®) was identified. Body weight [for linear clearance (CL) and central volume of distribution] as well as lymphocyte count pre-Thymoglobulin(®) infusion (for CL) were important covariates. As such, the current dosing regimen results in higher exposure in children with a higher bodyweight and/or a lower lymphocyte count pre-Thymoglobulin(®) infusion.

CONCLUSION

This model can be used to develop an individual dosing regimen for Thymoglobulin(®), based on both body weight and lymphocyte counts, once the therapeutic window has been determined. This individualized regimen may contribute to a better immune reconstitution and thus outcome of allogeneic HCT.

How many TCR clonotypes does a body maintain?

We consider the lifetime of a T cell clonotype, the set of T cells with the same T cell receptor, from its thymic origin to its extinction in a multiclonal repertoire. Using published estimates of total cell numbers and thymic production rates, we calculate the mean number of cells per TCR clonotype, and the total number of clonotypes, in mice and humans. When there is little peripheral division, as in a mouse, the number of cells per clonotype is small and governed by the number of cells with identical TCR that exit the thymus. In humans, peripheral division is important and a clonotype may survive for decades, during which it expands to comprise many cells. We therefore devise and analyse a computational model of homeostasis of a multiclonal population. Each T cell in the model competes for self pMHC stimuli, cells of any one clonotype only recognising a small fraction of the many subsets of stimuli. A constant mean total number of cells is maintained by a balance between cell division and death, and a stable number of clonotypes by a balance between thymic production of new clonotypes and extinction of existing ones. The number of distinct clonotypes in a human body may be smaller than the total number of naive T cells by only one order of magnitude.

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The number of T cells of one clonotype is an integer.

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The history of a clonotype starts with release from the thymus, and ends with extinction.

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Competition and cross-reactivity are included in a natural way.

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The average number of cells per clonotype, in a human body, is only of order 10.

Positive and Negative Regulatory Mechanisms for Fine-Tuning Cellularity and Functions of Medullary Thymic Epithelial Cells

Self-tolerant T cells and regulatory T cells develop in the thymus. A wide variety of cell-cell interactions in the thymus is required for the differentiation, proliferation, and repertoire selection of T cells. Various secreted and cell surface molecules expressed in thymic epithelial cells (TECs) mediate these processes. Moreover, cytokines expressed by cells of hematopoietic origin regulate the cellularity of TECs. Tumor necrosis factor (TNF) family RANK ligand, lymphotoxin, and CD40 ligand, expressed in T cells and innate lymphoid cells (ILCs), promote the differentiation and proliferation of medullary TECs (mTECs) that play critical roles in the induction of immune tolerance. A recent study suggests that interleukin-22 (IL-22) produced by ILCs promotes regeneration of TECs after irradiation. Intriguingly, tumor growth factor-β and osteoprotegerin limit cellularity of mTECs, thereby attenuating regulatory T cell generation. We will review recent insights into the molecular basis for cell-cell interactions regulating differentiation and proliferation of mTECs and also discuss about a perspective on use of mathematical models for understanding this complicated system.

A Stochastic Model for CD4+ T Cell Proliferation and Dissemination Network in Primary Immune Response

The study of the initial phase of the adaptive immune response after first antigen encounter provides essential information on the magnitude and quality of the immune response. This phase is characterized by proliferation and dissemination of T cells in the lymphoid organs. Modeling and identifying the key features of this phenomenon may provide a useful tool for the analysis and prediction of the effects of immunization. This knowledge can be effectively exploited in vaccinology, where it is of interest to evaluate and compare the responses to different vaccine formulations. The objective of this paper is to construct a stochastic model based on branching process theory, for the dissemination network of antigen-specific CD4+ T cells. The devised model is validated on in vivo animal experimental data. The model presented has been applied to the vaccine immunization context making references to simple proliferation laws that take into account division, death and quiescence, but it can also be applied to any context where it is of interest to study the dynamic evolution of a population.

Modeling the T cell immune response: a fascinating challenge

The immune system is designed to protect the organism from infection and to repair damaged tissue. An effective response requires recognition of the threat, the appropriate effector mechanism to clear the pathogen and a return to homeostasis with minimal damage to self-tissues. T cells play a central role in orchestrating the immune response at all stages of the response and have been the subject of intense study by both experimental immunologists and modelers. This review examines some of the more critical questions in T cell biology and describes the latest attempts to address those questions using approaches that combine mathematical modeling and experiments.

From pre-DP, post-DP, SP4, and SP8 Thymocyte Cell Counts to a Dynamical Model of Cortical and Medullary Selection

Cells of the mature αβ T cell repertoire arise from the development in the thymus of bone marrow precursors (thymocytes). αβ T cell maturation is characterized by the expression of thousands of copies of identical αβ T cell receptors and the CD4 and/or CD8 co-receptors on the surface of thymocytes. The maturation stages of a thymocyte are: (1) double negative (DN) (TCR⁻, CD4⁻ and CD8⁻), (2) double positive (DP) (TCR⁺, CD4⁺ and CD8⁺), and (3) single positive (SP) (TCR⁺, CD4⁺ or CD8⁺). Thymic antigen presenting cells provide the appropriate micro-architecture for the maturation of thymocytes, which “sense” the signaling environment via their randomly generated TCRs. Thymic development is characterized by (i) an extremely low success rate, and (ii) the selection of a functional and self-tolerant T cell repertoire. In this paper, we combine recent experimental data and mathematical modeling to study the selection events that take place in the thymus after the DN stage. The stable steady state of the model for the pre-DP, post-DP, and SP populations is identified with the experimentally measured cell counts from 5.5- to 17-week-old mice. We make use of residence times in the cortex and the medulla for the different populations, as well as recently reported asymmetric death rates for CD4 and CD8 SP thymocytes. We estimate that 65.8% of pre-DP thymocytes undergo death by neglect. In the post-DP compartment, 91.7% undergo death by negative selection, 4.7% become CD4 SP, and 3.6% become CD8 SP. Death by negative selection in the medulla removes 8.6% of CD4 SP and 32.1% of CD8 SP thymocytes. Approximately 46.3% of CD4 SP and 27% of CD8 SP thymocytes divide before dying or exiting the thymus.

Theories and quantification of thymic selection

The peripheral T cell repertoire is sculpted from prototypic T cells in the thymus bearing randomly generated T cell receptors (TCR) and by a series of developmental and selection steps that remove cells that are unresponsive or overly reactive to self-peptide–MHC complexes. The challenge of understanding how the kinetics of T cell development and the statistics of the selection processes combine to provide a diverse but self-tolerant T cell repertoire has invited quantitative modeling approaches, which are reviewed here.

Insights into the mechanisms of thymus involution and regeneration by modeling the glucocorticoid-induced perturbation of thymocyte populations dynamics

T-cells develop in the thymus and based on CD4 and CD8 expressions there are four main thymocyte populations in a normal mouse thymus. Currently, there are several mathematical models that describe the dynamics of thymocyte populations in a normal thymus, but only a few of them model the transient perturbation of their homeostasis. Our aim is to model the perturbation in the dynamics of each thymocyte population which is induced by the administration of a glucocorticoid, i.e. dexamethasone. The proposed approach relies on extending a four compartment thymus model based on differential equations by adding perturbation terms either globally (at the level of each equation) or locally (at the level of proliferation, death, and transfer rates). By fitting the perturbed model with experimental data on mice thymi collected before and after the administration of dexamethasone, it was possible to estimate the relevant parameters using a population-based stochastic search method. The fitted model is further used to conduct a quantitative analysis on the differentiated impact of dexamethasone on each T-cell population and on proliferation, death, and transfer processes. The obtained quantitative information on the perturbation could be used to explore and modify the flow of thymocytes between thymus compartments in order to elucidate the mechanisms of thymus involution and its subsequent regeneration. Since glucocorticoids are raised in many pathological situations, such a model could be useful in evaluating the impact of diseases on thymocyte dynamics in the thymus.

Mathematical Model of Naive T Cell Division and Survival IL-7 Thresholds

We develop a mathematical model of the peripheral naive T cell population to study the change in human naive T cell numbers from birth to adulthood, incorporating thymic output and the availability of interleukin-7 (IL-7). The model is formulated as three ordinary differential equations: two describe T cell numbers, in a resting state and progressing through the cell cycle. The third is introduced to describe changes in IL-7 availability. Thymic output is a decreasing function of time, representative of the thymic atrophy observed in aging humans. Each T cell is assumed to possess two interleukin-7 receptor (IL-7R) signaling thresholds: a survival threshold and a second, higher, proliferation threshold. If the IL-7R signaling strength is below its survival threshold, a cell may undergo apoptosis. When the signaling strength is above the survival threshold, but below the proliferation threshold, the cell survives but does not divide. Signaling strength above the proliferation threshold enables entry into cell cycle. Assuming that individual cell thresholds are log-normally distributed, we derive population-average rates for apoptosis and entry into cell cycle. We have analyzed the adiabatic change in homeostasis as thymic output decreases. With a parameter set representative of a healthy individual, the model predicts a unique equilibrium number of T cells. In a parameter range representative of persistent viral or bacterial infection, where naive T cell cycle progression is impaired, a decrease in thymic output may result in the collapse of the naive T cell repertoire.

Restoration of CMV-specific-CD4 T cells with ART occurs early and is greater in those with more advanced immunodeficiency

Objectives

Restoration of Cytomegalovirus-specific-CD4 T cell (CMV-Sp-CD4) responses partly accounts for the reduction of CMV-disease with antiretroviral-therapy (ART), but CMV-Sp-CD4 may also drive immune activation and immunosenescence. This study characterized the dynamics of CMV-Sp-CD4 after ART initiation and explored associations with CD4 T cell recovery as well as frequency of naïve CD4 T cells at week 96.

Methods

Fifty HIV-infected, ART-naïve Thai adults with CD4 T cell count ≤350cells/µL and starting ART were evaluated over 96 weeks (ClinicalTrials.gov identifier NCT01296373). CMV-Sp-CD4 was detected by co-expression of CD25/CD134 by flow cytometry after CMV-antigen stimulation.

Results

All subjects were CMV sero-positive, 4 had quantifiable CMV-DNA (range 2.3-3.9 log₁₀ copies/mL) at baseline but none had clinically apparent CMV-disease. Baseline CMV-Sp-CD4 response was positive in 40 subjects. Those with CD4 T cell count <100cells/µL were less likely to have positive baseline CMV-Sp-CD4 response (P=0.003). Positive baseline CMV-Sp-CD4 response was associated with reduced odds of quantifiable CMV-DNA (P=0.022). Mean CD4 T cell increase at week 96 was 213 cells/µL. This was associated positively with baseline HIV-VL (P=0.001) and negatively with age (P=0.003). The frequency of CMV-Sp-CD4 increased at week 4 (P=0.008), then declined. Those with lower baseline CMV-Sp-CD4 (P=0.009) or CDC category C (P<0.001) had greater increases in CMV-Sp-CD4 at week 4. At week 96, CD4 T cell count was positively (P<0.001) and the frequency of CMV-Sp-CD4 was negatively (P=0.001) associated with the percentage of naïve CD4 T cells.

Conclusions

Increases in CMV-Sp-CD4 with ART occurred early and were greater in those with more advanced immunodeficiency. The frequency of CMV-Sp-CD4 was associated with reduced naïve CD4 T cells, a marker associated with immunosenescence.

Asymmetric thymocyte death underlies the CD4:CD8 T-cell ratio in the adaptive immune system

It has long been recognized that the T-cell compartment has more CD4 helper than CD8 cytotoxic T cells, and this is most evident looking at T-cell development in the thymus. However, it remains unknown how thymocyte development so favors CD4 lineage development. To identify the basis of this asymmetry, we analyzed development of synchronized cohorts of thymocytes in vivo and estimated rates of thymocyte death and differentiation throughout development, inferring lineage-specific efficiencies of selection. Our analysis suggested that roughly equal numbers of cells of each lineage enter selection and found that, overall, a remarkable ∼75% of cells that start selection fail to complete the process. Importantly it revealed that class I-restricted thymocytes are specifically susceptible to apoptosis at the earliest stage of selection. The importance of differential apoptosis was confirmed by placing thymocytes under apoptotic stress, resulting in preferential death of class I-restricted thymocytes. Thus, asymmetric death during selection is the key determinant of the CD4:CD8 ratio in which T cells are generated by thymopoiesis.

Computational modelling of T-cell formation kinetics: output regulated by initial proliferation-linked deferral of developmental competence

Bone-marrow-derived progenitors must continually enter the thymus of an adult mouse to sustain T-cell homeostasis, yet only a few input cells per day are sufficient to support a yield of 5 × 10(7) immature T-cells per day and an eventual output of 1-2 × 10(6) mature cells per day. While substantial progress has been made to delineate the developmental pathway of T-cell lineage commitment, still little is known about the relationship between differentiation competence and the remarkable expansion of the earliest (DN1 stage) T-cell progenitors. To address this question, we developed computational models where the probability to progress to the next stage (DN2) is related to division number. To satisfy differentiation kinetics and overall cell yield data, our models require that adult DN1 cells divide multiple times before becoming competent to progress into DN2 stage. Our findings were subsequently tested by in vitro experiments, where putative early and later-stage DN1 progenitors from the thymus were purified and their progression into DN2 was measured. These experiments showed that the two DN1 sub-populations divided with similar rates, but progressed to the DN2 stage with different rates, thus providing experimental evidence that DN1 cells increase their commitment probability in a cell-intrinsic manner as they undergo cell division. Proliferation-linked shifts in eligibility of DN1 cells to undergo specification thus control kinetics of T-cell generation.

Dynamical modelling of haematopoiesis: an integrated view over the system in homeostasis and under perturbation

A very high number of different types of blood cells must be generated daily through a process called haematopoiesis in order to meet the physiological requirements of the organism. All blood cells originate from a population of relatively few haematopoietic stem cells residing in the bone marrow, which give rise to specific progenitors through different lineages. Steady-state dynamics are governed by cell division and commitment rates as well as by population sizes, while feedback components guarantee the restoration of steady-state conditions. In this study, all parameters governing these processes were estimated in a computational model to describe the haematopoietic hierarchy in adult mice. The model consisted of ordinary differential equations and included negative feedback regulation. A combination of literature data, a novel divide et impera approach for steady-state calculations and stochastic optimization allowed one to reduce possible configurations of the system. The model was able to recapitulate the fundamental steady-state features of haematopoiesis and simulate the re-establishment of steady-state conditions after haemorrhage and bone marrow transplantation. This computational approach to the haematopoietic system is novel and provides insight into the dynamics and the nature of possible solutions, with potential applications in both fundamental and clinical research.

Toward quantifying the thymic dysfunctional state in mouse models of inflammatory bowel disease

Inflammatory bowel disease is characterized by a number of immunological alterations, not the least in the T-cell compartment. Numerous animal models of colitis have revealed aberrant thymocyte dynamics associated with skewed thymocyte development. The recent advancements in quantitative methods have proposed critical kinetic alterations in the thymocyte development during the progression of colitis. This review focuses on the aberrant thymocyte dynamics in Gαi2-deficient mice as this mouse model provides most quantitative data of the thymocyte development associated with colitis. Herein, we discuss several dynamic changes during the progression of colitis and propose a hypothesis for the underlying causes for the skewed proportions of the thymocyte populations seen in the Gαi2-deficient mice and in other mouse models of colitis.

State-transition diagrams for biologists

It is clearly in the tradition of biologists to conceptualize the dynamical evolution of biological systems in terms of state-transitions of biological objects. This paper is mainly concerned with (but obviously not limited too) the immunological branch of biology and shows how the adoption of UML (Unified Modeling Language) state-transition diagrams can ease the modeling, the understanding, the coding, the manipulation or the documentation of population-based immune software model generally defined as a set of ordinary differential equations (ODE), describing the evolution in time of populations of various biological objects. Moreover, that same UML adoption naturally entails a far from negligible representational economy since one graphical item of the diagram might have to be repeated in various places of the mathematical model. First, the main graphical elements of the UML state-transition diagram and how they can be mapped onto a corresponding ODE mathematical model are presented. Then, two already published immune models of thymocyte behavior and time evolution in the thymus, the first one originally conceived as an ODE population-based model whereas the second one as an agent-based one, are refactored and expressed in a state-transition form so as to make them much easier to understand and their respective code easier to access, to modify and run. As an illustrative proof, for any immunologist, it should be possible to understand faithfully enough what the two software models are supposed to reproduce and how they execute with no need to plunge into the Java or Fortran lines.

A quantitative study of the mechanisms behind thymic atrophy in Gαi2-deficient mice during colitis development

Mice deficient for the G protein subunit Gαi2 spontaneously develop colitis, a chronic inflammatory disease associated with dysregulated T cell responses. We and others have previously demonstrated a thymic involution in these mice and an aberrant thymocyte dynamics. The Gαi2(-/-) mice have a dramatically reduced fraction of double positive thymocytes and an increased fraction of single positive (SP) thymocytes. In this study, we quantify a number of critical parameters in order to narrow down the underlying mechanisms that cause the dynamical changes of the thymocyte development in the Gαi2(-/-) mice. Our data suggest that the increased fraction of SP thymocytes results only from a decreased number of DP thymocytes, since the number of SP thymocytes in the Gαi2(-/-) mice is comparable to the control littermates. By measuring the frequency of T cell receptor excision circles (TRECs) in the thymocytes, we demonstrate that the number of cell divisions the Gαi2(-/-) SP thymocytes undergo is comparable to SP thymocytes from control littermates. In addition, our data show that the mature SP CD4(+) and CD8(+) thymocytes divide to the same extent before they egress from the thymus. By estimating the number of peripheral TREC(+) T lymphocytes and their death rate, we could calculate the daily egression of thymocytes. Gαi2(-/-) mice with no/mild and moderate colitis were found to have a slower export rate in comparison to the control littermates. The quantitative measurements in this study suggest a number of dynamical changes in the thymocyte development during the progression of colitis.

Modeling the role of IL2 in the interplay between CD4+ helper and regulatory T cells: studying the impact of IL2 modulation therapies

Several reports in the literature have drawn a complex picture of the effect of treatments aiming to modulate IL2 activity in vivo. They seem to promote indistinctly immunity or tolerance, probably depending on the specific context, dose and timing of their application. Such complexity might derives from the dual role of IL2 on T-cell dynamics. To theoretically address the latter possibility, we develop a mathematical model for helper, regulatory and memory T-cells dynamics, which account for most well-known facts relative to their relationship with IL2. We simulate the effect of three types of therapies: IL2 injections, IL2 depletion using anti-IL2 antibodies and IL2/anti-IL2 immune complexes injection. We focus in the qualitative and quantitative conditions of dose and timing for these treatments which allow them to potentate either immunity or tolerance. Our results provide reasonable explanations for the existent pre-clinical and clinical data and further provide interesting practical guidelines to optimize the future application of these types of treatments. Particularly, our results predict that: (i) Immune complexes IL2/anti-IL2 mAbs, using mAbs which block the interaction of IL2 and CD25 (the alpha chain of IL2 receptor), is the best option to potentate immunity alone or in combination with vaccines. These complexes are optimal when a 1:2 molar ratio of mAb:IL2 is used and the mAbs have the largest possible affinity; (ii) Immune complexes IL2/anti-IL2 mAbs, using mAbs which block the interaction of IL2 and CD122 (the beta chain of IL2 receptor), are the best option to reinforce preexistent natural tolerance, for instance to prevent allograft rejection. These complexes are optimal when a 1:2 molar ratio of mAb:IL2 is used and the mAbs have intermediate affinities; (iii) mAbs anti-IL2 can be successfully used alone to treat an ongoing autoimmune disorder, promoting the re-induction of tolerance. The best strategy in this therapy is to start treatment with an initially high dose of the mAbs (one capable to induce some immune suppression) and then scales down slowly the dose of mAb in subsequent applications.

Computational models of HIV-1 resistance to gene therapy elucidate therapy design principles

Gene therapy is an emerging alternative to conventional anti-HIV-1 drugs, and can potentially control the virus while alleviating major limitations of current approaches. Yet, HIV-1's ability to rapidly acquire mutations and escape therapy presents a critical challenge to any novel treatment paradigm. Viral escape is thus a key consideration in the design of any gene-based technique. We develop a computational model of HIV's evolutionary dynamics in vivo in the presence of a genetic therapy to explore the impact of therapy parameters and strategies on the development of resistance. Our model is generic and captures the properties of a broad class of gene-based agents that inhibit early stages of the viral life cycle. We highlight the differences in viral resistance dynamics between gene and standard antiretroviral therapies, and identify key factors that impact long-term viral suppression. In particular, we underscore the importance of mutationally-induced viral fitness losses in cells that are not genetically modified, as these can severely constrain the replication of resistant virus. We also propose and investigate a novel treatment strategy that leverages upon gene therapy's unique capacity to deliver different genes to distinct cell populations, and we find that such a strategy can dramatically improve efficacy when used judiciously within a certain parametric regime. Finally, we revisit a previously-suggested idea of improving clinical outcomes by boosting the proliferation of the genetically-modified cells, but we find that such an approach has mixed effects on resistance dynamics. Our results provide insights into the short- and long-term effects of gene therapy and the role of its key properties in the evolution of resistance, which can serve as guidelines for the choice and optimization of effective therapeutic agents.

A primary obstacle to the success of any anti-HIV treatment is HIV's ability to rapidly resist it by generating new viral strains whose vulnerability to the treatment is reduced. Gene therapies represent a novel class of treatments for HIV infection that may supplement or replace present therapies, as they alleviate some of their major shortcomings. The design of gene therapeutic agents that effectively reduce viral resistance can be aided by a quantitative elucidation of the processes by which resistance is acquired following therapy initiation. We developed a computational model that describes a patient's response to therapy and used it to quantify the influence of therapy parameters and strategies on the development of viral resistance. We find that gene therapy induces different clinical conditions and a much slower viral response than present therapies. These dictate different design principles such as a greater significance to the virus' competence in the absence of therapy. We also show that one can effectively delay emergence of resistance by delivering distinct therapeutic genes into separate cell populations. Our results highlight the differences between traditional and gene therapies and provide a basic understanding of how key controllable parameters and strategies affect resistance development.

A cellular automata-based mathematical model for thymocyte development

Intrathymic T cell development is an important process necessary for the normal formation of cell-mediated immune responses. Importantly, such a process depends on interactions of developing thymocytes with cellular and extracellular elements of the thymic microenvironment. Additionally, it includes a series of oriented and tunely regulated migration events, ultimately allowing mature cells to cross endothelial barriers and leave the organ. Herein we built a cellular automata-based mathematical model for thymocyte migration and development. The rules comprised in this model take into account the main stages of thymocyte development, two-dimensional sections of the normal thymic microenvironmental network, as well as the chemokines involved in intrathymic cell migration. Parameters of our computer simulations with further adjusted to results derived from previous experimental data using sub-lethally irradiated mice, in which thymus recovery can be evaluated. The model fitted with the increasing numbers of each CD4/CD8-defined thymocyte subset. It was further validated since it fitted with the times of permanence experimentally ascertained in each CD4/CD8-defined differentiation stage. Importantly, correlations using the whole mean volume of young normal adult mice revealed that the numbers of cells generated in silico with the mathematical model fall within the range of total thymocyte numbers seen in these animals. Furthermore, simulations made with a human thymic epithelial network using the same mathematical model generated similar profiles for temporal evolution of thymocyte developmental stages. Lastly, we provided in silico evidence that the thymus architecture is important in the thymocyte development, since changes in the epithelial network result in different theoretical profiles for T cell development/migration. This model likely can be used to predict thymocyte evolution following therapeutic strategies designed for recovery of the thymus in diseases coursing with thymus involution, such as some primary immunodeficiencies, acute infections, and malnutrition.

Why does the thymus involute? A selection-based hypothesis

Thymic involution remains a fundamental mystery in immunology. Here we present an argument that this seemingly counterproductive behavior may have evolved to allow for peripheral selection of a T-cell repertoire during young-adult life, optimized for fighting infections and avoiding reaction to self. Age-associated decline in immune function may be viewed as an unfortunate side effect of this selective process. Thus, the key to understanding thymic involution might lie in a more quantitative understanding of T-cell homeostasis in the periphery.

Immune reconstitution after cord blood transplants supported by coinfusion of mobilized hematopoietic stem cells from a third party donor

Low severity of GVHD, substantial graft vs tumor (GVT) and slow development of protective immunity are well-documented features of cord blood transplants (CBT). We have evaluated the immune reconstitution of adult recipients of single-unit CBT supported by the coinfusion of third party donor (TPD) mobilized hematopoietic stem cells (MHSC), a procedure-'dual CB/TPD-MHSC transplant'-that results in early recovery of circulating granulocytes, high rates of CB engraftment and full chimerism. Cumulative recovery of natural killer and B cells at or above the median values of normal controls were 1.0 and 0.76 by the sixth and ninth months. Recovery of T cells was much slower, naive cells lagging behind those of memory and effector (committed) immunophenotypes. Serial analyses of signal joint TCR excision circles showed a general pattern of very low levels by the third month after CBT, followed by recovery to levels persistently similar or higher than those observed before transplantation and in normal controls. Our results are consistent with the clinical observations of substantial GVT effect together with low incidence of serious GVHD and slow development of protective immunity and suggest that thymic function contributes substantially to the recovery of T-cell populations in adults receiving dual CB/TPD-MHSC transplants.

Quantifying the development of the peripheral naive CD4+ T-cell pool in humans

What are the rules that govern a naive T cell's prospects for survival or division after export from the thymus into the periphery? To help address these questions, we combine data from existing studies with robust mathematical models to estimate the absolute contributions of thymopoiesis, peripheral division, and loss or differentiation to the human naive CD4+ T-cell pool between the ages of 0 and 20 years. Despite their decline in frequency in the blood, total body numbers of naive CD4+ T cells increase throughout childhood and early adulthood. Our analysis shows that postthymic proliferation contributes more than double the number of cells entering the pool each day from the thymus. This ratio is preserved with age; as the thymus involutes, the average time between naive T-cell divisions in the periphery lengthens. We also show that the expected residence time of naive T cells increases with time. The naive CD4+ T-cell population thus becomes progressively less dynamic with age. Together with other studies, our results suggest a complex picture of naive T-cell homeostasis in which population size, time since export from the thymus, or time since the last division can influence a cell's prospects for survival or further divisions.

Efficient intrathymic gene transfer following in situ administration of a rAAV serotype 8 vector in mice and nonhuman primates

The thymus is the primary site of T-cell development and plays a key role in the induction of self-tolerance. We previously showed that the intrathymic (i.t.) injection of a transgene-expressing lentiviral vector (LV) in mice can result in the correction of a T cell-specific genetic defect. Nevertheless, the efficiency of thymocyte transduction did not exceed 0.1-0.3% and we were unable to detect any thymus transduction in macaques. As such, we initiated studies to assess the capacity of recombinant adeno-associated virus (rAAV) vectors to transduce murine and primate thymic cells. In vivo administration of AAV serotype 2-derived single-stranded AAV (ssAAV) and self-complementary AAV (scAAV) vectors pseudotyped with capsid proteins of serotypes 1, 2, 4, 5, and 8 demonstrated that murine thymus transduction was significantly enhanced by scAAV2/8. Transgene expression was detected in 5% of thymocytes and, notably, transduced cells represented 1% of peripheral T lymphocytes. Moreover, i.t. administration of scAAV2/8 particles in macaques, by endoscopic-mediated guidance, resulted in significant gene transfer. Thus, in healthy animals, where thymic gene transfer does not provide a selective advantage, scAAV2/8 is a unique tool promoting the in situ transduction of thymocytes with the subsequent export of gene-modified lymphocytes to the periphery.