Modeling T- and B-cell growth and differentiation

Distribution modeling quantifies collective TH cell decision circuits in chronic inflammation

Immune responses are tightly regulated by a diverse set of interacting immune cell populations. Alongside decision-making processes such as differentiation into specific effector cell types, immune cells initiate proliferation at the beginning of an inflammation, forming two layers of complexity. Here, we developed a general mathematical framework for the data-driven analysis of collective immune cell dynamics. We identified qualitative and quantitative properties of generic network motifs, and we specified differentiation dynamics by analysis of kinetic transcriptome data. Furthermore, we derived a specific, data-driven mathematical model for T helper 1 versus T follicular helper cell-fate decision dynamics in acute and chronic lymphocytic choriomeningitis virus infections in mice. The model recapitulates important dynamical properties without model fitting and solely by using measured response-time distributions. Model simulations predict different windows of opportunity for perturbation in acute and chronic infection scenarios, with potential implications for optimization of targeted immunotherapy.

Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics

Physiological processes rely on the control of cell proliferation, and the dysregulation of these processes underlies various pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-laboratory studies may be integrated with different mathematical modeling approaches to aid the interpretation of the results and to enable the prediction of cell behaviors, specifically in the context of cancer.

Counting generations in birth and death processes with competing Erlang and exponential waiting times

Lymphocyte populations, stimulated in vitro or in vivo, grow as cells divide. Stochastic models are appropriate because some cells undergo multiple rounds of division, some die, and others of the same type in the same conditions do not divide at all. If individual cells behave independently, then each cell can be imagined as sampling from a probability density of times to division and death. The exponential density is the most mathematically and computationally convenient choice. It has the advantage of satisfying the memoryless property, consistent with a Markov process, but it overestimates the probability of short division times. With the aim of preserving the advantages of a Markovian framework while improving the representation of experimentally-observed division times, we consider a multi-stage model of cellular division and death. We use Erlang-distributed (or, more generally, phase-type distributed) times to division, and exponentially distributed times to death. We classify cells into generations, using the rule that the daughters of cells in generation n are in generation [Formula: see text]. In some circumstances, our representation is equivalent to established models of lymphocyte dynamics. We find the growth rate of the cell population by calculating the proportions of cells by stage and generation. The exponent describing the late-time cell population growth, and the criterion for extinction of the population, differs from what would be expected if N steps with rate [Formula: see text] were equivalent to a single step of rate [Formula: see text]. We link with a published experimental dataset, where cell counts were reported after T cells were transferred to lymphopenic mice, using Approximate Bayesian Computation. In the comparison, the death rate is assumed to be proportional to the generation and the Erlang time to division for generation 0 is allowed to differ from that of subsequent generations. The multi-stage representation is preferred to a simple exponential in posterior distributions, and the mean time to first division is estimated to be longer than the mean time to subsequent divisions.

What Will B Will B: Identifying Molecular Determinants of Diverse B-Cell Fate Decisions Through Systems Biology

B-cells are the poster child for cellular diversity and heterogeneity. The diverse repertoire of B lymphocytes, each expressing unique antigen receptors, provides broad protection against pathogens. However, B-cell diversity goes beyond unique antigen receptors. Side-stepping B-cell receptor (BCR) diversity through BCR-independent stimuli or engineered organisms with monoclonal BCRs still results in seemingly identical B-cells reaching a wide variety of fates in response to the same challenge. Identifying to what extent the molecular state of a B-cell determines its fate is key to gaining a predictive understanding of B-cells and consequently the ability to control them with targeted therapies. Signals received by B-cells through transmembrane receptors converge on intracellular molecular signaling networks, which control whether each B-cell divides, dies, or differentiates into a number of antibody-secreting distinct B-cell subtypes. The signaling networks that interpret these signals are well known to be susceptible to molecular variability and noise, providing a potential source of diversity in cell fate decisions. Iterative mathematical modeling and experimental studies have provided quantitative insight into how B-cells achieve distinct fates in response to pathogenic stimuli. Here, we review how systems biology modeling of B-cells, and the molecular signaling networks controlling their fates, is revealing the key determinants of cell-to-cell variability in B-cell destiny.

Tracking keratinocytes and melanocytes using carboxyfluorescein hydroxysuccinimidyl ester staining

Introduction

The treatment of burn wounds and hypopigmentation conditions often require autologous transplantation of keratinocytes and melanocytes. Tracking transplanted cells to ascertain their contribution to tissue recapitulation presents a challenge. This study demonstrates a methodology based on passive staining with carboxyfluorescein hydroxysuccinimidyl ester (CFSE) that enables localization of cells in tissue sections to investigate the fate of transplanted cells in wound re-epithelialisation.

Methods

Viability and migration of CFSE-stained keratinocytes and melanocytes were investigated using viability staining and scratch assays, while proliferation of cells was measured using flow cytometry. In addition, CFSE-stained keratinocytes and melanocytes were transplanted to a human ex vivo wound model, either in suspension, or with the aid of macroporous gelatine microcarriers. Wounds were analysed seven, 14 and 21 days post transplantation using cryosectioning and fluorescence microscopy. Sections from wounds with transplanted co-cultured keratinocytes and melanocytes were stained for pancytokeratin to distinguish keratinocytes.

Results

CFSE-staining of keratinocytes and melanocytes did not affect the viability, migration or proliferation of the cells. Transplanted cells were tracked in ex vivo wounds for 21 days, illustrating that the staining had no effect on wound re-epithelialisation. In conclusion, this study presents a novel application of CFSE-staining for tacking transplanted primary human keratinocytes and melanocytes.

Death and population dynamics affect mutation rate estimates and evolvability under stress in bacteria

The stress-induced mutagenesis hypothesis postulates that in response to stress, bacteria increase their genome-wide mutation rate, in turn increasing the chances that a descendant is able to better withstand the stress. This has implications for antibiotic treatment: exposure to subinhibitory doses of antibiotics has been reported to increase bacterial mutation rates and thus probably the rate at which resistance mutations appear and lead to treatment failure. More generally, the hypothesis posits that stress increases evolvability (the ability of a population to generate adaptive genetic diversity) and thus accelerates evolution. Measuring mutation rates under stress, however, is problematic, because existing methods assume there is no death. Yet subinhibitory stress levels may induce a substantial death rate. Death events need to be compensated by extra replication to reach a given population size, thus providing more opportunities to acquire mutations. We show that ignoring death leads to a systematic overestimation of mutation rates under stress. We developed a system based on plasmid segregation that allows us to measure death and division rates simultaneously in bacterial populations. Using this system, we found that a substantial death rate occurs at the tested subinhibitory concentrations previously reported to increase mutation rate. Taking this death rate into account lowers and sometimes removes the signal for stress-induced mutagenesis. Moreover, even when antibiotics increase mutation rate, we show that subinhibitory treatments do not increase genetic diversity and evolvability, again because of effects of the antibiotics on population dynamics. We conclude that antibiotic-induced mutagenesis is overestimated because of death and that understanding evolvability under stress requires accounting for the effects of stress on population dynamics as much as on mutation rate. Our goal here is dual: we show that population dynamics and, in particular, the numbers of cell divisions are crucial but neglected parameters in the evolvability of a population, and we provide experimental and computational tools and methods to study evolvability under stress, leading to a reassessment of the magnitude and significance of the stress-induced mutagenesis paradigm.

The effect of environmental stress on bacterial mutagenesis has been a paradigm-shift discovery. Recent developments include evidence that various antibiotics increase mutation rates in bacteria when used at subinhibitory concentrations. It is therefore suggested that such treatments promote resistance evolution because they increase the generation of genetic variation on which natural selection can act. However, existing methods to compute mutation rate neglect the effect of stress on death and population dynamics. Developing new experimental and computational tools, we find that taking death into account significantly lowers the signal for stress-induced mutagenesis. Moreover, we show that treatments that increase mutation rate do not always lead to increased genetic diversity, which questions the standard paradigm of increased evolvability under stress.

Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions

Cell-to-cell communication networks have critical roles in coordinating diverse organismal processes, such as tissue development or immune cell response. However, compared with intracellular signal transduction networks, the function and engineering principles of cell-to-cell communication networks are far less understood. Major complications include: cells are themselves regulated by complex intracellular signaling networks; individual cells are heterogeneous; and output of any one cell can recursively become an additional input signal to other cells. Here, we make use of a framework that treats intracellular signal transduction networks as "black boxes" with characterized input-to-output response relationships. We study simple cell-to-cell communication circuit motifs and find conditions that generate bimodal responses in time, as well as mechanisms for independently controlling synchronization and delay of cell-population responses. We apply our modeling approach to explain otherwise puzzling data on cytokine secretion onset times in T cells. Our approach can be used to predict communication network structure using experimentally accessible input-to-output measurements and without detailed knowledge of intermediate steps.

A drift-diffusion checkpoint model predicts a highly variable and growth-factor-sensitive portion of the cell cycle G1 phase

Even among isogenic cells, the time to progress through the cell cycle, or the intermitotic time (IMT), is highly variable. This variability has been a topic of research for several decades and numerous mathematical models have been proposed to explain it. Previously, we developed a top-down, stochastic drift-diffusion+threshold (DDT) model of a cell cycle checkpoint and showed that it can accurately describe experimentally-derived IMT distributions [Leander R, Allen EJ, Garbett SP, Tyson DR, Quaranta V. Derivation and experimental comparison of cell-division probability densities. J. Theor. Biol. 2014;358:129-135]. Here, we use the DDT modeling approach for both descriptive and predictive data analysis. We develop a custom numerical method for the reliable maximum likelihood estimation of model parameters in the absence of a priori knowledge about the number of detectable checkpoints. We employ this method to fit different variants of the DDT model (with one, two, and three checkpoints) to IMT data from multiple cell lines under different growth conditions and drug treatments. We find that a two-checkpoint model best describes the data, consistent with the notion that the cell cycle can be broadly separated into two steps: the commitment to divide and the process of cell division. The model predicts one part of the cell cycle to be highly variable and growth factor sensitive while the other is less variable and relatively refractory to growth factor signaling. Using experimental data that separates IMT into G1 vs. S, G2, and M phases, we show that the model-predicted growth-factor-sensitive part of the cell cycle corresponds to a portion of G1, consistent with previous studies suggesting that the commitment step is the primary source of IMT variability. These results demonstrate that a simple stochastic model, with just a handful of parameters, can provide fundamental insights into the biological underpinnings of cell cycle progression.

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.

Mathematical Models for Immunology: Current State of the Art and Future Research Directions

The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years.

Multiscale modelling in immunology: a review

One of the greatest challenges in biomedicine is to get a unified view of observations made from the molecular up to the organism scale. Towards this goal, multiscale models have been highly instrumental in contexts such as the cardiovascular field, angiogenesis, neurosciences and tumour biology. More recently, such models are becoming an increasingly important resource to address immunological questions as well. Systematic mining of the literature in multiscale modelling led us to identify three main fields of immunological applications: host-virus interactions, inflammatory diseases and their treatment and development of multiscale simulation platforms for immunological research and for educational purposes. Here, we review the current developments in these directions, which illustrate that multiscale models can consistently integrate immunological data generated at several scales, and can be used to describe and optimize therapeutic treatments of complex immune diseases.

Communication III (Immunological Control)

One of controlling systems in the body which the body also uses for communication (externally and internally) is the Immune system, based upon existence of MHC molecules fundamental for recognition, and other molecules responsible for antigen-presentation and immediate or postponed reaction to it. The fundamental unique feature of immune system cells is the capability of distinguishing “self” from “non-self” cells and proteins. Communication between different cell types of the immune system is critical in the recognition of self, surveillance, defense, and clearance of foreign invaders. These signaling mechanisms involve direct cell–cell signaling as well as autocrine and paracrine signaling. The essential feature of particular cells of immunological system is memory and although still known at the level of phenomenology, presents the basis for vaccines.

Changing face of vaccination in immunocompromised hosts

Infection prevention is a key component of care and an important determinant of clinical outcomes in a diverse population of immunocompromised hosts. Vaccination remains a fundamental preventative strategy, and clear guidelines exist for the vaccination of immunocompromised individuals and close contacts. Unfortunately, adherence to such guidelines is frequently suboptimal, with consequent missed opportunities to prevent infection. Additionally, vaccination of immunocompromised individuals is known to produce responses inferior to those observed in immunocompetent hosts. Multiple factors contribute to this finding, and developing improved vaccination strategies for those at high risk of infectious complications remains a priority of care providers. Herein, we review potential factors contributing to vaccine outcomes, focusing on host immune responses, and propose a means for applying modern, innovative systems biology technology to model critical determinants of vaccination success. With influenza vaccine in solid organ transplants used as a case in point, novel means for stratifying individuals using a host "immunophenotype" are explored, and strategies for individualizing vaccine approaches tailored to safely optimize vaccine responses in those most at risk are discussed.

FlowMax: A Computational Tool for Maximum Likelihood Deconvolution of CFSE Time Courses

The immune response is a concerted dynamic multi-cellular process. Upon infection, the dynamics of lymphocyte populations are an aggregate of molecular processes that determine the activation, division, and longevity of individual cells. The timing of these single-cell processes is remarkably widely distributed with some cells undergoing their third division while others undergo their first. High cell-to-cell variability and technical noise pose challenges for interpreting popular dye-dilution experiments objectively. It remains an unresolved challenge to avoid under- or over-interpretation of such data when phenotyping gene-targeted mouse models or patient samples. Here we develop and characterize a computational methodology to parameterize a cell population model in the context of noisy dye-dilution data. To enable objective interpretation of model fits, our method estimates fit sensitivity and redundancy by stochastically sampling the solution landscape, calculating parameter sensitivities, and clustering to determine the maximum-likelihood solution ranges. Our methodology accounts for both technical and biological variability by using a cell fluorescence model as an adaptor during population model fitting, resulting in improved fit accuracy without the need for ad hoc objective functions. We have incorporated our methodology into an integrated phenotyping tool, FlowMax, and used it to analyze B cells from two NFκB knockout mice with distinct phenotypes; we not only confirm previously published findings at a fraction of the expended effort and cost, but reveal a novel phenotype of nfkb1/p105/50 in limiting the proliferative capacity of B cells following B-cell receptor stimulation. In addition to complementing experimental work, FlowMax is suitable for high throughput analysis of dye dilution studies within clinical and pharmacological screens with objective and quantitative conclusions.

A division-dependent compartmental model for computing cell numbers in CFSE-based lymphocyte proliferation assays

Some key features of a mathematical description of an immune response are an estimate of the number of responding cells and the manner in which those cells divide, differentiate, and die. The intracellular dye CFSE is a powerful experimental tool for the analysis of a population of dividing cells, and numerous mathematical treatments have been aimed at using CFSE data to describe an immune response [30,31,32,37,38,42,48,49]. Recently, partial differential equation structured population models, with measured CFSE fluorescence intensity as the structure variable, have been shown to accurately fit histogram data obtained from CFSE flow cytometry experiments [18,19,52,54]. In this report, the population of cells is mathematically organized into compartments, with all cells in a single compartment having undergone the same number of divisions. A system of structured partial differential equations is derived which can be fit directly to CFSE histogram data. From such a model, cell counts (in terms of the number of divisions undergone) can be directly computed and thus key biological parameters such as population doubling time and precursor viability can be determined. Mathematical aspects of this compartmental model are discussed, and the model is fit to a data set. As in [18,19], we find temporal and division dependence in the rates of proliferation and death to be essential features of a structured population model for CFSE data. Variability in cellular autofluorescence is found to play a significant role in the data, as well. Finally, the compartmental model is compared to previous work, and statistical aspects of the experimental data are discussed.

A novel method for kinetic measurements of rare cell proliferation using Cellometer image-based cytometry

Cell proliferation is an important assay for pharmaceutical and biomedical research to test the effects of a variety of treatments on cultured primary cells or cell lines. For immunological studies, the ability to perform rapid cell proliferation analysis allows the identification of potential biological reagents for inducing or inhibiting immune cell proliferation. Current cell proliferation analysis methods employ flow cytometry for fluorescence detection of CFSE-labeled cells. However, conventional flow cytometers require a considerable amount of cells per sample, which becomes an issue for kinetic measurements with rare cell population due to the lack of samples for flow cytometric analyses at multiple time points during proliferation period. Here we report the development of a novel cell proliferation kinetic detection method for low cell concentration samples using the new Cellometer Vision system. Since the Cellometer system requires only 20 μl of sample, cell proliferation can be measured at multiple time points over the entire culturing period, whereas typically, flow cytometry is only performed at the end of the proliferation period. To validate the detection method, B1 and B2 B cells were treated with a B cell mitogen for 6 days, and proliferation was measured using Cellometer on day 1, 3, 5, and 6. To demonstrate the capability of the system, B1 B cells were treated with a panel of TLR agonists (Pam3Cys, PolyIC, CLO97, and CpG) for 7 days, and proliferation was measured on day 2, 4, 6, and 7. Cellometer image-based cytometry (IBC) was able to obtain proliferation results on each day with the last time point comparable to flow cytometry. This novel method allows for kinetic measurements of the rare cell samples such as B1 B cell, which has the potential to revolutionize kinetic analysis of cell proliferation.

NONLINEAR DYNAMICS OF CELL CYCLES WITH STOCHASTIC MATHEMATICAL MODELS

Cell cycles are fundamental components of all living organisms and their systematic studies extend our knowledge about the interconnection between regulatory, metabolic, and signaling networks, and therefore open new opportunities for our ultimate efficient control of cellular processes for disease treatments, as well as for a wide variety of biomedical and biotechnological applications. In the study of cell cycles, nonlinear phenomena play a paramount role, in particular in those cases where the cellular dynamics is in the focus of attention. Quantification of this dynamics is a challenging task due to a wide range of parameters that require estimations and the presence of many stochastic effects. Based on the originally deterministic model, in this paper we develop a hierarchy of models that allow us to describe the nonlinear dynamics accounting for special events of cell cycles. First, we develop a model that takes into account fluctuations of relative concentrations of proteins during special events of cell cycles. Such fluctuations are induced by varying rates of relative concentrations of proteins and/or by relative concentrations of proteins themselves. As such fluctuations may be responsible for qualitative changes in the cell, we develop a new model that accounts for the effect of cellular dynamics on the cell cycle. Finally, we analyze numerically nonlinear effects in the cell cycle by constructing phase portraits based on the newly developed model and carry out a parametric sensitivity analysis in order to identify parameters for an efficient cell cycle control. The results of computational experiments demonstrate that the metabolic events in gene regulatory networks can qualitatively influence the dynamics of the cell cycle.

Systems biology in immunology: a computational modeling perspective

Systems biology is an emerging discipline that combines high-content, multiplexed measurements with informatic and computational modeling methods to better understand biological function at various scales. Here we present a detailed review of the methods used to create computational models and to conduct simulations of immune function. We provide descriptions of the key data-gathering techniques employed to generate the quantitative and qualitative data required for such modeling and simulation and summarize the progress to date in applying these tools and techniques to questions of immunological interest, including infectious disease. We include comments on what insights modeling can provide that complement information obtained from the more familiar experimental discovery methods used by most investigators and the reasons why quantitative methods are needed to eventually produce a better understanding of immune system operation in health and disease.

Stochastic models of lymphocyte proliferation and death

Quantitative understanding of the kinetics of lymphocyte proliferation and death upon activation with an antigen is crucial for elucidating factors determining the magnitude, duration and efficiency of the immune response. Recent advances in quantitative experimental techniques, in particular intracellular labeling and multi-channel flow cytometry, allow one to measure the population structure of proliferating and dying lymphocytes for several generations with high precision. These new experimental techniques require novel quantitative methods of analysis. We review several recent mathematical approaches used to describe and analyze cell proliferation data. Using a rigorous mathematical framework, we show that two commonly used models that are based on the theories of age-structured cell populations and of branching processes, are mathematically identical. We provide several simple analytical solutions for a model in which the distribution of inter-division times follows a gamma distribution and show that this model can fit both simulated and experimental data. We also show that the estimates of some critical kinetic parameters, such as the average inter-division time, obtained by fitting models to data may depend on the assumed distribution of inter-division times, highlighting the challenges in quantitative understanding of cell kinetics.

Tax gene expression and cell cycling but not cell death are selected during HTLV-1 infection in vivo

Background

Adult T cell leukemia results from the malignant transformation of a CD4⁺ lymphoid clone carrying an integrated HTLV-1 provirus that has undergone several oncogenic events over a 30-60 year period of persistent clonal expansion. Both CD4⁺ and CD8⁺ lymphocytes are infected in vivo; their expansion relies on CD4⁺ cell cycling and on the prevention of CD8⁺ cell death. Cloned infected CD4⁺ but not CD8⁺ T cells from patients without malignancy also add up nuclear and mitotic defects typical of genetic instability related to theexpression of the virus-encoded oncogene tax. HTLV-1 expression is cancer-prone in vitro, but in vivo numerous selection forces act to maintain T cell homeostasis and are possibly involved in clonal selection.

Results

Here we demonstrate that the HTLV-1 associated CD4⁺ preleukemic phenotype and the specific patterns of CD4⁺ and CD8⁺ clonal expansion are in vivo selected processes. By comparing the effects of recent (1 month) experimental infections performed in vitro and those observed in cloned T cells from patients infected for >6-26 years, we found that in chronically HTLV-1 infected individuals, HTLV-1 positive clones are selected for tax expression. In vivo, infected CD4⁺ cells are positively selected for cell cycling whereas infected CD8⁺ cells and uninfected CD4⁺ cells are negatively selected for the same processes. In contrast, the known HTLV-1-dependent prevention of CD8⁺ T cell death pertains to both in vivo and in vitro infected cells.

Conclusions

Therefore, virus-cell interactions alone are not sufficient to initiate early leukemogenesis in vivo.

BCR-induced superoxide negatively regulates B-cell proliferation and T-cell-independent type 2 Ab responses

Superoxide and its derivatives have been implicated as secondary messenger molecules that influence signaling cascades in non-phagocytes. B lymphocytes produce superoxide after BCR ligation. We found that these ROS regulate B-cell signaling and entry into the cell cycle. B cells from mice deficient in the gp91(phox) subunit of the NADPH oxidase complex are unable to generate ROS after BCR ligation. However, after BCR stimulation, more gp91(phox) KO B cells enter the G1 stage of the cell cycle and proliferate than WT B cells. BCR ligation leads to a more rapid decrease in p27(Kip1) levels in gp91(phox) KO B cells. Gp91(phox) KO mice display enhanced T-cell-independent type 2, but normal T-dependent Ab responses. ROS-dependent regulation of BCR-induced proliferation may help modulate the size of the humoral response to T-cell-independent type 2 Ag immunization.

Thymic development beyond beta-selection requires phosphatidylinositol 3-kinase activation by CXCR4

T cell development requires phosphatidylinositol 3-kinase (PI3K) signaling with contributions from both the class IA, p110δ, and class IB, p110γ catalytic subunits. However, the receptors on immature T cells by which each of these PI3Ks are activated have not been identified, nor has the mechanism behind their functional redundancy in the thymus. Here, we show that PI3K signaling from the preTCR requires p110δ, but not p110γ. Mice deficient for the class IB regulatory subunit p101 demonstrated the requirement for p101 in T cell development, implicating G protein–coupled receptor signaling in β-selection. We found evidence of a role for CXCR4 using small molecule antagonists in an in vitro model of β-selection and demonstrated a requirement for CXCR4 during thymic development in CXCR4-deficient embryos. Finally, we demonstrate that CXCL12, the ligand for CXCR4, allows for Notch-dependent differentiation of DN3 thymocytes in the absence of supporting stromal cells. These findings establish a role for CXCR4-mediated PI3K signaling that, together with signals from Notch and the preTCR, contributes to continued T cell development beyond β-selection.

Agent-based simulation of T-cell activation and proliferation within a lymph node

Recent intravital microscopy experiments have revealed the complex behavior of T cells within lymph nodes. Modeling T-cell responses in lymph nodes now requires integration of cell trafficking and motility with the molecular processes involved in T-cell activation. We describe an agent-based model that allows such integration, in which T cells undertake a random walk through a three-dimensional representation of the lymph node paracortex, integrating signals from dendritic cells (DCs), and proliferating in response. The model accommodates simulation of a large number of T cells packed at realistic densities, and includes dynamic cell trafficking that allows the lymph nodes to swell and shrink as the immune response progresses. The results from the model, including the kinetics of cognate T-cell proliferation and release, and the changes in their avidity profile, are similar to those observed in vivo. We therefore propose that this modeling framework is capable of successfully simulating T-cell activation while also accounting for new spatiotemporal knowledge of how T cells and DCs interact. Although some of the parameters used to drive the model are not yet experimentally validated, the model is capable of testing the effects of alternative values for any parameter on the T-cell response. We intend to refine each aspect of the model in collaboration with both theoreticians and experimentalists.

Immunobiological outcomes of repeated chlamydial infection from two models of within-host population dynamics

Background

Chlamydia trachomatis is a common human pathogen that mediates disease processes capable of inflicting serious complications on reproduction. Aggressive inflammatory immune responses are thought to not only direct a person's level of immunity but also the potential for immunopathology. With human immunobiology being debated as a cause of prevailing epidemiological trends, we examined some fundamental issues regarding susceptibility to multiple chlamydial infections that could have implications for infection spread. We argue that, compared to less-frequent exposure, frequent exposure to chlamydia may well produce unique immunobiological characteristics that likely to have important clinical and epidemiological implications.

Methods and Results

As a novel tool for studying chlamydia, we applied principles of modeling within-host pathogen dynamics to enable an understanding of some fundamental characteristics of an individual's immunobiology during multiple chlamydial infections. While the models were able to reproduce shorter-term infection kinetics of primary and secondary infections previously observed in animal models, it was also observed that longer periods between initial and second infection may increase an individual's chlamydial load and lengthen their duration of infectiousness. The cessation of short-term repeated exposure did not allow for the formation of long-lasting immunity. However, frequent re-exposure non-intuitively linked the formation of protective immunity, persistent infection, and the potential for immunopathology.

Conclusions

Overall, these results provide interesting insights that should be verified with continued study. Nevertheless, these results appear to raise challenges for current evidence of the development of long-lasting immunity against chlamydia, and suggest the existence of a previously unidentified mechanism for the formation of persistent infection. The obvious next goal is to investigate the qualitative impact of these results on the spread of chlamydia.

Regulation of B-cell entry into the cell cycle

B cells are induced to enter the cell cycle by stimuli including ligation of the B-cell receptor (BCR) complex and Toll-like receptor (TLR) agonists. This review discusses the contribution of several molecules, which act at distinct steps in B-cell activation. The adapter molecule Bam32 (B-lymphocyte adapter of 32 kDa) helps promote BCR-induced cell cycle entry, while the secondary messenger superoxide has the opposite effect. Bam32 and superoxide may fine tune BCR-induced activation by competing for the same limited resources, namely Rac1 and the plasma membrane phospholipid PI(3,4)P(2). The co-receptor CD22 can inhibit BCR-induced proliferation by binding to novel CD22 ligands. Finally, regulators of B-cell survival and death also play roles in B-cell transit through the cell cycle. Caspase 6 negatively regulates CD40- and TLR-dependent G(1) entry, while acting later in the cell cycle to promote S-phase entry. Caspase 6 deficiency predisposes B cells to differentiate rather than proliferate after stimulation. Bim, a pro-apoptotic Bcl-2 family member, exerts a positive regulatory effect on cell cycle entry, which is opposed by Bcl-2. New insights into what regulates B-cell transit through the cell cycle may lead to thoughtful design of highly selective drugs that target pathogenic B cells.

Measurement of proliferative responses of cultured lymphocytes

Measurement of proliferative responses of human lymphocytes is a fundamental technique for the assessment of their biological responses to various stimuli. Most simply, this involves measurement of the number of cells present in a culture before and after the addition of a stimulating agent. This unit contains several different prototype protocols to measure the proliferative response of lymphocytes following exposure to mitogens, antigens, allogeneic or autologous cells, or soluble factors. Each of these protocols can be used in conjunction with an accompanying support protocol which contains methods for pulsing cultures with [3H]thymidine and determining incorporation of [3H]thymidine into DNA or assessing cell proliferation by nonradioactive methods, e.g., reduction of tetrazolium salts (MTT). The protocols described here provide an estimate of DNA synthesis and cell proliferation in an entire cell population, but do not provide information on the proliferation of individual cells. A protocol for CFSE labeling allows specific subpopulations of cells to be separated viably for further analysis.

Development of an isoform-specific gene suppression system: the study of the human Pax-5B transcriptional element

The transcription factor Pax-5, is vital during B lymphocyte differentiation and is known to contribute to the oncogenesis of certain cancers. The Pax-5 locus generates multiple yet structurally related mRNA transcripts through the specific activation of alternative promoter regions and/or alternative splicing events which poses challenges in the study of specific isoform function. In this study, we investigated the function of a major Pax-5 transcript, Pax-5B using an enhanced version of the Hepatitis Delta Virus ribozyme (HDV Rz) suppression system that is specifically designed to recognize and cleave the human Pax-5B mRNA. The activity of these ribozymes resulted in the specific suppression of the Pax-5B transcripts without altering the transcript levels of other closely related Pax-5 isoforms mRNAs both in vitro and in an intracellular setting. Following stable transfection of the ribozymes into a model B cell line (REH), we showed that Pax-5B suppression led to an increase of CD19 mRNA and cell surface protein expression. In response to this Pax-5B specific deregulation, a marked increase in apoptotic activity compared to control cell lines was observed. These results suggest that Pax-5B has distinct roles in physiological processes in cell fate events during lymphocyte development.

Monitoring lymphocyte proliferation in vitro and in vivo with the intracellular fluorescent dye carboxyfluorescein diacetate succinimidyl ester

This protocol outlines the carboxyfluorescein diacetate succinimidyl ester (CFSE) method for following the proliferation of human lymphocytes in vitro and mouse lymphocytes both in vitro and in vivo. The method relies on the ability of CFSE to covalently label long-lived intracellular molecules with the highly fluorescent dye, carboxyfluorescein. Following each cell division, the equal distribution of these fluorescent molecules to progeny cells results in a halving of the fluorescence of daughter cells. The CFSE labeling protocol described, which typically takes <1 h to perform, allows the detection of up to eight cell divisions before CFSE fluorescence is decreased to the background fluorescence of unlabeled cells. Protocols are outlined for labeling large and small numbers of human and mouse lymphocytes, labeling conditions being identified that minimize CFSE toxicity but maximize the number of cell divisions detected. An important feature of the technique is that division-dependent changes in the expression of cell-surface markers and intracellular proteins are easily quantified by flow cytometry.

A probabilistic view of immunology: drawing parallels with physics

Regulating the strength and class of an immune response requires lymphocytes to act as complex signal integrating "machines", taking information from multiple sources while making decisions that affect the final outcome. Describing and understanding the decision-making behaviour of lymphocytes within the context of the dynamic multifaceted immune system appears immensely complicated. In this article I contrast two alternative frameworks, the deterministic and the probabilistic as competing paths to achieve successful quantitative immune models. As the two alternatives are traditional scientific rivals, I use the probabilistic fields in physics to highlight the potential value of the probabilistic perspective in immunology.