A logical analysis of T cell activation and anergy

Abstract

Interaction of the antigen-specific receptor of T lymphocytes with its antigenic ligand can lead either to cell activation or to a state of profound unresponsiveness (anergy). Although subtle changes in the nature of the ligand or of the antigen-presenting cell have been shown to affect the outcome of T cell receptor ligation, the mechanism by which the same receptor can induce alternative cellular responses is not completely understood. A model for explaining both positive (cell proliferation and cytokine production) and negative (anergy induction) signaling of T lymphocytes is described herein. This model relies on the autophosphorylative properties of the tyrosine kinases associated with the T cell receptor. One of its basic assumptions is that the kinase activity of these receptor-associated enzymes remains above background level after ligand removal and is responsible for cellular unresponsiveness. Using a simple Boolean formalism, we show how the timing of the binding and intracellular signal-transduction events can affect the properties of receptor signaling and determine the type of cellular response. The present approach integrates into a common framework a large body of experimental observations and allows specification of conditions leading to cellular activation or to anergy.The initiation and regulation of an immune response to antigen are dependent on the activation of helper T lymphocytes by an appropriate antigen͞major histocompatibility complex ligand (Ag͞MHC). In the recent years, it has been observed that engagement of the T cell antigen receptor (TCR) by its cognate ligand triggers a series of biochemical events within the cell, which can lead either to cellular activation, i.e., lymphokine production and cell proliferation, or to the induction of a state of unresponsiveness termed anergy. This anergic state is characterized by a refractoriness to further antigenic stimulation despite normal TCR cell surface expression and normal response to exogenous growth factors. A variety of experimental models have led to the conclusion that T cell anergy can develop in response to a heterogeneous set of stimuli and under different experimental protocols, as summarized below.A large body of data, obtained mostly in vitro, indicates that anergy may develop as a consequence of inadequate stimulation. Indeed, occupancy of the TCR by an appropriate ligand (an agonist Ag͞MHC complex, anti-TCR-complex antibodies, or mitogenic lectins) in the absence of other antigenpresenting cell (APC)-derived signals (termed costimulatory signals) has been shown to induce T cell anergy rather than a functional, proliferative response (see ref. 1 for review). A state of T cell unresponsiveness can also be induced in mature T cells by analogs of immunogenic peptides (altered peptide ligands or APLs), which interact in an unproductive fashion with the TCR complex even when presented by adequate, costimulatory-expressing APCs (2). These ligands are thought to elicit a subset of normal intracellular activation events, le...

A computational model of a class of gene networks with positive and negative controls

“…Interactions among the genes in the network are modelled by logical rules. Such approaches have yielded results that appear to confirm those obtained from quantitative approaches, and have been applied in the study of various actual biological processes, such as immunity control in bacteriophage lambda , activation and anergy of T cells (Kaufman et al, 1999), and flower morphogenesis in Arabidopsis thaliana (Mendoza et al, 1999).…”

A Markovian approach to the control of genetic regulatory networks

“…Interactions among the genes in the network are modelled by logical rules. Such approaches have yielded results that appear to confirm those obtained from quantitative approaches, and have been applied in the study of various actual biological processes, such as immunity control in bacteriophage lambda , activation and anergy of T cells (Kaufman et al, 1999), and flower morphogenesis in Arabidopsis thaliana (Mendoza et al, 1999). There are also attempts to develop approaches that use both discrete and continuous variables in the modelling of biological systems (e.g., Duan et al, 2000;Edwards et al, 2001).…”

Modeling Systems-Level Regulation of Host Immune Responses

“…Qualitative discrete modeling such as ours has been previously successfully implemented in gene regulatory networks and signal transduction networks for predicting the dynamic trajectory of biological circuits and for accessing the reliability of gene regulatory networks in signal processing [31,32,50]. In the study of immunological responses, this approach has been implemented in small networks for the analysis of T cell activation and anergy [51] and for the analysis of lymphocyte subsets [52]. Here, a comprehensive network was constructed to study the immunological responses at the systems level, and the dynamic model of this network was successfully validated.…”