Cell surface dynamics: the balance between diffusion, aggregation and endocytosis

On the meaning of affinity limits in B-cell epitope prediction for antipeptide antibody-mediated immunity

B-cell epitope prediction aims to aid the design of peptide-based immunogens (e.g., vaccines) for eliciting antipeptide antibodies that protect against disease, but such antibodies fail to confer protection and even promote disease if they bind with low affinity. Hence, the Immune Epitope Database (IEDB) was searched to obtain published thermodynamic and kinetic data on binding interactions of antipeptide antibodies. The data suggest that the affinity of the antibodies for their immunizing peptides appears to be limited in a manner consistent with previously proposed kinetic constraints on affinity maturation in vivo and that cross-reaction of the antibodies with proteins tends to occur with lower affinity than the corresponding reaction of the antibodies with their immunizing peptides. These observations better inform B-cell epitope prediction to avoid overestimating the affinity for both active and passive immunization; whereas active immunization is subject to limitations of affinity maturation in vivo and of the capacity to accumulate endogenous antibodies, passive immunization may transcend such limitations, possibly with the aid of artificial affinity-selection processes and of protein engineering. Additionally, protein disorder warrants further investigation as a possible supplementary criterion for B-cell epitope prediction, where such disorder obviates thermodynamically unfavorable protein structural adjustments in cross-reactions between antipeptide antibodies and proteins.

Expression of excess receptors and negative feedback control of signal pathways are required for rapid activation and prompt cessation of signal transduction

Background

Cellular signal transduction is initiated by the binding of extracellular ligands to membrane receptors. Receptors are often expressed in excess, and cells are activated when a small number of receptors bind ligands. Intracellular signal proteins are activated at a high level soon after ligand binding, and the activation level decreases in a negative feedback manner without ligand clearance. Why are excess receptors required? What is the physiological significance of the negative feedback regulation?

Results

To answer these questions, we developed a Monte Carlo simulation program to kinetically analyze signal pathways using the model in which ligands are bound to receptors and then membrane complexes with other membrane proteins are formed. Our simulation results showed that excess receptors are not required for cell activation when the dissociation constant (Kd) of the ligand-receptor complex is 10^-10 M or less. However, such low Kd values cause delayed signal shutdown after ligand clearance from the extracellular space. In contrast, when the Kd was 10^-8 M and the ligand level was less than 1 μM, excess receptors were required for prompt signal propagation and rapid signal cessation after ligand clearance. An initial increase in active cytosolic signal proteins to a high level is required for rapid activation of cellular signal pathways, and a low level of active signal proteins is essential for the rapid shutdown of signal pathways after ligand clearance.

Conclusion

The present kinetic analysis revealed that excess receptors and negative feedback regulation promote activation and cessation of signal transduction with a low amount of extracellular ligand.

Regulation of caveolin-1 membrane trafficking by the Na/K-ATPase

Here, we show that the Na/K-ATPase interacts with caveolin-1 (Cav1) and regulates Cav1 trafficking. Graded knockdown of Na/K-ATPase decreases the plasma membrane pool of Cav1, which results in a significant reduction in the number of caveolae on the cell surface. These effects are independent of the pumping function of Na/K-ATPase, and instead depend on interaction between Na/K-ATPase and Cav1 mediated by an N-terminal caveolin-binding motif within the ATPase α1 subunit. Moreover, knockdown of the Na/K-ATPase increases basal levels of active Src and stimulates endocytosis of Cav1 from the plasma membrane. Microtubule-dependent long-range directional trafficking in Na/K-ATPase–depleted cells results in perinuclear accumulation of Cav1-positive vesicles. Finally, Na/K-ATPase knockdown has no effect on processing or exit of Cav1 from the Golgi. Thus, the Na/K-ATPase regulates Cav1 endocytic trafficking and stabilizes the Cav1 plasma membrane pool.

Mechanistic insight into lymphocyte activation through quantitative imaging and theoretical modelling

Increasingly, it is apparent that in order to understand the complexity of immunosurveillance at the cell-cell junction, quantitative analysis at the single cell level is necessary. The visualisation of the large-scale rearrangement of proteins characterising what is known as the immunological synapse (IS) was an important discovery shaping our understanding of the events occurring during immune recognition. The use of supported planar bilayers and geometrically designed substrates combined with advanced imaging techniques such as total internal reflection fluorescence (TIRF) and Förster resonance energy transfer (FRET) has provided insight into the spatio-temporal dynamics of receptor signalling and the role of receptor trafficking in regulating cell signalling. Theoretical modelling will play a key role in the integration of such quantitative data providing mechanistic insight into lymphocyte activation.

The evolution of mathematical immunology

The types of mathematical models used in immunology and their scope have changed drastically in the past 10 years. Classical models were based on ordinary differential equations (ODEs), difference equations, and cellular automata. These models focused on the 'simple' dynamics obtained between a small number of reagent types (e.g. one type of receptor and one type of antigen or two T-cell populations). With the advent of high-throughput methods, genomic data, and unlimited computing power, immunological modeling shifted toward the informatics side. Many current applications of mathematical models in immunology are now focused around the concepts of high-throughput measurements and system immunology (immunomics), as well as the bioinformatics analysis of molecular immunology. The types of models have shifted from mainly ODEs of simple systems to the extensive use of Monte Carlo simulations. The transition to a more molecular and more computer-based attitude is similar to the one occurring over all the fields of complex systems analysis. An interesting additional aspect in theoretical immunology is the transition from an extreme focus on the adaptive immune system (that was considered more interesting from a theoretical point of view) to a more balanced focus taking into account the innate immune system also. We here review the origin and evolution of mathematical modeling in immunology and the contribution of such models to many important immunological concepts.