Literature DB >> 17367335

Stochasticity and spatial heterogeneity in T-cell activation.

Nigel J Burroughs1, P Anton van der Merwe.   

Abstract

Stochastic and spatial aspects are becoming increasingly recognized as an important factor in T-cell activation. Activation occurs in an intrinsically noisy environment, requiring only a handful of agonist peptide-major histocompatibility complex molecules, thus making consideration of signal to noise of prime importance in understanding sensitivity and specificity. Furthermore, it is widely established that surface-bound ligands are more effective at activation than soluble forms, while surface patternation has highlighted the role of spatial relocation in activation. Here we consider the results of a number of models of T-cell activation, from a realistic model of kinetic segregation-induced T-cell receptor (TCR) triggering through to simple queuing theory models. These studies highlight the constraints on cell activation by a surface receptor that recruits kinases. Our analysis shows that TCR triggering based on trapping of bound TCRs in regions of close proximity that exclude large ectodomain-containing molecules, such as the phosphatases CD45 and CD148, can effectively reproduce known signaling characteristics and is a viable 'signal transduction' mechanism distinct from oligomerization and conformation-based mechanisms. A queuing theory analysis shows the interrelation between sensitivity and specificity, emphasizing that these are properties of individual cell functions and need not be, nor are likely to be, uniform across different functions. In fact, threshold-based mechanisms of detection are shown to be poor at ligand discrimination because, although they can be highly specific, that specificity is limited to a small range of peptide densities. Time integration mechanisms however are able to control noise effectively, while kinetic proofreading mechanisms endow them with good specificity properties. Thus, threshold mechanisms are likely to be important for rapidly detecting minimal signaling requirements, thus achieving efficient scanning of antigen-presenting cells. However, for good specificity, time integration on a scale of hours is required.

Entities:  

Mesh:

Substances:

Year:  2007        PMID: 17367335     DOI: 10.1111/j.1600-065X.2006.00486.x

Source DB:  PubMed          Journal:  Immunol Rev        ISSN: 0105-2896            Impact factor:   12.988


  12 in total

1.  Tunable kinetic proofreading in a model with molecular frustration.

Authors:  Andre M Lindo; Bruno F Faria; Fernao V de Abreu
Journal:  Theory Biosci       Date:  2011-09-24       Impact factor: 1.919

2.  Early T-cell activation biophysics.

Authors:  Nelly Henry; Claire Hivroz
Journal:  HFSP J       Date:  2009-11-10

Review 3.  Pairing computation with experimentation: a powerful coupling for understanding T cell signalling.

Authors:  Arup K Chakraborty; Jayajit Das
Journal:  Nat Rev Immunol       Date:  2010-01       Impact factor: 53.106

Review 4.  Perspectives for computer modeling in the study of T cell activation.

Authors:  Jesse Coward; Ronald N Germain; Grégoire Altan-Bonnet
Journal:  Cold Spring Harb Perspect Biol       Date:  2010-05-05       Impact factor: 10.005

5.  Dependence of T cell antigen recognition on T cell receptor-peptide MHC confinement time.

Authors:  Milos Aleksic; Omer Dushek; Hao Zhang; Eugene Shenderov; Ji-Li Chen; Vincenzo Cerundolo; Daniel Coombs; P Anton van der Merwe
Journal:  Immunity       Date:  2010-02-04       Impact factor: 31.745

6.  Stochastic effects and bistability in T cell receptor signaling.

Authors:  Tomasz Lipniacki; Beata Hat; James R Faeder; William S Hlavacek
Journal:  J Theor Biol       Date:  2008-05-10       Impact factor: 2.691

7.  A novel statistical analysis and interpretation of flow cytometry data.

Authors:  H T Banks; D F Kapraun; W Clayton Thompson; Cristina Peligero; Jordi Argilaguet; Andreas Meyerhans
Journal:  J Biol Dyn       Date:  2013       Impact factor: 2.179

8.  Peripheral residence of naïve CD4 T cells induces MHC class II-dependent alterations in phenotype and function.

Authors:  Sanket Rane; Rituparna Das; Vidya Ranganathan; Savit Prabhu; Arundhoti Das; Hamid Mattoo; Jeannine Marie Durdik; Anna George; Satyajit Rath; Vineeta Bal
Journal:  BMC Biol       Date:  2014-12-21       Impact factor: 7.431

9.  Modelling the interplay between the CD4[Formula: see text]/CD8[Formula: see text] T-cell ratio and the expression of MHC-I in tumours.

Authors:  Christian John Hurry; Alexander Mozeika; Alessia Annibale
Journal:  J Math Biol       Date:  2021-06-18       Impact factor: 2.259

10.  A novel ZAP-70 dependent FRET based biosensor reveals kinase activity at both the immunological synapse and the antisynapse.

Authors:  Clotilde Randriamampita; Pierre Mouchacca; Bernard Malissen; Didier Marguet; Alain Trautmann; Annemarie Coffman Lellouch
Journal:  PLoS One       Date:  2008-01-30       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.