Literature DB >> 11374299

Th1 or Th2: how an appropriate T helper response can be made.

C Bergmann1, J L Van Hemmen, L A Segel.   

Abstract

Two types of T helper (Th) cells have been defined on the basis of their cytokine secretion patterns. The decision of a naive T cell to differentiate into Th1 or Th2 is crucial, since to a first approximation it determines whether a cell-mediated or humoral immune response is triggered against a particular pathogen, which profoundly influences disease outcome. Here we show that the internal behaviour of the T helper system, which emerges from regulatory mechanisms 'built into' the T helper system, itself can usually select the appropriate T helper response. This phenomenon arises from an initial Th1 bias together with the induction of Th1-->Th2 switches when Th1 effectors do not lead to efficient antigen clearance. The occurrence of these shifts is based on the antigen dose dependence of T helper differentiation, which is a consequence of asymmetries in cross-suppression. Critical for this feature is the rate with which Th2 cells undergo antigen-induced cell death.

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Year:  2001        PMID: 11374299     DOI: 10.1006/bulm.2000.0215

Source DB:  PubMed          Journal:  Bull Math Biol        ISSN: 0092-8240            Impact factor:   1.758


  9 in total

1.  GATA-3 transcriptional imprinting in Th2 lymphocytes: a mathematical model.

Authors:  Thomas Höfer; Holger Nathansen; Max Löhning; Andreas Radbruch; Reinhart Heinrich
Journal:  Proc Natl Acad Sci U S A       Date:  2002-06-26       Impact factor: 11.205

2.  Modeling Macrophage Polarization and Its Effect on Cancer Treatment Success.

Authors:  Valentin Morales; Luis Soto-Ortiz
Journal:  Open J Immunol       Date:  2018-06-29

3.  Diversity and plasticity of Th cell types predicted from regulatory network modelling.

Authors:  Aurélien Naldi; Jorge Carneiro; Claudine Chaouiya; Denis Thieffry
Journal:  PLoS Comput Biol       Date:  2010-09-02       Impact factor: 4.475

4.  Modeling stochasticity and robustness in gene regulatory networks.

Authors:  Abhishek Garg; Kartik Mohanram; Alessandro Di Cara; Giovanni De Micheli; Ioannis Xenarios
Journal:  Bioinformatics       Date:  2009-06-15       Impact factor: 6.937

5.  Competition for antigen between Th1 and Th2 responses determines the timing of the immune response switch during Mycobaterium avium subspecies paratuberulosis infection in ruminants.

Authors:  Gesham Magombedze; Shigetoshi Eda; Vitaly V Ganusov
Journal:  PLoS Comput Biol       Date:  2014-01-09       Impact factor: 4.475

Review 6.  Computational modeling of heterogeneity and function of CD4+ T cells.

Authors:  Adria Carbo; Raquel Hontecillas; Tricity Andrew; Kristin Eden; Yongguo Mei; Stefan Hoops; Josep Bassaganya-Riera
Journal:  Front Cell Dev Biol       Date:  2014-07-29

7.  Cellular and population plasticity of helper CD4(+) T cell responses.

Authors:  Gesham Magombedze; Pradeep B J Reddy; Shigetoshi Eda; Vitaly V Ganusov
Journal:  Front Physiol       Date:  2013-08-16       Impact factor: 4.566

8.  Synchronous versus asynchronous modeling of gene regulatory networks.

Authors:  Abhishek Garg; Alessandro Di Cara; Ioannis Xenarios; Luis Mendoza; Giovanni De Micheli
Journal:  Bioinformatics       Date:  2008-07-09       Impact factor: 6.937

9.  Molecular adjuvant interleukin-33 enhances the antifertility effect of Lagurus lagurus zona pellucida 3 DNA vaccine administered by the mucosal route.

Authors:  Y X Tu; X P Li; Z Kadir; F C Zhang
Journal:  Braz J Med Biol Res       Date:  2013-12-10       Impact factor: 2.590

  9 in total

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