title: The duration of T cell stimulation is a critical determinant of cell fate and plasticity.
abstract: Variations in T cell receptor (TCR) signal strength, as indicated by differential activation of downstream signaling pathways, determine the fate of naïve T cells after encounter with antigen. Low-strength signals favor differentiation into regulatory T (T(reg)) cells containing the transcription factor Foxp3, whereas high-strength signals favor generation of interleukin-2-producing T helper (T(H)) cells. We constructed a logic circuit model of TCR signaling pathways, a major feature of which is an incoherent feed-forward loop involving both TCR-dependent activation of Foxp3 and its inhibition by mammalian target of rapamycin (mTOR), leading to the transient appearance of Foxp3(+) cells under T(H) cell-generating conditions. Experiments confirmed this behavior and the prediction that the immunosuppressive cytokine TGF-β (transforming growth factor-β) could generate T(reg) cells even during continued Akt-mTOR signaling. We predicted that sustained mTOR activity could suppress FOXP3 expression upon TGF-β removal, suggesting a possible mechanism for the experimentally observed instability of Foxp3(+) cells. Our model predicted, and experiments confirmed, that transient stimulation of cells with high-dose antigen generated T(H), T(reg), and nonactivated cells in proportions depending on the duration of TCR stimulation. Experimental analysis of cells after antigen removal identified three populations that correlated with these T cell fates. Further analysis of simulations implicated a negative feedback loop involving Foxp3, the phosphatase PTEN, and Akt-mTOR in determining fate. These results suggest that there is a critical time after TCR stimulation during which heterogeneity in the differentiating population of cells leads to increased plasticity of cell fate.
authors.affiliation: 1Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA.
authors.forename: Natasa
authors.initials: N
authors.lastname: Miskov-Zivanov
authors.forename: Michael S
authors.initials: MS
authors.lastname: Turner
authors.forename: Lawrence P
authors.initials: LP
authors.lastname: Kane
authors.forename: Penelope A
authors.initials: PA
authors.lastname: Morel
authors.forename: James R
authors.initials: JR
authors.lastname: Faeder
mesh_terms: D000704:Analysis of Variance; D002454:Cell Differentiation / Q000276:immunology*; D003198:Computer Simulation; D003713:Dendritic Cells / Q000276:immunology; D025461:Feedback, Physiological / Q000502:physiology*; D005434:Flow Cytometry; D051858:Forkhead Transcription Factors / Q000276:immunology / Q000378:metabolism; D005786:Gene Expression Regulation / Q000276:immunology*; D006801:Humans; D018448:Models, Immunological; D011948:Receptors, Antigen, T-Cell / Q000378:metabolism*; D015398:Signal Transduction / Q000276:immunology*; D016176:T-Lymphocyte Subsets / Q000276:immunology*; D058570:TOR Serine-Threonine Kinases / Q000276:immunology / Q000378:metabolism; D013997:Time Factors; D016212:Transforming Growth Factor beta / Q000378:metabolism
chemical_list: C418974:FOXP3 protein, human; D051858:Forkhead Transcription Factors; D011948:Receptors, Antigen, T-Cell; D016212:Transforming Growth Factor beta; C546842:MTOR protein, human; D058570:TOR Serine-Threonine Kinases