title: A mathematical model for the reciprocal differentiation of T helper 17 cells and induced regulatory T cells.
abstract: The reciprocal differentiation of T helper 17 (T(H)17) cells and induced regulatory T (iT(reg)) cells plays a critical role in both the pathogenesis and resolution of diverse human inflammatory diseases. Although initial studies suggested a stable commitment to either the T(H)17 or the iT(reg) lineage, recent results reveal remarkable plasticity and heterogeneity, reflected in the capacity of differentiated effectors cells to be reprogrammed among T(H)17 and iT(reg) lineages and the intriguing phenomenon that a group of naïve precursor CD4(+) T cells can be programmed into phenotypically diverse populations by the same differentiation signal, transforming growth factor beta. To reconcile these observations, we have built a mathematical model of T(H)17/iT(reg) differentiation that exhibits four different stable steady states, governed by pitchfork bifurcations with certain degrees of broken symmetry. According to the model, a group of precursor cells with some small cell-to-cell variability can differentiate into phenotypically distinct subsets of cells, which exhibit distinct levels of the master transcription-factor regulators for the two T cell lineages. A dynamical control system with these properties is flexible enough to be steered down alternative pathways by polarizing signals, such as interleukin-6 and retinoic acid and it may be used by the immune system to generate functionally distinct effector cells in desired fractions in response to a range of differentiation signals. Additionally, the model suggests a quantitative explanation for the phenotype with high expression levels of both master regulators. This phenotype corresponds to a re-stabilized co-expressing state, appearing at a late stage of differentiation, rather than a bipotent precursor state observed under some other circumstances. Our simulations reconcile most published experimental observations and predict novel differentiation states as well as transitions among different phenotypes that have not yet been observed experimentally.
authors.affiliation: Genetics, Bioinformatics, and Computational Biology Program, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America.
authors.forename: Tian
authors.initials: T
authors.lastname: Hong
authors.forename: Jianhua
authors.initials: J
authors.lastname: Xing
authors.forename: Liwu
authors.initials: L
authors.lastname: Li
authors.forename: John J
authors.initials: JJ
authors.lastname: Tyson
mesh_terms: D002454:Cell Differentiation / Q000276:immunology*; D019295:Computational Biology; D003198:Computer Simulation; D051858:Forkhead Transcription Factors; D007249:Inflammation / Q000276:immunology; D020381:Interleukin-17 / Q000276:immunology; D015850:Interleukin-6 / Q000276:immunology; D018448:Models, Immunological; D057132:Nuclear Receptor Subfamily 1, Group F, Member 3 / Q000276:immunology; D010641:Phenotype; D015398:Signal Transduction; D006377:T-Lymphocytes, Helper-Inducer / Q000276:immunology*; D050378:T-Lymphocytes, Regulatory / Q000276:immunology*; D016212:Transforming Growth Factor beta / Q000494:pharmacology; D014212:Tretinoin / Q000276:immunology
chemical_list: D051858:Forkhead Transcription Factors; D020381:Interleukin-17; D015850:Interleukin-6; D057132:Nuclear Receptor Subfamily 1, Group F, Member 3; D016212:Transforming Growth Factor beta; D014212:Tretinoin