Browsing by Author "Hong, Tian"

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    A framework for understanding heterogeneous differentiation of CD4⁺ T cells
    Hong, Tian (Virginia Tech, 2013-08-05)
    CD4+ T cells are a group of lymphocytes that play critical roles in the immune system. By releasing cytokines, CD4+ T cells regulate other immune cells for maximizing the efficiency of the system. Naive CD4+ T cells are activated and become mature upon engagement with antigens, and the mature CD4+ T cells have several subsets, which play diverse regulatory functions. For the past two decades, our understanding of CD4+ T cells has been advanced through the studies on the differentiation process and the lineage specification of various subsets of these cells. Although in most experimental studies of CD4+ T cells, researchers focused on how transcription factors and signaling molecules influence the differentiation of a particular subset of these cells, many evidence have shown that the differentiation of CD4+ T cells can be heterogeneous in terms of the phenotypes of the cells involved. This dissertation describes a framework that uses mathematical models of the dynamics of the signaling pathways to explain heterogeneous differentiation. We show that the mutual inhibitions among the master regulators govern the formation of multi-stability behavior, which in turn gives rise to heterogeneous differentiation. The framework can be applied to systems with two or more master regulators, and models based on the framework can make specific predictions about heterogeneous differentiations. In addition, this dissertation describes an experimental study on CD4+ T cell differentiation. Being part of the adaptive immune system, the differentiation of CD4+ T cells was previously known to be induced by the signals from the innate immune cells. However, the expression of Toll-like receptor in CD4+ T cells suggests that microbial products can also influence the differentiation directly. Using an in vitro cell differentiation approach, we show that the differentiation and proliferation of CD4+ T cells can be influenced by lipopolysaccharide under the condition that would favor the differentiation of induced regulatory T cells. These theoretical and experimental studies give novel insights on how CD4+ T cells differentiate in response to pathogenic challenges, and help to gain deeper understanding of regulatory mechanisms of the complex immune system.
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    A Mathematical Model for the Reciprocal Differentiation of T Helper 17 Cells and Induced Regulatory T Cells
    Hong, Tian; Xing, Jianhua; Li, Liwu; Tyson, John J. (PLOS, 2011-07-01)
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    A simple theoretical framework for understanding heterogeneous differentiation of CD4(+) T cells
    Hong, Tian; Xing, Jianhua; Li, Liwu; Tyson, John J. (Biomed Central, 2012-06-14)
    Background CD4+ T cells have several subsets of functional phenotypes, which play critical yet diverse roles in the immune system. Pathogen-driven differentiation of these subsets of cells is often heterogeneous in terms of the induced phenotypic diversity. In vitro recapitulation of heterogeneous differentiation under homogeneous experimental conditions indicates some highly regulated mechanisms by which multiple phenotypes of CD4+ T cells can be generated from a single population of naïve CD4+ T cells. Therefore, conceptual understanding of induced heterogeneous differentiation will shed light on the mechanisms controlling the response of populations of CD4+ T cells under physiological conditions. Results We present a simple theoretical framework to show how heterogeneous differentiation in a two-master-regulator paradigm can be governed by a signaling network motif common to all subsets of CD4+ T cells. With this motif, a population of naïve CD4+ T cells can integrate the signals from their environment to generate a functionally diverse population with robust commitment of individual cells. Notably, two positive feedback loops in this network motif govern three bistable switches, which in turn, give rise to three types of heterogeneous differentiated states, depending upon particular combinations of input signals. We provide three prototype models illustrating how to use this framework to explain experimental observations and make specific testable predictions. Conclusions The process in which several types of T helper cells are generated simultaneously to mount complex immune responses upon pathogenic challenges can be highly regulated, and a simple signaling network motif can be responsible for generating all possible types of heterogeneous populations with respect to a pair of master regulators controlling CD4+ T cell differentiation. The framework provides a mathematical basis for understanding the decision-making mechanisms of CD4+ T cells, and it can be helpful for interpreting experimental results. Mathematical models based on the framework make specific testable predictions that may improve our understanding of this differentiation system.