A model of immune regulation as a consequence of randomized lymphocyte division and death times

Hawkins, Edwin D.; Turner, Marian L.; Dowling, Mark R.; Gend, C. van; Hodgkin, Philip D.

doi:10.1073/pnas.0700026104

Cited by 164 publications

(425 citation statements)

References 45 publications

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“…: 0.5 h). 27,51 Although individual stochastic simulations were diverse, the average behaviour of the stochastic model was similar to that of the deterministic model (as was to be expected).…”

Section: Activated Cellsupporting

confidence: 53%

Cell division curtails helper phenotype plasticity and expedites helper T‐cell differentiation

Ham

Boer

2012

Immunol Cell Biol

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Following activation by antigen, helper T cells differentiate into one of many effector phenotypes. Formulating mechanistic mathematical models combining regulatory networks at the transcriptional, translational and epigenetic level, we study how individual helper T cells may adopt their different phenotypes. For each cytokine phenotype, for example, T helper type 1 (Th1) and type 2 (Th2) cells, we find that the intracellular molecular network allows a cell to adopt one of the three states, which we interpret as naive, active and memory states. Cell division markedly speeds up the differentiation into a particular memory state because of DNA demythelation. In a memory state, cells readily resume production of the same cytokine they produced before. Using stochastic models we show that helper T-cell plasticity (that is, the ability to switch phenotype) is low during clonal expansion. Although most memory cells rapidly secrete the original cytokine upon restimulation, some adopt another phenotype and produce different cytokines, allowing for considerable diversity in the phenotypes that are adopted during a memory response. In summary, we show that helper T-cell division expedites cell differentiation by increasing DNA demethylation. We also show that plasticity is low during the clonal expansion phase, but that helper T cells may adopt alternative phenotypes during a memory response. Keywords: epigenetic regulation; helper T-cell phenotypes; mathematical modelling; transcriptional regulation For more than a century, biologists have studied the mechanisms that enable cells to encode genetic information, and how this information is passed on to the cell's progeny. 1 Besides the genetic DNA code of a cell that is replicated faithfully upon every cell division and therefore virtually identical in every cell within an individual, 2 additional 'epigenetic' information fixes differential cell development by prompting daughter cells to express a similar set of genes as their mother cell. 3 This facilitates and thus preserves the differentiation process that the mother cell and its ancestors have undergone as part of cellular diversification within an organism.A variety of biological systems have now been studied using highthroughput technologies, which provide data that allow a better understanding of information processing within a cell. [4][5][6][7] The availability of quantitative data and the apparent complexity of cellular regulatory networks have led to a data-driven mathematical modelling approach that is usually referred to as computational or systems biology. Mathematical models have revealed nonlinearity in genetic networks that allow cells to switch between states. Examples are the Lac operon 8 and cells of the haematopoietic lineage. [9][10][11][12] Recently, epigenetic regulation mediated by histone modification has been studied using similar mathematical models. [13][14][15] At the molecular level these mathematical models describe quite different genetic and epigenetic systems. Mathematically they share im...

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Section: Activated Cellsupporting

confidence: 53%

Cell division curtails helper phenotype plasticity and expedites helper T‐cell differentiation

Ham

Boer

2012

Immunol Cell Biol

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“…All lymphocytes were isolated from 8-to 16-week-old C57BL/6 mice. B cells were prepared, CFSE labelled and cultured as described 30,60 . Cell numbers were determined by reference to FACS calibration beads as described 29,31 .…”

Section: Methodsmentioning

confidence: 99%

“…However, the direct functional effect of HDACi on the protective B-cell response and consequence for cancer patients undergoing HDACi therapy, how specific classes of HDACi regulate components of the antibody response, and analysis of newly emerging HDACi such as panobinostat has not yet been determined. Recently, quantitative systems have emerged as a powerful tool for dissecting components that contribute to lymphocyte responses in vitro [29][30][31][32] . The aim of this study was to dissect how these compounds affected the separate components of the protective antibody response and shed light on the possible immunological consequences for cancer patients post HDACi therapy.…”

mentioning

confidence: 99%

Manipulation of B-cell responses with histone deacetylase inhibitors

et al. 2015

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Histone deacetylase inhibitors (HDACi) are approved for treating certain haematological malignancies, however, recent evidence also illustrates they are modulators of the immune system. In experimental models, HDACi are particularly potent against malignancies originating from the B-lymphocyte lineage. Here we examine the ability of this class of compounds to modify both protective and autoimmune antibody responses. In vitro, HDACi affect B-cell proliferation, survival and differentiation in an HDAC-class-dependent manner. Strikingly, treatment of lupus-prone Mrl/lpr mice with the HDACi panobinostat significantly reduces autoreactive plasma-cell numbers, autoantibodies and nephritis, while other immune parameters remain largely unaffected. Immunized control mice treated with panobinostat or the clinically approved HDACi vorinostat have significantly impaired primary antibody responses, but these treatments surprisingly spare circulating memory B cells. These studies indicate that panobinostat is a potential therapy for B-cell-driven autoimmune conditions and HDACi do not induce major long-term detrimental effects on B-cell memory.

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“…Cell cultures were supplemented with IL-2, because the PAg-driven expansion of TCRVg9 + gd cells requires an exogenous supply of this cytokine (46). We observed the CFSE was progressively diluted from days 5 to 7, in good match with a stochastic cell division model (44) (Fig. 1A).…”

Section: Resultsmentioning

confidence: 50%

“…The progressive dynamics of CFSE dilution by dividing TCRVg9 cells was measured after specified durations following PAg stimulation of PBMCs as above and compared with the theoretical cell growth Cyton model (CPSM software, kindly provided by P.D. Hodgkin) (44). The maturation stages of CFSE-labeled TCRVg9 cells (1 3 10 6 cells/ml) activated with BrHPP (100 nM) and rhIL-2 (10 IU/ ml) with or without TGF-b (2 ng/ml) were determined by simultaneous immunostaining of TCRVg9, CD27, and CD45RA surface markers and flow cytometry.…”

Section: Vg9 T Cell Divisionmentioning

confidence: 99%

Phosphoantigens Overcome Human TCRVγ9+ γδ Cell Immunosuppression by TGF-β: Relevance for Cancer Immunotherapy

Capietto

Martinet

Cendron

et al. 2010

The Journal of Immunology

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Human γδ cells expressing TCRVγ9 are HLA-unrestricted CTLs with high relevance for cancer immunotherapy. Many tumor cell types produce TGF-β, however, a cytokine strongly immunosuppressive for conventional T CD4, CD8, and NK cells. Whether TGF-β also inhibits TCRVγ9+ lymphocytes was unknown. Because phosphoantigens (PAgs), such as bromohydrin pyrophosphate, selectively activate the antitumor functions of TCRVγ9+ T cells, in this study, we investigated whether TGF-β modulates these functions. We report that TGF-β does not block activation of TCRVγ9+ T cells but inhibits their PAg/IL-2–induced proliferation and maturation into effector cells and finally reduces the cytotoxic activity of these γδ T cells when exposed to lymphoma target cells. TGF-β did not bias their differentiation pattern toward γδ Th17 or γδ regulatory T cells. Nevertheless, increasing doses of PAg stimulus countered TGF-β inhibition. So, although TGF-β impairs TCRVγ9+ γδ cells like other cytolytic lymphocytes, PAg alone or combined to therapeutic mAb has the ability to bypass its immunosuppressive activity.

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A model of immune regulation as a consequence of randomized lymphocyte division and death times

Cited by 164 publications

References 45 publications

Cell division curtails helper phenotype plasticity and expedites helper T‐cell differentiation

Cell division curtails helper phenotype plasticity and expedites helper T‐cell differentiation

Manipulation of B-cell responses with histone deacetylase inhibitors

Phosphoantigens Overcome Human TCRVγ9+ γδ Cell Immunosuppression by TGF-β: Relevance for Cancer Immunotherapy

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