Modeling the T cell immune response: a fascinating challenge

Morel, Penelope A.; Faeder, James R.; Hawse, William F.; Miskov-Zivanov, Natasa

doi:10.1007/s10928-014-9376-y

Cited by 7 publications

(8 citation statements)

References 145 publications

(167 reference statements)

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“…Although the effect on individual gene segments can be considered small compared with the genetic bias, the cumulative effect on the repertoire could be significant. These results suggest that any model of recombination, which could be very useful in testing hypotheses about the formation of the repertoire30, should account for the effects of epigenetics and chromosomal bias.…”

Section: Discussionmentioning

confidence: 91%

Individual heritable differences result in unique cell lymphocyte receptor repertoires of naïve and antigen-experienced cells

et al. 2016

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The adaptive immune system's capability to protect the body requires a highly diverse lymphocyte antigen receptor repertoire. However, the influence of individual genetic and epigenetic differences on these repertoires is not typically measured. By leveraging the unique characteristics of B, CD4+ T and CD8+ T-lymphocyte subsets from monozygotic twins, we quantify the impact of heritable factors on both the V(D)J recombination process and on thymic selection. We show that the resulting biases in both V(D)J usage and N/P addition lengths, which are found in naïve and antigen experienced cells, contribute to significant variation in the CDR3 region. Moreover, we show that the relative usage of V and J gene segments is chromosomally biased, with ∼1.5 times as many rearrangements originating from a single chromosome. These data refine our understanding of the heritable mechanisms affecting the repertoire, and show that biases are evident on a chromosome-wide level.

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Section: Discussionmentioning

confidence: 91%

Individual heritable differences result in unique cell lymphocyte receptor repertoires of naïve and antigen-experienced cells

et al. 2016

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“…The ability to quickly detect and avoid engagement with apoptotic targets would also be highly advantageous for cytotoxic effector cells, engaged in a race with exponentially replicating virus (62,90,91). Lymphocyte-mediated cytolysis entails receptor detection of surface target cognate antigen, a time-consuming process of membrane spreading and probing for rare (as few as 1-5) surface cognate antigens.…”

Section: Cytotoxic Lymphocyte Recognition and Avoidance Of Apoptotic mentioning

confidence: 99%

Recognition of Apoptotic Cells by Viruses and Cytolytic Lymphocytes: Target Selection in the Fog of War

Schwartz

Iyengar

2020

Viral Immunology

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Viruses and cytolytic lymphocytes operate in an environment filled with dying and dead cells, and cell fragments. For viruses, irreversible fusion with doomed cells is suicide. For cytotoxic T lymphocyte and natural killer cells, time and limited lytic resources spent on apoptotic targets is wasteful and may result in death of the host. We make the case that the target membrane cytoskeleton is the best source of information regarding the suitability of potential targets for engagement for both viruses and lytic effector cells, and we present experimental evidence for detection of apoptotic cells by HIV, without loss of infectivity.

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“…1) [11]. Both approaches have been successfully applied to the immune system and have generated novel insights into many immune processes including vaccine responses [12,13], IL-2 and Tregs [14], IL-7 receptor signaling [15] IgE receptor [16] and TCR signaling pathways [17,18]. This review will discuss recent examples in which each of these modeling approaches have shed light on the complex world of cytokines and their functions in the immune response.…”

Section: Introductionmentioning

confidence: 99%

Demystifying the cytokine network: Mathematical models point the way

2017

Self Cite

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Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed.

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Modeling the T cell immune response: a fascinating challenge

Cited by 7 publications

References 145 publications

Individual heritable differences result in unique cell lymphocyte receptor repertoires of naïve and antigen-experienced cells

Individual heritable differences result in unique cell lymphocyte receptor repertoires of naïve and antigen-experienced cells

Recognition of Apoptotic Cells by Viruses and Cytolytic Lymphocytes: Target Selection in the Fog of War

Demystifying the cytokine network: Mathematical models point the way

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