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Simulation of Stimulation: Cytokine Dosage and Cell Cycle Crosstalk Driving Timing-Dependent T Cell Differentiation

Overview of attention for article published in Frontiers in Physiology, August 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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Title
Simulation of Stimulation: Cytokine Dosage and Cell Cycle Crosstalk Driving Timing-Dependent T Cell Differentiation
Published in
Frontiers in Physiology, August 2018
DOI 10.3389/fphys.2018.00879
Pubmed ID
Authors

Matteo Barberis, Tomáš Helikar, Paul Verbruggen

Abstract

Triggering an appropriate protective response against invading agents is crucial to the effectiveness of human innate and adaptive immunity. Pathogen recognition and elimination requires integration of a myriad of signals from many different immune cells. For example, T cell functioning is not qualitatively, but quantitatively determined by cellular and humoral signals. Tipping the balance of signals, such that one of these is favored or gains advantage on another one, may impact the plasticity of T cells. This may lead to switching their phenotypes and, ultimately, modulating the balance between proliferating and memory T cells to sustain an appropriate immune response. We hypothesize that, similar to other intracellular processes such as the cell cycle, the process of T cell differentiation is the result of: (i) pleiotropy (pattern) and (ii) magnitude (dosage/concentration) of input signals, as well as (iii) their timing and duration. That is, a flexible, yet robust immune response upon recognition of the pathogen may result from the integration of signals at the right dosage and timing. To investigate and understand how system's properties such as T cell plasticity and T cell-mediated robust response arise from the interplay between these signals, the use of experimental toolboxes that modulate immune proteins may be explored. Currently available methodologies to engineer T cells and a recently devised strategy to measure protein dosage may be employed to precisely determine, for example, the expression of transcription factors responsible for T cell differentiation into various subtypes. Thus, the immune response may be systematically investigated quantitatively. Here, we provide a perspective of how pattern, dosage and timing of specific signals, called interleukins, may influence T cell activation and differentiation during the course of the immune response. We further propose that interleukins alone cannot explain the phenotype variability observed in T cells. Specifically, we provide evidence that the dosage of intercellular components of both the immune system and the cell cycle regulating cell proliferation may contribute to T cell activation, differentiation, as well as T cell memory formation and maintenance. Altogether, we envision that a qualitative (pattern) and quantitative (dosage) crosstalk between the extracellular milieu and intracellular proteins leads to T cell plasticity and robustness. The understanding of this complex interplay is crucial to predict and prevent scenarios where tipping the balance of signals may be compromised, such as in autoimmunity.

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X Demographics

The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 84 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 23%
Researcher 11 13%
Student > Master 10 12%
Student > Doctoral Student 8 10%
Student > Bachelor 8 10%
Other 9 11%
Unknown 19 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 20%
Agricultural and Biological Sciences 9 11%
Immunology and Microbiology 9 11%
Chemistry 6 7%
Medicine and Dentistry 6 7%
Other 16 19%
Unknown 21 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 May 2023.
All research outputs
#5,145,478
of 25,257,066 outputs
Outputs from Frontiers in Physiology
#2,549
of 15,523 outputs
Outputs of similar age
#89,748
of 337,333 outputs
Outputs of similar age from Frontiers in Physiology
#120
of 477 outputs
Altmetric has tracked 25,257,066 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 15,523 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.1. This one has done well, scoring higher than 83% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 337,333 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 477 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.