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Predictable Irreversible Switching Between Acute and Chronic Inflammation

Overview of attention for article published in Frontiers in immunology, August 2018
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Title
Predictable Irreversible Switching Between Acute and Chronic Inflammation
Published in
Frontiers in immunology, August 2018
DOI 10.3389/fimmu.2018.01596
Pubmed ID
Authors

Abulikemu Abudukelimu, Matteo Barberis, Frank A. Redegeld, Nilgun Sahin, Hans V. Westerhoff

Abstract

Many a disease associates with inflammation. Upon binding of antigen-antibody complexes to immunoglobulin-like receptors, mast cells release tumor necrosis factor-α and proteases, causing fibroblasts to release endogenous antigens that may be cross reactive with exogenous antigens. We made a predictive dynamic map of the corresponding extracellular network. In silico, this map cleared bacterial infections, via acute inflammation, but could also cause chronic inflammation. In the calculations, limited inflammation flipped to strong inflammation when cross-reacting antigen exceeded an "On threshold." Subsequent reduction of the antigen load to below this "On threshold" did not remove the strong inflammation phenotype unless the antigen load dropped below a much lower and subtler "Off" threshold. In between both thresholds, the network appeared caught either in a "low" or a "high" inflammatory state. This was not simply a matter of bi-stability, however, the transition to the "high" state was temporarily revertible but ultimately irreversible: removing antigen after high exposure reduced the inflammatory phenotype back to "low" levels but if then the antigen dosage was increased only a little, the high inflammation state was already re-attained. This property may explain why the high inflammation state is indeed "chronic," whereas only the naive low-inflammation state is "acute." The model demonstrates that therapies of chronic inflammation such as with anti-IgLC should require fibroblast implantation (or corresponding stem cell activation) for permanence in order to redress the irreversible transition.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 57 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 19%
Student > Bachelor 9 16%
Student > Master 7 12%
Student > Doctoral Student 3 5%
Researcher 2 4%
Other 4 7%
Unknown 21 37%
Readers by discipline Count As %
Medicine and Dentistry 9 16%
Biochemistry, Genetics and Molecular Biology 7 12%
Agricultural and Biological Sciences 3 5%
Pharmacology, Toxicology and Pharmaceutical Science 3 5%
Neuroscience 3 5%
Other 6 11%
Unknown 26 46%