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Virus Encoded MHC-Like Decoys Diversify the Inhibitory KIR Repertoire

Overview of attention for article published in PLoS Computational Biology, October 2013
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Title
Virus Encoded MHC-Like Decoys Diversify the Inhibitory KIR Repertoire
Published in
PLoS Computational Biology, October 2013
DOI 10.1371/journal.pcbi.1003264
Pubmed ID
Authors

Paola Carrillo-Bustamante, Can Keşmir, Rob J. de Boer

Abstract

Natural killer (NK) cells are circulating lymphocytes that play an important role in the control of viral infections and tumors. Their functions are regulated by several activating and inhibitory receptors. A subset of these receptors in human NK cells are the killer immunoglobulin-like receptors (KIRs), which interact with the highly polymorphic MHC class I molecules. One important function of NK cells is to detect cells that have down-regulated MHC expression (missing-self). Because MHC molecules have non polymorphic regions, their expression could have been monitored with a limited set of monomorphic receptors. Surprisingly, the KIR family has a remarkable genetic diversity, the function of which remains poorly understood. The mouse cytomegalovirus (MCMV) is able to evade NK cell responses by coding "decoy" molecules that mimic MHC class I. This interaction was suggested to have driven the evolution of novel NK cell receptors. Inspired by the MCMV system, we develop an agent-based model of a host population infected with viruses that are able to evolve MHC down-regulation and decoy molecules. Our simulations show that specific recognition of MHC class I molecules by inhibitory KIRs provides excellent protection against viruses evolving decoys, and that the diversity of inhibitory KIRs will subsequently evolve as a result of the required discrimination between host MHC molecules and decoy molecules.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
Germany 1 2%
Unknown 42 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 34%
Researcher 9 20%
Student > Bachelor 7 16%
Student > Master 4 9%
Professor > Associate Professor 2 5%
Other 4 9%
Unknown 3 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 41%
Immunology and Microbiology 9 20%
Biochemistry, Genetics and Molecular Biology 7 16%
Medicine and Dentistry 3 7%
Neuroscience 2 5%
Other 3 7%
Unknown 2 5%