Evidence map›Paper›PMID 28695292›Full record

ArticleImmunogenetics2018

Specificity of inhibitory KIRs enables NK cells to detect changes in an altered peptide environment.

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

Abstract read
In one paragraph

Article in Immunogenetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Paola Carrillo-BustamanteTheoretical Biology and Bioinformatics, Department of Biology, Utrecht University, Utrecht, The Netherlands. paola.carrillo-bustamante@bioquant.uni-heidelberg.de.
Rob J de BoerTheoretical Biology and Bioinformatics, Department of Biology, Utrecht University, Utrecht, The Netherlands.
Can KeşmirTheoretical Biology and Bioinformatics, Department of Biology, Utrecht University, Utrecht, The Netherlands.

Funding

Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NL) 635.100.025
6 · The paper itself

Abstract

The activity of natural killer (NK) cells is tightly regulated by inhibitory and activating receptors. Inhibitory killer immunoglobulin-like receptors (iKIRs) survey the surface of target cells by monitoring the expression of human leukocyte antigen (HLA) class I. The binding of iKIRs has been shown to be sensitive to the peptides presented by HLA class I, implying that iKIRs have the ability to detect the changes in the repertoire of peptide-HLA class I complexes (pHLA), a process occurring during viral infection and in tumor cells. To study how the pHLA repertoire changes upon infection, and whether an iKIR is able to detect these changes, we study peptides eluted from cells prior and after infection with measles virus (MV). Remarkably, most changes in the repertoire of potential iKIR ligands are predicted to be caused by the altered expression of self-peptides. We show that an iKIR can detect these changes in the presented peptides only if it is sufficiently specific, e.g., if iKIRs can distinguish between different amino acids in the contact residues (e.g., position 7 and 8). Our analysis further indicates that one single iKIR per host is not sufficient to detect changes in the peptide repertoire, suggesting that a multigene family encoding for different iKIRs is required for successful peptide recognition.

Indexed as

Amino AcidsComputer SimulationHistocompatibility Antigens Class IHumansKiller Cells, NaturalLigandsMeasles virusPeptidesReceptors, KIRAmino AcidsHistocompatibility Antigens Class ILigandsPeptidesReceptors, KIRKIRNK cellsPeptide sensitivityViral infection

Identifiers

PMID28695292
PMCPMC5775373

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.