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Enregistrement W3082319527 · doi:10.1097/qad.0000000000002657

Regulation of natural killer cells: analog peptide handshake goes digital

2020· letter· en· W3082319527 sur OpenAlexaff
Michael D. Grant

Notice bibliographique

RevueAIDS · 2020
Typeletter
Langueen
DomaineImmunology and Microbiology
ThématiqueImmune Cell Function and Interaction
Établissements canadiensMemorial University of Newfoundland
Organismes subventionnairesnon disponible
Mots-clésMHC restrictionMajor histocompatibility complexBiologyMHC class IImmunologyT-cell receptorAntigenCell biologyT cellImmune system

Résumé

récupéré en direct d'OpenAlex

As an immunology graduate student, I fondly remember Tak Mak likening the T-cell receptor (TCR) to an outstretched hand interrogating each major histocompatibility complex (MHC) antigen it encountered for tactile evidence of viral subversion. Through many series of thoughtful experiments and ultimately, X-ray crystallography, the molecular basis for T-cell specificity and MHC restriction was revealed. This reinforced the distinction between natural killer (NK) cells and T cells and the sophistication of TCR selection and recognition processes elevated T cells into a more advanced evolutionary niche. Immunology texts referenced ‘missing self’ as key to NK cell function, a model which has expanded into integration of multiple positive and negative signals determining the outcome of NK cell engagement. Discovery of antigenic specificity for murine cytomegalovirus and possibly other antigens, evidence of memory and expanding knowledge of their regulation by killer cell immunoglobulin like receptors (KIR) has forced re-evaluation of NK cell nature [1]. Ironically, the longstanding illusion of MHC-independence segregating them from T cells has given way to indicate a surprisingly consistent focus on the same MHC molecules. Interaction between KIR and class I MHC molecules is offset from that of the TCR in such a way that NK cells experience thousands of distinct MHC molecules as only a handful of different subsets. Although this avoids the most polymorphic part of MHC molecules, affinities of KIR/class I MHC interactions within the same broad subsets vary for different allelic pairings. This variability creates a wide range of activating and inhibitory signal intensities through which KIR molecules influence NK cell activation and the epistatic interactions underlying susceptibility or resistance to certain pathogens. The proximity of KIR binding to the peptide binding site of MHC molecules led to speculation and demonstration that the strength of interaction between KIR and an MHC molecule could also be affected by the nature of peptide bound [2]. Decisive regulatory effects the cellular peptide repertoire can have on NK cell activation against viruses arose from identification of HIV and hepatitis C virus peptides that triggered higher levels of human histocompatibility-linked leukocyte antigen (HLA)-E expression [3,4]. Upregulation of HLA-E increases signaling through either negative (NKG2A) or positive (NKG2C) receptors, depending on what the NK cell expressed or in some cases, on the peptide itself. Human cytomegalovirus encodes several peptides that bind HLA-E and trigger preferential expansion of NKG2C-expressing NK cells [5,6]. Many examples of peptide dependent modulation of KIR affinity for MHC have been reported, thus, peptide specificity and MHC dependence, the very things thought to separate them from T cells, are now considered integral to NK cell regulation and adaptation through KIR and other regulatory receptors [7–9]. In this issue, Ziegler et al.[10] conduct an in depth comparison of the total peptide repertoire presented by class I HLA molecules on resting, activated and HIV-infected activated CD4+ T cells. As certain MHC class I/KIR epistatic interactions can influence the course of viral infections, including HIV infection, there is a strong rationale for investigating the effect of viral infection on the peptide repertoire expressed and the subsequent impact of changes in the peptide repertoire on KIR/HLA binding [9]. The authors prepared cell lysates, purified class I MHC molecules and analyzed peptides bound to the MHC class I molecules by liquid chromatography coupled with tandem mass spectrometry. The experiments were repeated 6 months apart with the same source of CD4+ T cells to demonstrate reproducibility. Over 12 000 peptides binding to HLA-A, B and C molecules were identified with 1369 unique to nonactivated CD4+ T cells, 4716 unique to activated CD4+ T cells and 3962 unique to HIV-infected CD4+ T cells. These data demonstrate broad lability in the expressed peptide repertoire and distinctive influences of activation and HIV infection. An interesting point the authors noted, confirming previous studies, was that less than 0.2% of the peptides expressed on HIV-infected cells came from HIV [11]. Thus, HIV infection affects peptide generation and or transport globally, mostly through mechanisms other than production of HIV-encoded peptides that compete with self peptides. To determine how this might affect NK cell recognition of HIV-infected cells, the authors focused on HLA-C∗03:04, the ligand for inhibitory KIR2DL3. Using HLA-C∗03:04-transfected 721 221 cells and KIR2DL3-Fc fusion protein, they showed that the most abundant peptide bound to HLA-C∗03:04 on uninfected cells mediated strong binding of KIR2DL3 while the most abundant peptide bound to HLA-C∗03:04 on HIV-infected cells mediated weaker binding. These results were confirmed using KIR2DL3γ Jurkat reporter cells and the molecular structural basis for the difference was modeled with atomistic molecular dynamics simulation. While their focus on a single inhibitory KIR/MHC ligand pair necessarily oversimplifies the impact on NK cell recognition, this reductionist approach provides an important illustration of another facet of the arms race between infectious agents and the immune system. In this case, NK cells are potentially skewed toward activation against HIV-infected cells by the dominant changes that occur in peptides presented by HLA-C∗03:04. If this imposed a strong selective effect against a highly mutating virus like HIV, there would be pressure for emergence of variants shifting the peptide (s) bound to HLA-C∗03:04 toward increasing the affinity of KIR2DL3. Significantly, this form of selection was reported in a previous study involving KIR2DL2 [12]. The article in this issue shows that escape is not necessarily through a mutant HIV peptide that itself binds to HLA-C, but potentially by an HIV variant influencing which host peptide dominates binding to HLA-C. Growing appreciation for the complexity of NK cell recognition and regulation strategies contrasts with the static picture of TCR specificity for a single peptide/HLA pairing. Although the idiosyncratic CD8+ T-cell response is often effective, interrogation of MHC molecules by a single TCR is unidimensional relative to an NK cell's use of a panel of receptors to interpret and integrate meaning from the entire array of different peptide/HLA molecules present on a target cell surface. The ability of single NK cells to process information on the nature of a target cell through multiple HLA/peptide interactions across a broad range of affinities empowers a versatility unavailable to adaptive immune cells governed by clonotypic receptors. Acknowledgements Conflicts of interest There are no conflicts of interest.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,394
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,009
Tête enseignante GPT0,205
Écart entre enseignants0,196 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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