Non-self HLA-derived peptides presented by self HLA: implications for alloimmunity after transplantation
Notice bibliographique
Résumé
HLA matching plays an important role in the success of a kidney transplantation. A mismatch between donor and recipient could lead to alloreactivity, which may result in rejection and even graft failure. In this thesis, we further explore how non-self HLA presented by self HLA may lead to alloimmunity in allogeneic setting, with a focus on kidney transplantation. In the first part of the thesis, we investigate how donor HLA-derived CD4+ T-cell epitopes are associated with transplant outcome. Such CD4+ T-cell epitopes, consisting of peptides derived from non-self donor HLA presented by HLA class II molecules of the recipient, can be predicted with the PIRCHE-II algorithm. In this thesis, we show that the number of predicted donor HLA-derived CD4+ T-cell epitopes associates with T-cell-mediated rejection after kidney transplantation. We also examined whether a higher potential for CD4+ T-cell memory increases the risk of graft failure in pre-immunized kidney transplant recipients. As a proxy for T-cell memory, we calculated the overlap between immunizing HLA- and donor HLA-derived peptides that can be presented by recipient HLA class II. We observed that pre-immunized recipients with a higher number of such overlapping peptides had a significantly increased risk of developing graft failure after transplantation. We also investigated CD4+ T-cell alloreactivity in another allogeneic setting, namely pregnancy. We observed that untreated women with secondary recurrent pregnancy loss who gave birth during the study had more predicted paternal HLA-derived CD4+ T-cell epitopes compared to women who experienced another pregnancy loss. In addition, these women had more overlapping peptides between the two paternal haplotypes compared to women who experienced another pregnancy loss, suggesting a protective role for such T-cell epitopes in women experiencing recurrent pregnancy loss. When matching for HLA, we mainly focus on the mature HLA proteins as presented on the cell surface. However, the leader peptides of HLA class I alleles may also affect the outcome of a transplantation. In the second part of this thesis, we have studied these leader peptides. As several HLA-A and -C leader peptides are identical to a peptide produced by specific CMV strains, we hypothesized that CMV-seropositive recipients may have generated immunological memory against this peptide, which may be reactivated by this same peptide present in the transplant. We found that CMV-seropositive kidney transplant recipients without such a leader peptide, who are transplanted with a donor with the leader peptide, have an increased risk of developing T-cell-mediated rejection early after transplantation. In addition, we show that that CMV-seropositive kidney transplant recipients with a specific variant of the HLA-B leader peptide also have an increased risk of early TCMR. Combined, the results presented in this thesis highlight an important role for both CD4+ T-cell epitopes and HLA leader peptides. The results contribute to a better understanding of the immune response against peptides derived from non-self HLA that are presented in self HLA. In the future, the insights from this work may help improve donor organ allocation and enable a more accurate assessment of the risk of rejection after transplantation.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».