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Record W4412948142 · doi:10.33540/3076

Non-self HLA-derived peptides presented by self HLA: implications for alloimmunity after transplantation

2025· dissertation· en· W4412948142 on OpenAlexaff
Emma T. M. Peereboom

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsAlloimmunityHuman leukocyte antigenTransplantationImmunologyMedicineAntigenInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.336
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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