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Donor T Cell Gene Expression and GVHD.

2008· article· en· W2595243603 on OpenAlexaff
Claude Perreault

Bibliographic record

VenueBlood · 2008
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsInstitute for Research in Immunology and Cancer
Fundersnot available
KeywordsImmunologyCD8BiologyHematopoietic stem cell transplantationGraft-versus-host diseaseMinor histocompatibility antigenHaematopoiesisT cellTransplantationMajor histocompatibility complexStem cellImmune systemGeneticsMedicineInternal medicine

Abstract

fetched live from OpenAlex

GVHD is initiated by donor T cell responses to host alloantigens. However, the occurrence and severity of GVHD are not determined solely by the level of histoincompatibility between donor and recipient. Two MHC-identical subjects will display over 50 minor histocompatibility antigen differences. If histoincompatibility is sufficient for triggering GVHD, the rate of GVHD in MHC-matched recipients of allogeneic hematopoietic cell transplantation (HCT) that receive no immunosuppressive agents should be 100%. Under these conditions, however, GVHD is found in only 50% and 73% of mouse and human recipients, respectively. Histoincompatibility is thus necessary but not sufficient to elicit GVHD. We tested the hypothesis that some donors may be “stronger alloresponders” than others, and consequently more likely to elicit GVHD. To this end, we studied the gene expression profiles of CD4 and CD8 T cells from 50 HCT donors using microarrays and qRT-PCR. We found that gene expression profiling before HCT was able to distinguish those donors whose cells caused GVHD from those whose cells did not. The “dangerous donor” trait (GVHD+ recipient) is under polygenic control and is shaped by the activity of genes that regulate TGF-β signaling and cell proliferation. The donor gene profile defined on day 0 shows strong correlation with that of recipient CD4 and CD8 T cells harvested one year post-AHCT. The latter correlation provides compelling evidence that a significant portion of the differential gene profiles between GVHD+ and GVHD– donors is imprinted at the hematopoietic stem cell level. Moreover, stability of the gene expression profiles over a one-year period suggests that the profiles result from inherited genetic traits as opposed to environmental factors. The gene with the best GVHD-predictive accuracy was SMAD3, a key component of the TGF-β pathway. By testing a cohort of 450 subjects using qRT-PCR, we found that amounts of SMAD3 transcripts varied over a 6-fold range. In mice and humans, SMAD3 is constitutively activated (as evidenced by phosphorylation and accumulation in the nucleus) in many leukocyte subsets. We found in mice that induction of TGF-β signaling in donor T cells is an early event following AHCT and that Smad3-deficient donors trigger more severe GVHD than wild-type littermates. These findings strongly suggest that the donor gene expression profile has a dominant influence on the occurrence of GVHD. In allogeneic HCT, the ability to discriminate strong and weak alloresponders using gene expression profiling could help select low-risk donors and permit tailoring GVHD prophylaxis regimens according to the probability of GVHD occurrence.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.230
Teacher spread0.214 · 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 designObservational
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
Published2008
Admission routes1
Has abstractyes

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