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Record W4379769158 · doi:10.1097/tp.0000000000004659

ABO Antibody Development, a New Look at a Very Old Question

2023· letter· en· W4379769158 on OpenAlexaboutno aff
Emmanuel Zorn

Bibliographic record

VenueTransplantation · 2023
Typeletter
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of Health
KeywordsABO blood group systemAntibodyImmunologyAntigenImmune systemTransfusion medicineTransplantationBiologyIsoantibodiesBlood transfusionMedicineInternal medicine

Abstract

fetched live from OpenAlex

The ABO blood group system is central to blood transfusion and transplantation medicine. The system is determined by the presence or absence of the A and B antigens on the surface of red blood cells and corresponding antibodies reactive to these antigens in the plasma. The ABO system was discovered more than a century ago by Karl Landsteiner, for which he received the Nobel Prize in Physiology or Medicine in 1930.1 Yet to this day, the mechanism underlying the development of ABO antibodies remains obscure. Early studies suggested that immune reactions to the developing microbiota in early infancy generated cross-reactive antibodies to blood group antigens.2 This common view explained why infants and toddlers up to 14 mo of age whose ABO antibodies are not fully developed can successfully receive ABO-incompatible heart transplants, as documented by Dr Lori West in a seminal study published in 2001.3 However, although the timing of ABO antibody production is well documented, its dependency on exposure to microbial antigenic determinants is still hypothetical. The study by Dr Adam and his colleagues of the Edmonton group led by Dr West published in this issue of Transplantation sheds new light on this long-standing question.4 The investigation, conducted in a mouse model, allowed the authors to address and essentially single out the contribution of CD4+ T cells, microbiota, and sex in the natural and induced development of antibodies to blood group antigen type A. The study first revealed that natural production of anti-A antibodies did not require CD4+ T cells. On the contrary, CD4+ T cells controlled and weakened the development of anti-A antibodies. This effect had previously been reported for anti-gal antibodies but not for anti-ABO antibodies.5 Secondly and unexpectedly, the capacity of CD4+ T cells to mitigate the development of natural anti-A antibodies appeared more pronounced in females than in males. This intriguing effect of sex had not been reported before. It is possible that sex hormones influence the development of anti-A antibodies as was once described for the production of innate antibodies.6 This observation is also in line with previous studies that have reported sex differences in the immune response to various infections and vaccinations.7 Finally, using either germ-free or antibiotic-treated mice, the authors showed that spontaneous development of anti-A antibodies did not require microbiota in several mouse strains. Quite the opposite, the presence of microbiota reduced the formation of anti-A antibodies in 2 out of 3 strains, with a more pronounced effect in female mice. This is a surprising observation because it contradicts the previous notion that microbiota is directly responsible for the development of immune responses. The authors suggested that the mitigating effect of microbiota may result from the generation of specific CD4+ T-cell populations with regulatory properties that would modulate antibody generation. Such an explanation is consistent with the capacity of regulatory T cells to control antibody responses as Dr Sakaguchi reported in his first description of regulatory T cells in 1995.8 Contrasting with their natural development, the induced production of anti-A antibodies after immunization with human A+ red blood cells required CD4+ T-cell populations and did not show any noticeable differences between females and males. This latter observation indicates that the generation of ABO antibodies through immunization may be distinct from their spontaneous development. Overall, this study represents a significant contribution to our understanding of the development of ABO antibodies and has important implications for blood transfusion and transplantation medicine. A potential limitation of the study is that it was conducted in a mouse model, although it is reasonable to assume that the findings related to the spontaneous development of anti-A antibodies can be extrapolated to humans. In contrast, the experimental generation of anti-A-antibodies through immunization may not fully replicate the production of ABO antibodies in humans. The fact that the microbiota does not influence the generation of anti-A antibodies is puzzling. One cannot help but wonder what antigens, if not those exposed on microbes, elicit ABO antibodies in early life. Other candidates include food antigens toward which humoral responses develop during the first 2 y of life.9 Alternatively, we may ponder the possibility that ABO antibodies are not induced by exposure to environmental antigens but rather result from antibody responses to self-determinants developing in childhood. Nevertheless, by bringing a new perspective on this known but still obscure phenomenon, the study by Adam and Dr West’s group reminds us once again to be cautious about widely disseminated yet unproven concepts.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.398
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.021
GPT teacher head0.285
Teacher spread0.264 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2023
Admission routes1
Has abstractyes

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