Identification of Two IgD+ B Cell Populations in Channel Catfish, <i>Ictalurus punctatus</i>
Bibliographic record
Abstract
Channel catfish Ictalurus punctatus express two Ig isotypes: IgM and IgD. Although catfish IgM has been extensively studied at the functional and structural levels, much less is known about IgD. In this study, IgM(+)/IgD(+) and IgM(-)/IgD(+) catfish B cell populations were identified through the use of anti-IgM and anti-IgD mAbs. Catfish IgM(+)/IgD(+) B cells are small and agranular. In contrast, IgM(-)/IgD(+) B cells are larger and exhibit a plasmablast morphology. The use of cell sorting, flow cytometry, and RT-PCR demonstrated that IgD(+) B cell expression varies among individuals. For example, some catfish have <5% IgM(-)/IgD(+) B cells in their PBLs, whereas in others the IgM(-)/IgD(+) B cell population can represent as much as 72%. Furthermore, IgD expressed by IgM(-)/IgD(+) B cells preferentially associates with IgL σ. Comparatively, IgM(+)/IgD(+) B cells can express any of the four catfish IgL isotypes. Also, transfection studies show that IgD functions as a typical BCR, because Igδ-chains associate with CD79a and CD79b molecules, and all membrane IgD transcripts from sorted IgM(-)/IgD(+) B cells contain viable VDJ rearrangements, with no bias in family member usage. Interestingly, all secreted IgD transcripts from IgM(+)/IgD(+) and IgM(-)/IgD(+) B cells were V-less and began with a leader spliced to Cδ1. Importantly, transfection of catfish clonal B cells demonstrated that this leader mediated IgD secretion. Together, these findings imply that catfish IgM(-)/IgD(+) B cells likely expand in response to certain pathogens and that the catfish IgD Fc-region, as has been suggested for human IgD, may function as a pattern recognition molecule.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".