Blood and colostral IgM and IgG B cell repertoires in high, average, and low immune responder Holstein Friesian cows and heifers
Bibliographic record
Abstract
In dairy cattle, genetic selection for higher antibody-mediated (AMIR) and cell-mediated (CMIR) immune responses can enhance disease resistance. Cattle produce a unique subset of B cells with B cell receptors with ultralong complementarity determining regions 3 (CDR3). Antibodies with these specialized structures have superior virus neutralization characteristics. Published studies of B cell receptors with ultralong CDR3s in dairy cattle have been limited by the number of animals examined (1–4 animals in each study), and by varying breeds and ages. The objective of this study was to assess the percentage of IgM and IgG sequences with ultralong CDR3s, and gene usage in blood and colostral lymphocytes from cows classified as high, average, and low immune responders based on their estimated breeding values. B lymphocytes were isolated from the blood of 14 heifers and 7 cows. In addition, cells were isolated from colostrum of the 7 cows. RNA was extracted, cDNA was produced, and IgM and IgG transcripts were amplified using polymerase chain reactions. Amplicons were sequenced using Oxford Nanopore long-read sequencing. In sequences derived from blood B cells, AMIR estimated breeding values were significantly and positively associated with higher percentages of IgG ultralong CDR3 sequences. High AMIR cows (n = 3) also produced colostrum with a significantly greater percentage of IgG ultralong CDR3 sequences (18.0 %) than average AMIR cows (n = 4, mean 8.8 %). Larger studies are needed to investigate the association between percentages of B cells expressing IgG ultralong CDR3s and observed health traits. • Cattle produce B cells with ultralong CDR3s with extraordinary virus neutralization. • There are limited studies of B cell receptor repertoires of Holstein cattle. • High AMIR cows had higher percent ultralong CDR3s in blood and colostrum. • Selecting for high AMIR and high percents of ultralong CDR3s could improve health.
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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".