Targets of somatic hypermutation within immunoglobulin light chain genes in zebrafish
Bibliographic record
Abstract
In mammals, somatic hypermutation (SHM) of immunoglobulin (Ig) genes is critical for the generation of high-affinity antibodies and effective immune responses. Knowledge of sequence-specific biases in the targeting of somatic mutations can be useful for studies aimed at understanding antibody repertoires produced in response to infections, B-cell neoplasms, or autoimmune disease. To evaluate potential nucleotide targets of somatic mutation in zebrafish (Danio rerio), an enriched IgL cDNA library was constructed and > 250 randomly selected clones were sequenced and analysed. In total, 55 unique VJ-C sequences were identified encoding a total of 125 mutations. Mutations were most prevalent in V(L) with a bias towards single base transitions and increased mutation in the complementarity-determining regions (CDRs). Overall, mutations were overrepresented at WRCH/DGYW motifs suggestive of activation-induced cytidine deaminase (AID) targeting which is common in mice and humans. In contrast to mammalian models, N and P addition was not observed and mutations at AID hotspots were largely restricted to palindromic WRCH/DGYW motifs. Mutability indexes for di- and trinucleotide combinations confirmed C/G targets within WRCH/DGYW motifs to be statistically significant mutational hotspots and showed trinucleotides ATC and ATG to be mutation coldspots. Additive mutations in VJ-C sequences revealed patterns of clonal expansion consistent with affinity maturation responses seen in higher vertebrates. Taken together, the data reveal specific nucleotide targets of SHM in zebrafish and suggest that AID and affinity maturation contribute to antibody diversification in this emerging immunological model.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".