Why Maintain Light Chain Isotypes? The Influence of Heavy Chain Isotype and Complementary Determining Region Lengths upon Light Chain Isotype in Xenopus laevis
Bibliographic record
Abstract
Different immunoglobulin (Ig) heavy chain (H) isotypes have distinct functions, but so far it is unclear if Ig light (L) chains follow the same pattern. It is usually assumed that form follows function; but if this is true, then why have different IgL isotypes with no known functional differences? In this study we investigate IgH and IgL isotype preferential binding and complementary determining region (CDR) lengths to try to address this question using the African clawed frog, Xenopus laevis, as a model. Amphibians exhibit IgH isotype class switch at a single IgH locus and have an additional, more divergent, IgL isotype (σ) plus the two found in mammals (λ and κ). We used quantitative PCR (qPCR) analysis of IgH isotype of B cells sorted by surface IgL isotype expression to find evidence of preferential use of IgL isotype by IgH isotype. We found a relative skewing in the Igκ cells for IgY, in the Igλ cells for IgX, the Igσ cells for IgM, and corroborated published immunoprecipitations showing that IgY and Igσ do not pair with gene expression data of the IgL isotype sorted cells. Our data also suggests that the exaggerated CDR1 of IgHV families III and VII and the long CDR2 of Igσ may cramp IgH CDR3, making the IgHV III/VII-Igσ pairing less common. While these data do not resolve the conundrum of multiple IgL isotype maintenance in vertebrates, they do show that in a tetrapod with several IgH and several IgL isotype options, IgL isotype use is not random.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".