Combinatorial immunoglobulin light chain variability creates sufficient B cell diversity to mount protective antibody responses against pathogen infections
Bibliographic record
Abstract
To analyze how combinatorial light (L) chain diversity influences the B cell repertoire, we studied mice with a homozygous immunoglobulin-heavy-chain null mutation (mu MT), in which the B cell developmental block was overridden by the expression of a transgenic immunoglobulin mu heavy (H) chain derived from a vesicular stomatitis virus Indiana serotype (VSV-IND)-neutralizing Ab (T11 mu MT mice). The randomly integrated transgene could not undergo secondary rearrangements and was expressed in combination with endogenous kappa or lambda chains. T11 mu MT mice had a skewed B cell repertoire as evidenced by 30-60% VSV-IND-specific peripheral B cells and spontaneous VSV-IND-neutralizing serum titers. Upon immunization, T11 mu MT mice mounted specific IgM antibody responses against VSV-IND but, interestingly, they also responded against VSV New Jersey serotype (VSV-NJ), lymphocytic choriomeningitis virus, poliovirus and Salmonella typhi porins. Variable-region sequence analysis revealed that VSV-NJ-specific antibodies expressed numerous L chains in combination with the transgenic H chain, which was devoid of hypermutations. Thus, in T11 mu MT mice combinatorial L chain variability alone is able to build up a sufficiently complex B cell repertoire to mount protective immunoglobulin responses against a variety of pathogens.
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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 | 0.000 | 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".