Rituximab Therapy Leads to Reduced Imprints of Receptor Revision in Immunoglobulin κ and λ Light Chains
Bibliographic record
Abstract
OBJECTIVE: Transient B cell depletion by rituximab (RTX) has become a specific treatment of rheumatoid arthritis (RA). Although phenotypic repopulation kinetics of B cell subsets are well documented, precise molecular analyses of the reconstituting immunoglobulin (Ig) genes encoding the B cell receptor in RA are sparse. METHODS: A total of 708 individual CD19+CD27+ (memory) and CD19+CD27- (naive) B cells from 2 patients with RA were analyzed at baseline and 7 months after RTX at B cell repopulation. Ig light chain variable kappa (Vκ) and lambda (Vλ) light chain gene rearrangements were amplified, sequenced, and analyzed with a focus on receptor revision. RESULTS: The naive as well as the memory repertoire repopulated polyclonally with diverse use of variable light chain gene families and minigenes. During the reconstitution phase, B cells used significantly fewer Jκ distal Vκ genes (p = 0.0006), with a higher frequency of somatic hypermutation of rearrangements employing Jκ5 compared to baseline in memory B cells. The use of Vλ rearrangements in regenerating B cells was also biased toward use of Vλ genes of the proximal cassette. In general, reemerging CD27+ Ig light chain genes were substantially more highly mutated than before RTX therapy (p < 0.0001, baseline vs during reconstitution). CONCLUSION: Our data indicate that RTX therapy leads to generation of distinct Vκ/Jκ and Vλ/Jλ gene repertoires consistent with replenishment of antigen-experienced B cells by germinal centers. At baseline, the imprints of receptor revision appeared to be more striking, which indicates that receptor revision is active in patients with RA and can be reduced by RTX.
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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 | 0.000 |
| 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".