Disruption of memory B-cell trafficking by belimumab in patients with systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVES: Autoreactive memory B cells (MBCs) contribute to chronic and progressive courses in autoimmune diseases like SLE. The efficacy of belimumab (BEL), the first approved biologic treatment for SLE and LN, is generally attributed to depletion of activated naïve B cells and inhibition of B-cell activation. BEL's effect on MBCs is currently unexplained. We performed an in-depth cellular and transcriptomic analysis of BEL's impact on the blood MBC compartment in patients with SLE. METHODS: A retrospective meta-analysis was conducted, pooling flow cytometry data from four randomized trials involving 1245 patients with SLE treated with intravenous BEL or placebo. Then, extensive MBC phenotyping was performed using high-sensitivity flow cytometry in patients with mild/moderate SLE and severe SLE/LN treated with subcutaneous BEL. Finally, transcriptomic characterization of surging MBCs was performed by single-cell RNA sequencing. RESULTS: In BEL-treated patients, a significant increase in circulating MBCs, in a broad range of MBC subsets, was established at week 2, gradually returning to baseline by week 52. The increase was most prominent in patients with higher SLE disease activity, serologically active patients and patients aged ≤18 years. MBCs had a non-proliferating phenotype with a prominent decrease in activation status and downregulation of numerous migration genes. CONCLUSION: Upon BEL initiation, an increase of MBCs was firmly established. In the small cohort investigated, circulating MBCs were de-activated, non-proliferative and demonstrated characteristics of disrupted lymphocyte trafficking, expanding on our understanding of the therapeutic mechanism of B-cell-activating factor inhibition by BEL. TRIAL REGISTRATION: ClinicalTrials.gov, http://clinicaltrials.gov, NCT00071487, NCT00410384, NCT01632241, NCT01649765, NCT03312907, NCT03747159.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".