Reduced vaccine-induced germinal center outputs in patients with inflammatory bowel disease treated with anti-TNF biologics
Bibliographic record
Abstract
BACKGROUNDAnti-TNF biologics are widely used to treat patients with immune-mediated inflammatory diseases. In mouse models, the complete absence of TNF impairs germinal center (GC) responses. Less is known about the impact of anti-TNF therapy on specific immune responses in humans. Widespread vaccination against SARS-CoV-2 offered an unprecedented opportunity to investigate the effects of biological therapies on responses to specific immunization. Previous work demonstrated that patients with inflammatory bowel disease (IBD) who were treated with anti-TNF biologics exhibited decreased Spike-specific antibody responses compared with patients with IBD treated with anti-IL-12/23 or healthy controls, even after 4 doses of mRNA vaccine.METHODSHere we analyzed humoral responses to SARS-CoV-2 immunization using single-cell RNA-Sequencing and flow cytometry of Spike-specific memory B cells (MBC), as well as avidity measurements of plasma antibodies from patients with IBD treated with anti-TNF or anti-IL-12/23 and from people in the healthy control group.RESULTSWe observed decreased somatic hypermutation in the B cell receptors of Spike-specific MBCs and decreased antigen-specific MBC accumulation following SARS-CoV-2 mRNA vaccination in patients with IBD treated with anti-TNF, compared with patients with IBD treated with anti-IL-12/23 or people in the healthy control group. This decreased somatic hypermutation in Spike-specific MBCs in patients treated with anti-TNF correlated with decreased and delayed antibody affinity maturation and reduced neutralization activity.CONCLUSIONThese data provide in vivo evidence that anti-TNF, but not anti-IL-12/23, therapy impairs the quantity and quality of antigen-specific GC outputs in humans.FUNDINGJuan and Stefania Speck (donation) and by Canadian Institutes of Health Research (CIHR)/COVID-Immunity Task Force (CITF) grants VR-1 172711, VS1-175545, GA2-177716, GA1-177703 and CIHR FDN 143301 &143350.
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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 | 0.001 |
| 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) | 0.002 | 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".