TLR9 agonist MGN1703 enhances B cell differentiation and function in lymph nodes
Bibliographic record
Abstract
BACKGROUND: TLR9 agonists are being developed as immunotherapy against malignancies and infections. TLR9 is primarily expressed in B cells and plasmacytoid dendritic cells (pDCs). TLR9 signalling may be critically important for B cell activity in lymph nodes but little is known about the in vivo impact of TLR9 agonism on human lymph node B cells. As a pre-defined sub-study within our clinical trial investigating TLR9 agonist MGN1703 (lefitolimod) treatment in the context of developing HIV cure strategies (NCT02443935), we assessed TLR9 agonist-mediated effects in lymph nodes. METHODS: Participants received MGN1703 for 24 weeks concurrent with antiretroviral therapy. Seven participants completed the sub-study including lymph node resection at baseline and after 24 weeks of treatment. A variety of tissue-based immunologic and virologic parameters were assessed. FINDINGS: MGN1703 dosing increased B cell differentiation; activated pDCs, NK cells, and T cells; and induced a robust interferon response in lymph nodes. Expression of Activation-Induced cytidine Deaminase, an essential regulator of B cell diversification and somatic hypermutation, was highly elevated. During MGN1703 treatment IgG production increased and antibody glycosylation patterns were changed. INTERPRETATION: Our data present novel evidence that the TLR9 agonist MGN1703 modulates human lymph node B cells in vivo. These findings warrant further considerations in the development of TLR9 agonists as immunotherapy against cancers and infectious diseases. FUND: This work was supported by Aarhus University Research Foundation, the Danish Council for Independent Research and the NovoNordisk Foundation. Mologen AG provided study drug free of charge.
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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) | 0.004 | 0.001 |
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".