Agonistic nanobodies and antibodies to human VISTA
Bibliographic record
Abstract
The V-domain Ig Suppressor of T-cell Activation (VISTA) is an immune checkpoint regulator that suppresses immune responses and is readily expressed on human and murine myeloid cells and T cells. This immunosuppressive pathway can be activated using VISTA agonists. Here, we report the development of murine anti-human VISTA (anti-hVISTA) monoclonal antibodies (mAbs), anti-hVISTA nanobodies (Nbs), and cross-reactive rat anti-murine/human VISTA (anti-hmVISTA) mAbs. All mAbs and Nbs generated bound to VISTA (human and/or murine) with dissociation constants in the sub-nanomolar or low nanomolar range. Competition analysis revealed that the selected Nbs bound the same or a nearby epitope(s) as the human VISTA-specific mAbs. However, the cross-reactive mAbs only partially competed with Nbs for binding to hVISTA. All mAbs and one Nb (hVISTANb7) were able to strongly detect VISTA expression on primary human monocytes. Importantly, the murine anti-hVISTA mAbs 7E12 and 7G5 displayed strong agonistic activity in human peripheral blood mononuclear cell cultures, while Nb7 and rat anti-hmVISTA mAbs 3C3, 7C6, 7C7, and 7G1 also behaved as hVISTA agonists, albeit to a lesser extent. Cross-reactive mAbs 7C7 and 7G1 further displayed agonistic potential in murine splenocyte assays. Importantly, mAb 7G1 significantly reduced inflammation associated with the murine model of imiquimod-induced psoriasis. These agonistic VISTA mAbs may represent therapeutic leads to treat inflammatory disorders.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".