Monoclonal antibodies that block Roundabout 1 and 2 signaling target pathological ocular neovascularization through myeloid cells
Bibliographic record
Abstract
Roundabout (ROBO) 1 and 2 are transmembrane receptors that bind secreted SLIT ligands through their extracellular domains (ECDs) and signal through their cytoplasmic domains to modulate the cytoskeleton and regulate cell migration, adhesion, and proliferation. SLIT-ROBO signaling regulates pathological ocular neovascularization, which is a major cause of vision loss worldwide, but pharmacological tools to prevent SLIT-ROBO signaling are lacking. Here, we developed human monoclonal antibodies (mAbs) against the ROBO1 and ROBO2 ECDs. One antibody that inhibited in vitro SLIT2 signaling through ROBO1 and ROBO2 (anti-ROBO1/2) also reduced ocular neovascularization in oxygen-induced retinopathy (OIR) and laser-induced corneal neovascularization (CNV) mouse models in vivo. Single-cell RNA sequencing of OIR retinas revealed that antibody treatment affected several cell types relevant to physiological and pathological angiogenesis, including endothelial cells, pericytes, and a heterogeneous population of myeloid cells. mAb treatment improved blood-retina barrier integrity and prevented pathological pericyte activation in OIR. SLIT-ROBO signaling inhibition prevented pathological activation of myeloid cells and increased neuroprotective myeloid populations normally seen in the developing retina. Microglia/infiltrating macrophage–specific ablation of Robo1 and Robo2 or knockout of the downstream effector phosphatidylinositol 3-kinase ( Pik3cg ) encoding PI3Kγ in both OIR and CNV models phenocopied anti-ROBO1/2 treatment, further demonstrating the key role of myeloid cells as drivers of ocular neovascular diseases. ROBO1/2 blocking antibodies may thus provide a promising strategy to combat inflammation in blinding eye diseases.
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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 | 0.000 | 0.001 |
| 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".