Decoding ADGRE5: How Proteolytic Cleavage and Mechanical Forces Unleash Cellular Signals
Bibliographic record
Abstract
The adhesion G protein-coupled receptor ADGRE5/CD97 is upregulated in many cancers, representing a potential drug target in oncology/immuno-oncology. Yet, ADGRE5's activation and signaling mechanisms remain poorly understood. Here, we used enhanced bystander bioluminescence resonance energy transfer (ebBRET)-based biosensors and three strategies to characterize human (h) ADGRE5 signaling. First, a synthetic tobacco etch virus (TEV) protease-cleavable receptor chimera enabling controlled tethered agonist (TA) exposure at the GPCR proteolysis site (GPS) revealed signaling through Gα12 and Gα13, along with the recruitment of β-Arrestins 1/2 (β-Arrs). Second, we investigated WT hADGRE5 signaling elicited by Gingipain K (Kgp), an endopeptidase that cleaves hADGRE5 upstream of the GAIN domain. Kgp mirrored TEV-induced signaling but also promoted Gαz and Gα11 activity. The abolition of hADGRE5's GPS did not block Kgp-induced receptor activation, revealing a GPS cleavage-independent mechanism of action. Finally, we developed an assay to study hADGRE5 mechanical stimulation (MS) using β-Arr2 as a readout. MS promoted β-Arr2 recruitment in hADGRE5-expressing cells, and this response was lost upon abolition of the GPS. A neutralizing antibody to the hADGRE5 ligand CD55 significantly dampened MS-induced β-Arr2 engagement. Overall, this study advances our understanding of hADGRE5's signaling and highlights the receptor's plasticity in activating pathways via both GPS cleavage-dependent and -independent mechanisms.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".