Targeting the<i>cis</i>‐dimerization of<scp>LINGO</scp>‐1 with low<scp>MW</scp>compounds affects its downstream signalling
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The transmembrane protein LINGO-1 is a negative regulator in the nervous system mainly affecting axonal regeneration, neuronal survival, oligodendrocyte differentiation and myelination. However, the molecular mechanisms regulating its functions are poorly understood. In the present study, we investigated the formation and the role of LINGO-1 cis-dimers in the regulation of its biological activity. EXPERIMENTAL APPROACH: LINGO-1 homodimers were identified in both HEK293 and SH-SY5Y cells using co-immunoprecipitation experiments and BRET saturation analysis. We performed a hypothesis-driven screen for identification of small-molecule protein-protein interaction modulators of LINGO-1 using a BRET-based assay, adapted for screening. The compound identified was further assessed for effects on LINGO-1 downstream signalling pathways using Western blotting analysis and AlphaScreen technology. KEY RESULTS: LINGO-1 was present as homodimers in primary neuronal cultures. LINGO-1 interacted homotypically in cis-orientation and LINGO-1 cis-dimers were formed early during LINGO-1 biosynthesis. A BRET-based assay allowed us to identify phenoxybenzamine as the first conformational modulator of LINGO-1 dimers. In HEK-293 cells, phenoxybenzamine was a positive modulator of LINGO-1 function, increasing the LINGO-1-mediated inhibition of EGF receptor signalling and Erk phosphorylation. CONCLUSIONS AND IMPLICATIONS: Our data suggest that LINGO-1 forms constitutive cis-dimers at the plasma membrane and that low MW compounds affecting the conformational state of these dimers can regulate LINGO-1 downstream signalling pathways. We propose that targeting the LINGO-1 dimerization interface opens a new pharmacological approach to the modulation of its function and provides a new strategy for drug discovery.
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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 | 0.001 |
| 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".