Teneurin-4 switches between self-recognition and canonical Latrophilin binding to direct neuronal migration
Bibliographic record
Abstract
Abstract Cortical migration is a complex process in which neurons migrate along radial glial cells (RGC) to form functional layers. Teneurins (Ten1-4) play a role by interacting with Latrophilins (Lphn/ADGRL1-3). Teneurins are also known as cell adhesion molecules, but how homophilic and heterophilic Teneurin interactions are integrated is unknown. Here, single-particle-cryo-EM data of Ten2 shows that canonical Latrophilin-binding is sterically incompatible with Ten2-dimerisation, making these interactions exclusive. We engineered surface mutations that specifically disrupt Ten2-Ten2 or Ten2-Latrophilin interactions. These are transferrable to Ten4, suggesting conserved binding mechanisms. Proteomics , in-vivo -gene-editing and super-resolution-microscopy show that Ten4 is expressed along RGC fibres and that migrating neurons switch from low-to-high Ten4-expression. Ten4 expression is highest in the cortical plate where Ten4-Ten4 interactions reduce RGC-attachment. In the intermediate zone, Ten4-Latrophilin interactions are required to promote neuron-RGC association. The results show how Ten4 orchestrates cortical migration by exclusive structural mechanisms, underpinning the integration of distinct migration programmes. Note: the adhesion GPCR ADGRL is largely referred to as ‘Latrophilin’, which is in line with previous papers in the Teneurin field. We would be happy to implement a different naming scheme if recommended .
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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) | 0.002 | 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".