Abstract 1570 Design and Expression of an Ankyrin-like Molecular Probe for α-Neurexin 1 Labelling in Live Neurons
Bibliographic record
Abstract
Neurexins (NRXNs) are a set of polymorphic cell adhesion molecules that play a key role in formation of new synaptic connections and pre-synaptic differentiation. Multiple binding partners for both α- and β- neurexins help shape the synaptic terminal and stablish stable connections between neurons. Additionally, NRXNs participate in remodelling of synapses terminal and the establishment of new connections. Impairment of proper neurexin function has been related to several psychological, developmental disorders and have been proposed to play a role in neurodegenerative pathogenesis in Alzheimer's disease alongside their post-synaptic counterpart neuroligin 1 (NLGN1). However, we lack the tools to study molecular mechanisms involving neurexins impairments in live cells without undergoing genetic engineering or heterologous gene expression. Herein we describe the development of a selective molecular probe for a-NRXN 1 to allow the real-time tracking of the receptor in live neurons. To achieve this, we used α-latrotoxin (α-LTX), a neurotoxin from the Latrodectus genus. Docking simulations were carried out to determine the binding domain to α-NRXN. We generated a bacterial recombinant expression vector to produce an ankyrin-like fragment of α-LTX. This α-LTX fragment was then used in transfected HEK 293 cells with α-NRXN 1 to measure binding selectivity and cytotoxicity. The final molecular probe offers a new approach for neurexin labelling in live cells to track its dynamic re-organization at synaptic termini during synaptic plasticity. This research was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC 2020-06103) and the University of British Columbia. We gratefully thank UBC Okanagan College of Graduate Studies for active support and research scholarships.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".