DIG-1, a novel giant protein, non-autonomously mediates maintenance of nervous system architecture
Bibliographic record
Abstract
Dedicated mechanisms exist to maintain the architecture of an animal's nervous system after development is completed. To date, three immunoglobulin superfamily members have been implicated in this process in the nematode Caenorhabditis elegans: the secreted two-Ig domain protein ZIG-4, the FGF receptor EGL-15 and the L1-like SAX-7 protein. These proteins provide crucial information for neuronal structures, such as axons, that allows them to maintain the precise position they acquired during development. Yet, how widespread this mechanism is throughout the nervous system, and what other types of factors underlie such a maintenance mechanism, remains poorly understood. Here, we describe a new maintenance gene, dig-1, that encodes a predicted giant secreted protein containing a large number of protein interaction domains. With 13,100 amino acids, the DIG-1 protein is the largest secreted protein identifiable in any genome database. dig-1 functions post-developmentally to maintain axons and cell bodies in place within axonal fascicles and ganglia. The failure to maintain axon and cell body position is accompanied by defects in basement membrane structure, as evidenced by electron microscopy analysis of dig-1 mutants. Expression pattern and mosaic analysis reveals that dig-1 is produced by muscles to maintain nervous system architecture, demonstrating that dig-1 functions non-autonomously to preserve the proper layout of neural structures. We propose that DIG-1 is a component of the basement membrane that mediates specific contacts between cellular surfaces and their environment through the interaction with a cell-type specific set of other maintenance factors.
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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) | 0.000 | 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".