Axon guidance genes identified in a large-scale RNAi screen using the RNAi-hypersensitive <i>Caenorhabditis elegans</i> strain <i>nre-1(hd20) lin-15b(hd126</i> )
Bibliographic record
Abstract
The navigation of axons toward their targets is a highly dynamic and precisely regulated process during nervous system development. The molecular basis of this navigation process is only partly understood. In Caenorhabditis elegans, we isolated the RNAi-hypersensitive strain nre-1(hd20) lin-15b(hd126), which allows us to phenocopy axon guidance defects of known genes by feeding RNAi. We used this mutant strain to systematically screen 4,577 genes on chromosomes I and III for axon guidance phenotypes. We identified 93 genes whose down-regulation led to penetrant ventral cord fasciculation defects or motoneuron commissure outgrowth defects. These genes encode various classes of proteins, ranging from secreted or putative cell surface proteins to transcription factors controlling gene expression. A majority of the genes is evolutionary conserved and previously uncharacterized. In addition, we found axon guidance functions for known genes like pry-1, a component of the Wnt-signaling pathway, and ced-1, a receptor required for the engulfment of neurons undergoing apoptosis during development. Our screen provides insights into molecular pathways operating during the generation of neuronal circuits and provides a basis for a more detailed analysis of gene networks regulating axon navigation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".