Auxin does not affect a suite of morphological or behavioral phenotypes in two wild-type C. elegans strains
Bibliographic record
Abstract
The Auxin-Inducible Degradation (AID) system is a powerful technique in the C. elegans toolkit that enables conditional and reversible protein depletion with high temporal and spatial specificity (Zhang et al. 2015; Martinez et al. 2020; Ashley et al. 2020; Martinez and Matus 2020). This system relies on tagging a gene of interest with a short AID degron sequence and transgenic expression of TIR1, an inducible E3 ubiquitin ligase normally found only in plants (Nishimura et al. 2009; Zhang et al. 2015). Upon exposure to the plant-derived hormone Auxin, TIR1 is activated and targets AID-tagged proteins for proteasomal degradation (Nishimura et al. 2009; Zhang et al. 2015) (Figure 1A). While there are qualitative reports that Auxin does not overtly affect the morphology or behavior of wild-type C. elegans (Zhang et al. 2015), this has not been quantitatively assessed. Determining whether Auxin significantly affects C. elegans morphology and behavior, even in subtle ways, is important given the C. elegans community’s rapid uptake of the AID system (Kasimatis et al. 2018; Nance and Frøkjær-Jensen 2019; Ashley et al. 2020; McDiarmid et al. 2020). Here, we use our high-throughput machine vision tracking system, the Multi-Worm Tracker (MWT) (Swierczek et al. 2011), to investigate whether exposure to Auxin affects a suite of morphological, locomotor, mechanosensory, and short-term habituation learning phenotypes in our lab’s derivate of Bristol N2 wild-type worms and the CGC wild-type reference strain, PD1074 (Yoshimura et al. 2019) (Figure 1B).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".