Loss-of-function approaches in comparative physiology: is there a future for knockdown experiments in the era of genome editing?
Bibliographic record
Abstract
Loss-of-function technologies, such as morpholino- and RNAi-mediated gene knockdown, and TALEN- and CRISPR/Cas9-mediated gene knockout, are widely used to investigate gene function and its physiological significance. Here, we provide a general overview of the various knockdown and knockout technologies commonly used in comparative physiology and discuss the merits and drawbacks of these technologies with a particular focus on research conducted in zebrafish. Despite their widespread use, there is an ongoing debate surrounding the use of knockdown versus knockout approaches and their potential off-target effects. This debate is primarily fueled by the observations that, in some studies, knockout mutants exhibit phenotypes different from those observed in response to knockdown using morpholinos or RNAi. We discuss the current debate and focus on the discrepancies between knockdown and knockout phenotypes, providing literature and primary data to show that the different phenotypes are not necessarily a direct result of the off-target effects of the knockdown agents used. Nevertheless, given the recent evidence of some knockdown phenotypes being recapitulated in knockout mutants lacking the morpholino or RNAi target, we stress that results of knockdown experiments need to be interpreted with caution. We ultimately argue that knockdown experiments should not be discontinued if proper control experiments are performed, and that with careful interpretation, knockdown approaches remain useful to complement the limitations of knockout studies (e.g. lethality of knockout and compensatory responses).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".