Orchestrated translation specializes dinoflagellate metabolism three times per day
Bibliographic record
Abstract
Many cells specialize for different metabolic tasks at different times over their normal ZT cycle by changes in gene expression. However, in most cases, circadian gene expression has been assessed at the mRNA accumulation level, which may not faithfully reflect protein synthesis rates. Here, we use ribosome profiling in the dinoflagellate Lingulodinium polyedra to identify thousands of transcripts showing coordinated translation. All of the components in carbon fixation are concurrently regulated at ZT0, predicting the known rhythm of carbon fixation, and many enzymes involved in DNA replication are concurrently regulated at ZT12, also predicting the known rhythm in this process. Most of the enzymes in glycolysis and the TCA cycle are also regulated together, suggesting rhythms in these processes as well. Surprisingly, a third cluster of transcripts show peak translation at approximately ZT16, and these transcripts encode enzymes involved in transcription, translation, and amino acid biosynthesis. The latter has physiological consequences, as measured free amino acid levels increase at night and thus represent a previously undocumented rhythm in this model. Our results suggest that ribosome profiling may be a more accurate predictor of changed metabolic state than transcriptomics.
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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) | 0.001 | 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".