Closing the protein gap in plant chronobiology
Bibliographic record
Abstract
Our modern understanding of diel cell regulation in plants stems from foundational work in the late 1990s that analysed the dynamics of selected genes and mutants in Arabidopsis thaliana. The subsequent rise of transcriptomics technologies such as microarrays and RNA sequencing has substantially increased our understanding of anticipatory (circadian) and reactive (light- or dark-triggered) diel events in plants. However, it is also becoming clear that gene expression data fail to capture critical events in diel regulation that can only be explained by studying protein-level dynamics. Over the past decade, mass spectrometry technologies and quantitative proteomic workflows have significantly advanced, finally allowing scientists to characterise diel protein regulation at high throughput. Initial proteomic investigations suggest that the diel transcriptome and proteome generally lack synchrony and that the timing of daily regulatory events in plants is impacted by multiple levels of protein regulation (e.g., post-translational modifications [PTMs] and protein-protein interactions [PPIs]). Here, we highlight and summarise how the use of quantitative proteomics to elucidate diel plant cell regulation has advanced our understanding of these processes. We argue that this new understanding, coupled with the extraordinary developments in mass spectrometry technologies, demands greater focus on protein-level regulation of, and by, the circadian clock. This includes hitherto unexplored diel dynamics of protein turnover, PTMs, protein subcellular localisation and PPIs that can be masked by simple transcript- and protein-level changes. Finally, we propose new directions for how the latest advancements in quantitative proteomics can be utilised to answer outstanding questions in plant chronobiology.
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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 | 0.001 |
| 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".