Uncovering the Proteome Response of the Master Circadian Clock to Light Using an AutoProteome System
Bibliographic record
Abstract
In mammals, the suprachiasmatic nucleus (SCN) is the central circadian pacemaker that governs rhythmic fluctuations in behavior and physiology in a 24-hr cycle and synchronizes them to the external environment by daily resetting in response to light. The bilateral SCN is comprised of a mere ~20,000 neurons serving as cellular oscillators, a fact that has, until now, hindered the systematic study of the SCN on a global proteome level. Here we developed a fully automated and integrated proteomics platform, termed AutoProteome system, for an in-depth analysis of the light-responsive proteome of the murine SCN. All requisite steps for a large-scale proteomic study, including preconcentration, buffer exchanging, reduction, alkylation, digestion and online two-dimensional liquid chromatography-tandem MS analysis, are performed automatically on a standard liquid chromatography-MS system. As low as 2 ng of model protein bovine serum albumin and up to 20 μg and 200 μg of SCN proteins can be readily processed and analyzed by this system. From the SCN tissue of a single mouse, we were able to confidently identify 2131 proteins, of which 387 were light-regulated based on a spectral counts quantification approach. Bioinformatics analysis of the light-inducible proteins reveals their diverse distribution in different canonical pathways and their heavy connection in 19 protein interaction networks. The AutoProteome system identified vasopressin-neurophysin 2-copeptin and casein kinase 1 delta, both of which had been previously implicated in clock timing processes, as light-inducible proteins in the SCN. Ras-specific guanine nucleotide-releasing factor 1, ubiquitin protein ligase E3A, and X-linked ubiquitin specific protease 9, none of which had previously been implicated in SCN clock timing processes, were also identified in this study as light-inducible proteins. The AutoProteome system opens a new avenue to systematically explore the proteome-wide events that occur in the SCN, either in response to light or other stimuli, or as a consequence of its intrinsic pacemaker capacity.
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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 | 0.001 | 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 | 0.000 |
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".