Proteomics of protein trafficking by <i>in vivo</i> tissue-specific labeling
Bibliographic record
Abstract
Abstract Secreted interorgan communication factors encode key regulators of homeostasis. However, long-standing questions surround their origins/destinations, mechanisms of interactions, and the number of proteins involved. Progress has been hindered by the lack of methodologies for these factors’ large-scale identification and characterization, as conventional approaches cannot identify low-abundance factors and the origins and destinations of secreted proteins. We established an in vivo platform to investigate secreted protein trafficking between organs proteome-wide, whereby engineered promiscuous biotin ligase BirA*G3 (a relative of TurboID) biotinylates all proteins in a subcellular compartment of one tissue, and biotinylated proteins are affinity-enriched and identified from distal organs using quantitative mass spectrometry. Using this platform, we identified 51 putative muscle-secreted proteins from heads and 269 fat body-secreted proteins from legs/muscles, of which 60-70% have human orthologs. We demonstrate, in particular, that conserved fat body-derived novel interorgan communication factors CG31326, CG2145, and CG4332 promote muscle activity. Our results indicate that the communication network of secreted proteins is vast, and we identified systemic functions for a number of these factors. This approach is widely applicable to studies in interorgan, local and intracellular protein trafficking networks, non-conventional secretion, and to mammalian systems, under healthy or diseased states. One Sentence Summary We developed an in vivo platform to investigate protein trafficking between organs proteome-wide, provide a resource for interorgan communication factors, and determined conserved adipokines that affect muscles.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".