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 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".