Identifying Natural Substrates for Dipeptidyl Peptidases 8 and 9 Using Terminal Amine Isotopic Labeling of Substrates (TAILS) Reveals in Vivo Roles in Cellular Homeostasis and Energy Metabolism
Bibliographic record
Abstract
Dipeptidyl peptidases (DP) 8 and 9 are homologous, cytoplasmic N-terminal post-proline-cleaving enzymes that are anti-targets for the development of DP4 (DPPIV/CD26) inhibitors for treating type II diabetes. To date, DP8 and DP9 have been implicated in immune responses and cancer biology, but their pathophysiological functions and substrate repertoire remain unknown. This study utilizes terminal amine isotopic labeling of substrates (TAILS), an N-terminal positional proteomic approach, for the discovery of in vivo DP8 and DP9 substrates. In vivo roles for DP8 and DP9 in cellular metabolism and homeostasis were revealed via the identification of more than 29 candidate natural substrates and pathways affected by DP8/DP9 overexpression. Cleavage of 14 substrates was investigated in vitro; 9/14 substrates for both DP8 and DP9 were confirmed by MALDI-TOF MS, including two of high confidence, calreticulin and adenylate kinase 2. Adenylate kinase 2 plays key roles in cellular energy and nucleotide homeostasis. These results demonstrate remarkable in vivo substrate overlap between DP8/DP9, suggesting compensatory roles for these enzymes. This work provides the first global investigation into DP8 and DP9 substrates, providing a number of leads for future investigations into the biological roles and significance of DP8 and DP9 in human health and disease.
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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".