Post-Translational Deimination of Immunological and Metabolic Protein Markers in Plasma and Extracellular Vesicles of Naked Mole-Rat (Heterocephalus glaber)
Bibliographic record
Abstract
Naked mole-rats are long-lived animals that show unusual resistance to hypoxia, cancer and ageing. Protein deimination is an irreversible post-translational modification caused by the peptidylarginine deiminase (PAD) family of enzymes, which convert arginine into citrulline in target proteins. Protein deimination can cause structural and functional protein changes, facilitating protein moonlighting, but also leading to neo-epitope generation and effects on gene regulation. Furthermore, PADs have been found to regulate cellular release of extracellular vesicles (EVs), which are lipid-vesicles released from cells as part of cellular communication. EVs carry protein and genetic cargo and are indicative biomarkers that can be isolated from most body fluids. This study was aimed at profiling deiminated proteins in plasma and EVs of naked mole-rat. Key immune and metabolic proteins were identified to be post-translationally deiminated, with 65 proteins specific for plasma, while 42 proteins were identified to be deiminated in EVs only. Using protein-protein interaction network analysis, deiminated plasma proteins were found to belong to KEEG (Kyoto Encyclopedia of Genes and Genomes) pathways of immunity, infection, cholesterol and drug metabolism, while deiminated proteins in EVs were also linked to KEEG pathways of HIF-1 signalling and glycolysis. The mole-rat EV profiles showed a poly-dispersed population of 50-300 nm, similar to observations of human plasma. Furthermore, the EVs were assessed for three key microRNAs involved in cancer, inflammation and hypoxia. The identification of post-translational deimination of critical immunological and metabolic markers contributes to the current understanding of protein moonlighting functions, via post-translational changes, in the longevity and cancer resistance of naked mole-rats.
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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".