A 20+ Ma old enamel proteome from Canada’s High Arctic reveals diversification of Rhinocerotidae in the middle Eocene-Oligocene
Bibliographic record
Abstract
In the past decade, ancient protein sequences have emerged as a valuable source of data for deep-time phylogenetic inference. Still, the recovery of protein sequences providing novel phylogenetic insights does not exceed 3.7 Ma (Pliocene). Here, we push this boundary back to 21-24 Ma (early Miocene), by retrieving enamel protein sequences of an early-diverging rhinocerotid ( Epiaceratherium sp. - CMNF-59632) from the Canadian High Arctic. We recover partial sequences of seven enamel proteins (AHSG, ALB, AMBN, AMELX, AMTN, ENAM, MMP20) and over 1000 peptide-spectrum matches, spanning over at least 251 amino acids. Authentic endogeneity of these sequences is supported by indicators of protein damage, including several spontaneous and irreversible post-translational modifications accumulated during prolonged diagenesis and reaching near-complete occupancy at many sites. Bayesian tip-dating, across 15 extant and extinct perissodactyl taxa, places the divergence time of CMNF-59632 in the middle Eocene-Oligocene, and identifies a later divergence time for Elasmotheriinae in the Oligocene. The finding weakens alternative models suggesting a deep basal split between Elasmotheriinae and Rhinocerotinae. This divergence time of CMNF-59632 coincides with a phase of high diversification of rhinocerotids, and supports a Eurasian origin of this clade in the late Eocene or Oligocene. The findings are consistent with previous hypotheses on the origin of the enigmatic fauna of the Haughton crater, which, in spite of their considerable degree of endemism, also display similarity to distant Eurasian faunas. Our findings demonstrate the potential of palaeoproteomics in obtaining phylogenetic information from a specimen that is ten times older than any sample from which endogenous DNA has been obtained.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".