Widespread occurrence of botulinum and tetanus neurotoxin genes in ancient DNA
Bibliographic record
Abstract
Ancient DNA collected from archaeological specimens not only provides a window into ancient human genetic diversity but also contains a rich mixture of associated microbial DNA including potential pathogens. In recent work, we identified C. tetani and tetanus neurotoxin (TeNT) genes in ancient DNA datasets collected from human archaeological specimens. However, the reasons underlying the occurrence of these toxin genes and the extent to which other toxin genes are present in ancient DNA is unclear. Here, we performed a large-scale analysis of 6,435 ancient DNA (aDNA) sequencing datasets including human and non-human sources, searching for 49 clostridial neurotoxin types and subtypes, as well as 3 additional unrelated toxins. Our search identified a total of 105 ancient DNA datasets (1.6%) containing significant matches to one or more neurotoxin genes. Consistent with our earlier work, TeNT genes were most common, found in 50 ancient DNA datasets. In addition, we identified sequences encoding diverse botulinum neurotoxins including BoNT/C (40 samples), BoNT/D (6 samples), BoNT/B (4 samples), BoNT/E (1 sample), and the Enterococcus -associated BoNT/En (10 samples). TeNT genes were detected in a broad range of ancient samples including human and animal (horse, wild bear, chimpanzee, gorilla, dog) remains, whereas the largest diversity of toxins was detected in aDNA from Egyptian mummies. Phylogenetic and sequence analysis of the identified matches revealed close identity to modern forms of these toxins. Damage analysis revealed several toxin genes with hallmarks of ancient DNA associated damage, indicative of an ancient origin. Our work reveals that clostridial neurotoxin genes occur frequently in aDNA samples, including human and animal-associated toxin variants. We conclude that the frequent association of these genes with aDNA likely reflects a strong ecological association of pathogenic clostridia with decaying human and animal remains and possible post-mortem colonization of these samples. • We examined 6,435 ancient DNA sequencing datasets for neurotoxin genes, including tetanus and botulinum neurotoxins. • Neurotoxin genes were detected in 105 datasets (1.6%) associated with 91 distinct ancient DNA samples from both human and animal remains • Identified toxin genes included TeNT, BoNT/B, C, D, E, and En, with TeNT and BoNT/C being most frequently detected. • Ancient DNA associated neurotoxin genes closely resemble modern sequences • Several identified neurotoxin genes show evidence of ancient DNA associated DNA damage, suggesting an ancient origin
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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) | 0.000 | 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) | 0.000 | 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".