Molecular Identification of Bacteria by Total Sequence Screening: Determining the Cause of Death in Ancient Human Subjects
Bibliographic record
Abstract
Research of ancient pathogens in ancient human skeletons has been mainly carried out on the basis of one essential historical or archaeological observation, permitting specific pathogens to be targeted. Detection of ancient human pathogens without such evidence is more difficult, since the quantity and quality of ancient DNA, as well as the environmental bacteria potentially present in the sample, limit the analyses possible. Using human lung tissue and/or teeth samples from burials in eastern Siberia, dating from the end of 17(th) to the 19(th) century, we propose a methodology that includes the: 1) amplification of all 16S rDNA gene sequences present in each sample; 2) identification of all bacterial DNA sequences with a degree of identity ≥ 95%, according to quality criteria; 3) identification and confirmation of bacterial pathogens by the amplification of the rpoB gene; and 4) establishment of authenticity criteria for ancient DNA. This study demonstrates that from teeth samples originating from ancient human subjects, we can realise: 1) the correct identification of bacterial molecular sequence signatures by quality criteria; 2) the separation of environmental and pathogenic bacterial 16S rDNA sequences; 3) the distribution of bacterial species for each subject and for each burial; and 4) the characterisation of bacteria specific to the permafrost. Moreover, we identified three pathogens in different teeth samples by 16S rDNA sequence amplification: Bordetella sp., Streptococcus pneumoniae and Shigella dysenteriae. We tested for the presence of these pathogens by amplifying the rpoB gene. For the first time, we confirmed sequences from Bordetella pertussis in the lungs of an ancient male Siberian subject, whose grave dated from the end of the 17(th) century to the early 18(th) century.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".