Heavy Metal Pollution From a Major Earthquake and Tsunami in Chile Is Associated With Geographic Divergence of Clinical Isolates of Methicillin-Resistant <i>Staphylococcus aureus</i> in Latin America
Bibliographic record
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is a priority pathogen listed by the World Health Organization. The global spread of MRSA is characterized by successive waves of epidemic clones that predominate in specific geographical regions. The acquisition of genes encoding resistance to heavy-metals is thought to be a key feature in the divergence and geographical spread of MRSA. Increasing evidence suggests that extreme natural events, such as earthquakes and tsunamis, could release heavy-metals into the environment. However, the impact of environmental exposition to heavy-metals on the divergence and spread of MRSA clones has been insufficiently explored. We assess the association between a major earthquake and tsunami in an industrialized port in southern Chile and MRSA clone divergence in Latin America. We performed a phylogenomic reconstruction of 113 MRSA clinical isolates from seven Latin American healthcare centers, including 25 isolates collected in a geographic area affected by an earthquake and tsunami that led to high levels of heavy-metal environmental contamination. We found a divergence event strongly associated with the presence of a plasmid harboring heavy-metal resistance genes in the isolates obtained in the area where the earthquake and tsunami occurred. Moreover, clinical isolates carrying this plasmid showed increased tolerance to mercury, arsenic, and cadmium. We also observed a physiological burden in the plasmid-carrying isolates in absence of heavy-metals. Our results are the first evidence that suggests that heavy-metal contamination, in the aftermath of an environmental disaster, appears to be a key evolutionary event for the spread and dissemination of MRSA in Latin America.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".