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Record W4408618710 · doi:10.1128/mbio.03669-24

Geographic divergence of methicillin-resistant <i>Staphylococcus aureus</i> ST5-SCC <i>mec</i> I in the aftermath of a major earthquake and tsunami: impact of a plasmid harboring heavy metal resistance genes

2025· article· en· W4408618710 on OpenAlexaff
José R W Martínez, Manuel Alcalde‐Rico, Estefanía Jara-Videla, Jinnethe Reyes, Lina P Carvajal, Sandra Rincón, Rafael S. Rios, Lorena Díaz, Ana María Quesille-Villalobos, Roberto Riquelme-Neira, Lina Rivas, Ahmed M. Moustafa, Blake Hanson, Eduardo A. Undurraga, Jorge Olivares‐Pacheco, Patricia García, Rafael Araos, Paul J. Planet, César A. Arias, José M. Munita

Bibliographic record

VenuemBio · 2025
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsCanadian Institute for Advanced Research
FundersNational Institute of Allergy and Infectious DiseasesAgencia Nacional de Investigación y DesarrolloCommon Fund for Commodities
KeywordsStaphylococcus aureusLineage (genetic)Methicillin-resistant Staphylococcus aureusDivergence (linguistics)PlasmidCarriageGeneBiologyMicrobiologyGeneticsGeographyBacteria

Abstract

fetched live from OpenAlex

ABSTRACT Methicillin-resistant Staphylococcus aureus (MRSA) is a major public health menace. The global spread of MRSA is characterized by successive waves of epidemic clones dominating specific geographical regions. The acquisition of genes encoding resistance to heavy metals (HMRGs) is thought to be a key feature in the geographic divergence of MRSA. However, the cause-effect relationship between the presence of HMRGs and the divergence of MRSA clones remains to be clarified. In this study, we assessed the role that HMRGs may have played in the evolutionary divergence of the MRSA ST5-SCC mec I lineage in Latin America. We conducted a genomic characterization of 113 MRSA clinical isolates from six Latin American healthcare centers, including 53 isolates collected from two cities in Chile (Santiago and Concepción). We found a plasmid (pSCL4752) harboring arsenic, cadmium, and mercury resistance genes in 65% ( n = 71) of the ST5-SCC mec I isolates. We also observed a geographic divergence associated with the presence of pSCL4752 in Chilean isolates, with a higher frequency in isolates from Concepción (88%) compared to Santiago (29%). Interestingly, a molecular clock analysis revealed that this divergence occurred in the aftermath of an 8.8 Mw earthquake and tsunami that struck the Concepción area in 2010. Moreover, our results demonstrate that the carriage of pSCL4752 can be beneficial or detrimental for ST5-SCC mec I isolates, depending on the environmental availability of these heavy metals. Our results suggest that the divergence of the ST5-SCC mec I MRSA lineage in Latin America could have been fostered by environmental disasters and influenced by the presence/absence of HMRGs harbored in a plasmid. IMPORTANCE Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of life-threatening infections worldwide and a growing public health concern. The rise of antibiotic-resistant bacteria, such as MRSA, is often linked to genetic adaptations that enhance their survival. Our research sheds light on how environmental changes, such as those triggered by a natural disaster, can influence the evolution and geographic spread of a highly resistant MRSA lineage in Latin America. We identified a plasmid carrying genes for resistance to arsenic, cadmium, and mercury, which was associated with the geographic divergence of the ST5-SCC mec I MRSA lineage, with striking differences in its prevalence between regions affected by a major earthquake and tsunami. By linking environmental events to pathogen evolution, our study highlights the role of ecological pressures in the spread of MRSA. These findings emphasize the need to integrate environmental monitoring into public health strategies to better understand the global challenge of antimicrobial resistance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.279
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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