Abundance of clinically relevant antimicrobial resistance genes in the golden jackal ( <i>Canis aureus</i> ) gut
Bibliographic record
Abstract
ABSTRACT The spread of antimicrobial resistance (AMR) is a critical One Health issue. Wildlife could act as reservoirs or vehicles of AMR bacteria (ARBs) and AMR genes (ARGs) but are relatively understudied. We sought to investigate clinically relevant ARGs in golden jackals ( Canis aureus ) thriving near human settlements in Israel. Fecal samples were collected from 111 jackals across four regions over a 10-month period. Various animal and spatio-temporal metadata were collected. Samples were analyzed by quantitative PCR (qPCR) for beta-lactamases ( bla TEM, bla CTX-M15, and bla SHV), qnrS and int1 . A subset of samples was subject to shotgun metagenomic sequencing followed by resistome and microbiome analyses. qPCR detected a high prevalence of ARGs, including beta-lactamases ( bla TEM-1, 96.4%; bla CTX-M-15, 51.4%, bla SHV, 15.3%), fluoroquinolone resistance ( qnrS , 87.4%), and class 1 integrons ( Int1 , 94.6%). The bla TEM-1 gene was found to be more prevalent in adult jackals compared to younger ones. Metagenomic analysis of a subset of samples revealed a diverse gut microbiome harboring a rich resistome with tetracycline resistance genes being the most prevalent. Metagenome-assembled genome analysis further identified several ARGs associated with clinically relevant bacteria. These findings highlight the potential role of golden jackals as reservoirs for AMR and emphasize the need for ongoing surveillance to better understand AMR transmission dynamics at the wildlife–human interface. IMPORTANCE The research highlights the potential role of the golden jackals as reservoirs for antimicrobial resistance (AMR). The high prevalence of clinically relevant AMR genes in these jackals emphasizes the need for ongoing surveillance and monitoring to better understand AMR transmission dynamics at the wildlife–human interface.
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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 | 0.001 | 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".