Horizontal plasmid transfer promotes antibiotic resistance in selected bacteria in Chinese frog farms
Bibliographic record
Abstract
The emergence and dissemination of antibiotic resistance genes (ARGs) in the ecosystem are global public health concerns. One Health emphasizes the interconnectivity between different habitats and seeks to optimize animal, human, and environmental health. However, information on the dissemination of antibiotic resistance genes (ARGs) within complex microbiomes in natural habitats is scarce. We investigated the prevalence of antibiotic resistant bacteria (ARB) and the spread of ARGs in intensive bullfrog (Rana catesbeiana) farms in the Shantou area of China. Antibiotic susceptibilities of 361 strains, combined with microbiome analyses, revealed Escherichia coli, Edwardsiella tarda, Citrobacter and Klebsiella sp. as prevalent multidrug resistant bacteria on these farms. Whole genome sequencing of 95 ARB identified 250 large plasmids that harbored a wide range of ARGs. Plasmid sequences and sediment metagenomes revealed an abundance of tetA, sul1, and aph(3″)-Ib ARGs. Notably, antibiotic resistance (against 15 antibiotics) highly correlated with plasmid-borne rather than chromosome-borne ARGs. Based on sequence similarities, most plasmids (62%) fell into 32 distinct groups, indicating a potential for horizontal plasmid transfer (HPT) within the frog farm microbiome. HPT was confirmed in inter- and intra-species conjugation experiments. Furthermore, identical mobile ARGs, flanked by mobile genetic elements (MGEs), were found in different locations on the same plasmid, or on different plasmids residing in the same or different hosts. Our results suggest a synergy between MGEs and HPT to facilitate ARGs dissemination in frog farms. Mining public databases retrieved similar plasmids from different bacterial species found in other environmental niches globally. Our findings underscore the importance of HPT in mediating the spread of ARGs in frog farms and other microbiomes of the ecosystem.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".