Studying the Association between Antibiotic Resistance Genes and Insertion Sequences in Metagenomes: Challenges and Pitfalls
Bibliographic record
Abstract
Antibiotic resistance is an issue in many areas of human activity. The mobilization of antibiotic resistance genes within the bacterial community makes it difficult to study and control the phenomenon. It is known that certain insertion sequences, which are mobile genetic elements, can participate in the mobilization of antibiotic resistance genes and in the expression of these genes. However, the magnitude of the contribution of insertion sequences to the mobility of antibiotic resistance genes remains understudied. In this study, the relationships between insertion sequences and antibiotic resistance genes present in the microbiome were investigated using two public datasets. The first made it possible to analyze the effects of different antibiotics in a controlled mouse model. The second dataset came from a study of the differences between conventional and organic-raised cattle. Although it was possible to find statistically significant correlations between the insertion sequences and antibiotic resistance genes in both datasets, several challenges remain to better understand the contribution of insertion sequences to the motility of antibiotic resistance genes. Obtaining more complete and less fragmented metagenomes with long-read sequencing technologies could make it possible to understand the mechanisms favoring horizontal transfers within the microbiome with greater precision.
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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 | 0.024 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".