Coupling next-generation sequencing to dominant positive screens for finding antibiotic cellular targets and resistance mechanisms in Escherichia coli
Bibliographic record
Abstract
In order to expedite the discovery of genes coding for either drug targets or antibiotic resistance, we have developed a functional genomic strategy termed Plas-Seq. This technique involves coupling a multicopy suppressor library to next-generation sequencing. We generated an Escherichia coli plasmid genomic library that was transformed into E. coli. These transformants were selected step by step using 0.25× to 2× minimum inhibitory concentrations for ceftriaxone, gentamicin, levofloxacin, tetracycline or trimethoprim. Plasmids were isolated at each selection step and subjected to Illumina sequencing. By searching for genomic loci whose sequencing coverage increased with antibiotic pressure we were able to detect 48 different genomic loci that were enriched by at least one antibiotic. Fifteen of these loci were studied functionally, and we showed that 13 can decrease the susceptibility of E. coli to antibiotics when overexpressed. These genes coded for drug targets, transcription factors, membrane proteins and resistance factors. The technique of Plas-Seq is expediting the discovery of genes associated with the mode of action or resistance to antibiotics and led to the isolation of a novel gene influencing drug susceptibility. It has the potential for being applied to novel molecules and to other microbial species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".