Investigating the Effects of Genetic Background on the Fitness of Quinolone Resistance Mutations in Escherichia Coli
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is one of the largest threats to public health and puts a serious strain on healthcare systems around the world.Many first line antibiotics can no longer be used to fight infections due to increased resistance.The fitness of AMR strains of bacteria is determined in part by epistasis, whereby resistance mutations may have different effects on different genetic backgrounds.Thus, the objective of this research is to investigate the effects of genotype on the fitness of AMR.Using genetic techniques in E. coli, quinolone resistance alleles of the gyrA gene (S83L, D87N, and a combination of S83L-D87N) were transferred into a collection of knockout strains, resulting in approximately 12 000 double-mutants.Genetic interactions that affected fitness, both positively and negatively, were common and a variety of synthetic lethal/sick interactions were found.A number of the genes lethal with gyrA mutations were involved in DNA synthesis, repair, and replication, much like gyrA itself.Using strains that were able to conditionally express the knocked out genes, yield and growth curves were examined over a 24 hour period to validate the synthetic lethal interactions.Results varied for each strain -of 33 strains assayed, 21 showed a deficit in at least one fitness-related phenotype.Overall, more work should be done to further examine these putative synthetic lethal interactions.Once done, looking at all these genetic interactions will help to untangle the functionality of certain genes and, in the context of AMR, identification of synthetic lethal interactions of AMR mutations may lead to drug targets that can specifically kill resistant bacteria.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".