The effect of growth environment on the phenotypic and transcriptomic response of Pseudomonas aeruginosa to antibiotic treatment
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a global health crisis, killing 700,000 people annually. Biofilm formation has been implicated in higher levels of AMR through preventing diffusion of antibiotics to their targets. Understanding how AMR arises and how to detect it is an important area of research to undertake. Pseudomonas aeruginosa is a key nosocomial pathogen which is greatly implicated in chronic infections. This thesis focuses on antibiotic susceptibility of P. aeruginosa when grown in different environments. Here I tested liquid culture media: Mueller-Hinton broth (MHB), synthetic cystic fibrosis sputum media (SCFM) and a synthetic wound fluid (SWF). Additionally, I tested three biofilm models: the Calgary assay, where P. aeruginosa is grown in previously mentioned liquid media, the ex vivo pig lung CF model (EVPL) and a synthetic wound model (SWM). \n \n The antibiotic susceptibility of P. aeruginosa varies depending on the growth media and type of biofilm model used. Growth in SCFM and SWF showed increased resistance to colistin compared with MHB. Metabolomics research showed different preferences for P. aeruginosa carbon metabolism between the media and different metabolic responses when treated with colistin. Despite antimicrobial susceptibility tests (ASTs) routinely testing planktonic bacterial growth, during infections bacteria are commonly found living as a biofilm. Growth in the EVPL and SWM biofilm models showed increased resistance compared with the Calgary model. A P. aeruginosa PA14 transposon mutant pelA_, which cannot produce a fully mature biofilm, showed equally high levels of resistance as the WT when grown in the biofilm models. Thus, suggesting mechanisms beyond biofilm production aid in the increase in AMR. RNA-sequencing of PA14 genotypes grown in the EVPL model ± colistin, showed similar expression of cationic antimicrobial peptide (CAMP) resistance genes but significant differences in the e✏ux pumps between the two genotypes. \n \nOverall, this highlights the importance of considering growth environment when trying to understand the biology and clinical implications of AMR. \n
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".