Growth of Gram-negative uropathogens in human urine under hypoxic conditions produces clinically relevant metabolomic profiles
Bibliographic record
Abstract
SUMMARY Background Much work on uropathogens is done in rich laboratory media that do not reflect the nutrient availability of urine. Also, the environment of the bladder is microaerobic (∼5 % O 2 ) but routine laboratory work with uropathogens is done under aerobic conditions. Aims To understand the influence of oxygen conditions (aerobic, microaerobic) and physiologically relevant growth substrates (artificial urine, human urine) on the ability of bacteria isolated from catheter-associated urinary tract infections (CAUTIs) to form biofilms, and to begin to define the CAUTI bacterial metabolome. Methods Biofilm assays were conducted in rich lab medium (tryptone soy broth supplemented with glucose), and commercially available artificial and pooled human urine for 29 well-characterized uropathogens representing five different genera of Gram-negative bacteria. Spent media were collected and analysed using 400 MHz 1 H-NMR, to quantify major metabolites produced by uropathogens in artificial urine and human urine. Findings There was a significant decrease in biofilm formation for all uropathogens when grown in physiologically relevant growth substrates. Untargeted metabolomic analyses of artificial and human urine showed the artificial urine used in this study did not recapitulate the complexity of major metabolites present in human urine. Further analyses of spent human urine highlighted significant increases in acetate production by the uropathogens when they were grown under microaerobic compared with aerobic conditions. Conclusions Growth of uropathogenic Enterobacteriaceae under physiologically relevant conditions (i.e. human urine, 5 % O 2 ) generates data more relevant to clinical disease and is an important consideration for future work on bacteria causing urinary tract infections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".