Metabolomics survey of uropathogenic bacteria in human urine
Bibliographic record
Abstract
Abstract Urinary tract infections (UTIs) are one of the most prevalent infections in North America and are caused by a diverse range of bacterial species. Although uropathogenesis has been studied extensively in the context of macromolecular interactions, the degree to which metabolism may contribute to infection is unclear. Currently, most of what is known about the metabolic capacity of uropathogens has been derived from genomics, genetic knockout studies or transcriptomic analyses. However, there are currently very little empirical data on the metabolic activity of uropathogens when grown in urine. To address this gap, we conducted a systematic survey of the metabolic activities of eight of the most common uropathogenic bacterial species that collectively represent 99% of uncomplicated UTIs. Liquid chromatography-mass spectrometry (LC-MS) analyses of human urine cultures revealed that uropathogens have four distinct metabolic clades. We generalized these clades as serine consumers ( Escherichia coli, Klebsiella pneumoniae and Proteus mirabilis ), glutamine consumers ( Pseudomonas aeruginosa ), amino acid abstainers ( Enterococcus faecalis and Streptococcus agalactiae ), and amino acid minimalists ( Staphylococcus aureus and Staphylococcus saprophyticus ). These metabolic classifications can be further subdivided on a species-to-species level. This survey provides a framework to understanding the metabolic activity of the diverse range of uropathogens and how these species use divergent metabolic strategies to occupy the same niche.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".