Metabolic footprinting of the anaerobic bacterium <i>Fusobacterium</i> <i>varium</i> using 1H NMR spectroscopy
Bibliographic record
Abstract
Metabolic footprinting of the anaerobic bacterium Fusobacterium varium demonstrated the accumulation of six carboxylic acids as metabolic end-products and revealed specific growth requirements and utilization capabilities towards amino acids. Guided by (1)H NMR determinations of residual amino acids in spent medium, a modified chemically defined minimal medium (CDMM*) was developed by minimizing the amino acid composition while satisfying nutritional requirements to support abundant growth of F. varium. Quantitative determinations of carboxylate salts and residual substrates were readily performed by (1)H NMR analysis of lyophilized residues from CDMM* cultures without interference from initial medium components. Only small concentrations of alanine, arginine, glycine, isoleucine, leucine, methionine, proline and valine were required to support growth of F. varium, whereas larger quantities of aspartate, asparagine, cysteine, glutamine, glutamate, histidine, lysine, serine and threonine were utilized, most likely as energy sources. Both bacterial growth and the distribution of carboxylate end-products depended on the composition of the chemically defined medium. In cultures provided with glucose as the primary energy source, the accumulation of butyrate and lactate correlated with growth, consistent with the regeneration of reduced coenzyme formed by the oxidative steps of glucose catabolism.
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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) | 0.000 | 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 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".