Proteomic investigation of glucose metabolism in the butyrate‐producing gut anaerobe <b><i>Fusobacterium varium</i></b>
Bibliographic record
Abstract
A proteome survey and MS analysis were conducted to investigate glucose metabolism in Fusobacterium varium, a butyrate-producing constituent of the indigenous human gut microflora. The bacterium was capable of catabolizing glucose as the main energy source via the Embden-Meyerhof-Parnas pathway. 2-DE analyses revealed that the apparent concentrations of the six identified glycolytic enzymes (pyruvate kinase, enolase, glucose-6-phosphate isomerase, phosphoglycerate kinase, triosephosphate isomerase, and glyceraldehyde-3-phosphate dehydrogenase) were specifically increased in response to the presence of glucose in the chemically defined minimal growth medium, and did not diminish when the medium was additionally supplemented with L-glutamate, an amino acid readily fermented by members of the Fusobacterium genus. A substrate pool depletion study revealed that the sugar, and not the amino acid, is the more efficient growth substrate. Both proteomics and substrate pool depletion studies revealed that F. varium can simultaneously utilize both glucose and L-glutamate as energy sources. Enzymes involved in L-glutamate metabolism were also identified, including an NAD-dependent glutamate dehydrogenase and two enzymes of the methylaspartate pathway of L-glutamate catabolism (glutamate mutase and methylaspartate ammonia-lyase). Their apparent intracellular concentrations were elevated when the bacterium was cultured in media supplemented with excess L-glutamate. Our observation that the apparent concentrations of specific proteins were elevated in response to a particular growth substrate supplied as an energy source provides the first evidence for the presence of a nutrient-responsive mechanism governing intracellular protein concentration in F. varium.
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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 | 0.001 |
| 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".