Adaptive evolution of <i>Clostridium thermocellum</i> ATCC 27405 on alternate carbon sources leads to altered fermentation profiles
Bibliographic record
Abstract
Consolidated bioprocessing candidate, Clostridium thermocellum, is a cellulose hydrolysis specialist, with the ability to ferment the released sugars to produce bioethanol. C. thermocellum is generally studied with model substrates Avicel and cellobiose to understand the metabolic pathway leading to ethanol. In the present study, adaptive laboratory evolution, allowing C. thermocellum DSM 1237 to adapt to growth on glucose, fructose, and sorbitol, with the prospect that some strains will adapt their metabolism to yield more ethanol. Adaptive growth on glucose and sorbitol resulted in an approximately 1 mM and 2 mM increase in ethanol yield per millimolar glucose equivalent, respectively, accompanied by a shift in the production of the other expected fermentation end products. The increase in ethanol yield observed for sorbitol adapted cells was due to the carbon source being more reduced compared to cellobiose. Glucose and cellobiose have similar oxidation states thus the increase in ethanol yield is due to the rerouting of electrons from other reduced metabolic products excluding H2 which did not decrease in yield. There was no increase in ethanol yield observed for fructose adapted cells, but there was an unanticipated elimination of formate production, also observed in sorbitol adapted cells suggesting that fructose has regulatory implications on formate production either at the transcription or protein level.
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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 | 0.001 |
| 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".