A quantitative metabolomics study of high sodium response in Clostridium acetobutylicum ATCC 824 acetone-butanol-ethanol (ABE) fermentation
Bibliographic record
Abstract
Hemicellulose hydrolysates, sugar-rich feedstocks used in biobutanol refinery, are normally obtained by adding sodium hydroxide in the hydrolyze process. However, the resulting high sodium concentration in the hydrolysate inhibits ABE (acetone-butanol-ethanol) fermentation, and thus limits the use of these low-cost feedstocks. We have thus studied the effect of high sodium on the metabolic behavior of Clostridium acetobutyricum ATCC 824, with xylose as the carbon source. At a threshold sodium concentration of 200 mM, a decrease of the maximum cell dry weight (-19.50 ± 0.85%) and of ABE yield (-35.14 ± 3.50% acetone, -33.37 ± 0.74% butanol, -22.95 ± 1.81% ethanol) were observed compared to control culture. However, solvents specific productivities were not affected by supplementing sodium. The main effects of high sodium on cell metabolism were observed in acidogenesis, during which we observed the accumulation of ATP and NADH, and the inhibition of the pentose phosphate (PPP) and the glycolytic pathways with up to 80.73 ± 1.47% and 68.84 ± 3.42% decrease of the associated metabolic intermediates, respectively. However, the NADP(+)-to-NADPH ratio was constant for the whole culture duration, a phenomenon explaining the robustness of solvents specific productivities. Therefore, high sodium, which inhibited biomass growth through coordinated metabolic effects, interestingly triggered cell robustness on solvents specific productivity.
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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) | 0.001 | 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".