Comparative shotgun proteomic analysis of Clostridium acetobutylicum from butanol fermentation using glucose and xylose
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Butanol is a second generation biofuel produced by Clostridium acetobutylicum through acetone-butanol-ethanol (ABE) fermentation process. Shotgun proteomics provides a direct approach to study the whole proteome of an organism in depth. This paper focuses on shotgun proteomic profiling of C. acetobutylicum from ABE fermentation using glucose and xylose to understand the functional mechanisms of C. acetobutylicum proteins involved in butanol production. RESULTS: We identified 894 different proteins in C. acetobutylicum from ABE fermentation process by two dimensional - liquid chromatography - tandem mass spectrometry (2D-LC-MS/MS) method. This includes 717 proteins from glucose and 826 proteins from the xylose substrate. A total of 649 proteins were found to be common and 22 significantly differentially expressed proteins were identified between glucose and xylose substrates. CONCLUSION: Our results demonstrate that flagellar proteins are highly up-regulated with glucose compared to xylose substrate during ABE fermentation. Chemotactic activity was also found to be lost with the xylose substrate due to the absence of CheW and CheV proteins. This is the first report on the shotgun proteomic analysis of C. acetobutylicum ATCC 824 in ABE fermentation between glucose and xylose substrate from a single time data point and the number of proteins identified here is more than any other study performed on this organism up to this report.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it