Temporal analysis of xylose fermentation by <i>Scheffersomyces stipitis</i> using shotgun proteomics
Bibliographic record
Abstract
Proteomics and fermentation technology have begun to integrate to investigate fermentation organisms in bioprocess development. This is the first shotgun proteomics study employed to monitor the proteomes of Scheffersomyces stipitis during xylose fermentation under oxygen limitation. We identified 958 nonredundant proteins and observed highly similar proteomes from exponential to early stationary phases. In analyzing the temporal proteome, we identified unique expression patterns in biological processes and metabolic pathways, including alternative respiration salicylhydroxamic acid (SHAM) pathway, activation of glyoxylate cycle, expression of galactose enzymes, and secondary zinc-containing alcohol dehydrogenase and O-glycosyl hydrolases. We identified the expression of a putative, high-affinity xylose sugar transporter Xut1p, but low-affinity xylose transporters were absent. Throughout cell growth, housekeeping processes included oxidative phosphorylation, glycolysis, nonoxidative branch of the pentose phosphate pathway, gluconeogenesis, biosynthesis of amino acids and aminoacyl total RNA (tRNA), protein synthesis and proteolysis, fatty acid metabolism, and cell division. This study emphasized qualitative analysis and demonstrated that shotgun proteomics is capable of monitoring S. stipitis fermentation and identifying physiological states, such as nutrient deficiency.
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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".