Population dynamics analysis of <i>Saccharomyces cerevisiae</i> deletion library during fed-batch cultivation using Bar-seq
Bibliographic record
Abstract
Abstract To understand the genetic basis of changes in strain physiology during industrial fermentation, and the corresponding roles these genes play in strain performance, we employed a barcoded yeast deletion library to assess genome-wide strain fitness across a simulated industrial fermentation regime. Our results demonstrate the utility of Bar-seq to assess fermentation associated stresses in yeast populations under industrial conditions. We find that mutant population diversity is maintained through multiple seed trains, enabling for large scale fermentation selective pressures to act upon the community. We identify specific deletion mutants that were enriched in all processes, independent of the cultivation conditions, which include MCK1, RIM11, MRK1 , and YGK3 that encode homologues of mammalian glycogen synthase kinase 3 (GSK-3). Further, we show that significant changes in the population diversity during fed-batch cultivations reflect the presence of significant external stresses, such as the accumulation of the fermentative byproduct ethanol. The mutants that were lost during the time of most extreme population selection suggest that specific biological processes may be required to cope with these specific stresses. Overall our work highlights a promising avenue to identify genetic loci and biological stress responses required for fitness under industrial conditions.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".