How to characterize a strain? Clonal heterogeneity in industrial <i>Saccharomyces</i> influences both phenotypes and heterogeneity in phenotypes
Bibliographic record
Abstract
Populations of microbes are constantly evolving heterogeneity that selection acts upon, yet heterogeneity is nontrivial to assess methodologically. The necessary practice of isolating single-cell colonies and thus subclone lineages for establishing, transferring, and using a strain results in single-cell bottlenecks with a generally neglected effect on the characteristics of the strain itself. Here, we present evidence that various subclone lineages for industrial yeasts sequenced for recent genomic studies show considerable differences, ranging from loss of heterozygosity to aneuploidies. Subsequently, we assessed whether phenotypic heterogeneity is also observable in industrial yeast, by individually testing subclone lineages obtained from products. Phenotyping of industrial yeast samples and their newly isolated subclones showed that single-cell bottlenecks during isolation can indeed considerably influence the observable phenotype. Next, we decoupled fitness distributions on the level of individual cells from clonal interference by plating single-cell colonies and quantifying colony area distributions. We describe and apply an approach using statistical modeling to compare the heterogeneity in phenotypes across samples and subclone lineages. One strain was further used to show how individual subclonal lineages are remarkably different not just in phenotype but also in the level of heterogeneity in phenotype. With these observations, we call attention to the fact that choosing an initial clonal lineage from an industrial yeast strain may vastly influence downstream performances and observations on karyotype, on phenotype, and also on heterogeneity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".