How to characterize a strain? Clonal heterogeneity in industrial <i>Saccharomyces</i> influences both phenotypes and heterogeneity in phenotypes
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
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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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 | 0.001 |
| 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 it