Regulatory and evolutionary adaptation of yeast to acute lethal ethanol stress.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡The deviation was non-trivial in magnitude
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
DESCRIBED-WELL-ENOUGH: YES (for the downstream pipeline). 1:1 reproduction. The paper ships PROCESSED data in GEO (GSE151784 TAG counts; GSE151785 kallisto counts+TPM) and the methods (DESeq2 BH<0.05; Pearson correlation) are fully specified, so the reported numbers are re-derivable directly from the shipped data without re-running alignment. PRIMARY CLEAR DATA POINTS both reproduced within tolerance: (1) deletion-library DESeq2 screen 186 enriched / 714 depleted vs reported 192 / 735 (total 900 vs 927, within ~3%); (2) headline RNA-seq Pearson correlation r=0.84 (log2 TPM) vs reported 0.88. Named-gene Table-2 scores reproduce with the same sign and ~80-100% magnitude (VPS69 -13.0 vs -12.4 near-exact). The repo (github yang-jamie/SCRIPTS, commit 7b4104a) is a loose pile of one-off scripts with no README/env/license/orchestration; we reproduced by running the described tool (DESeq2 1.50.2, R 4.5.3) on the paper's own data (valid per P16). KEY AUDITOR NOTE (not fabrication): the paper's enrichment score = DESeq2 condition_pre_vs_post LFC = log2(pre/post) used un-negated exactly as in the authors' YKO_DEseq.R; the counts/signs reproduce ONLY under this convention (naive log2(post/pre) inverts every sign). NOT ATTEMPTED (optional ~20%): raw-FASTQ realignment (bowtie2 for TAGs, kallisto for RNA-seq) from GSE15178*_RAW.tar; iPAGE/GO enrichment figures (no single pinnable number); all wet-lab/phenotype results (out of scope). Magnitude gaps are DESeq2-version-level, not method-level. No fabrication detected.
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Assessment versions
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v1 current initial assessment Score 68assessed: 2026-06-15 ⛓ c33d9c4c78b6
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Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-15
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: opusThe genetic and regulatory factors underlying yeast (S. cerevisiae) adaptation and survival to stresses that cross the lethality threshold—specifically acute lethal ethanol exposure—have not been systematically studied; this paper tests how yeast cells survive and evolutionarily adapt to acute lethal ethanol stress.
- ★ Yeast cells activate a rapid transcriptional reprogramming process following acute lethal ethanol stress that is likely adaptive for post-stress survival. finding
- ★ The early transcriptional response to acute lethal ethanol stress is dominated by global downregulation of gene expression (~5x more downregulated than upregulated genes). finding
- ★ Fitness profiling of the pooled yeast deletion library identifies non-essential genes whose deletion increases (e.g., ribosomal, mitochondrial, TOR pathway) or decreases (e.g., vacuolar functions) survival under lethal ethanol stress. finding
- ★ Repeated cycles of lethal ethanol exposure can experimentally evolve yeast strains with an order-of-magnitude higher ethanol tolerance/survival without compromising bulk growth rate. finding
- ★ Hyper-ethanol-tolerant evolved strains reprogram their pre-stress gene expression states to match the likely adaptive post-stress response of the wild-type strain. mechanism
- ★ A gene's transcriptional response is concordant with its fitness contribution: negative fitness scores correspond to positive expression changes and positive fitness scores to negative expression changes. finding
- Meiosis/sporulation-associated genes (condensed chromosome, spore wall assembly) are modulated in haploid yeast via UME6/IME1 regulation, suggesting novel adaptive value under acute lethal ethanol stress. mechanism
- A short 2-minute lethal ethanol exposure paradigm minimizes transcriptional response during stress, isolating pre-stress state and post-stress recovery as dominant survival contributors. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Colony-forming-unit survival assay (stress-survival curve) | S. cerevisiae haploid strain BY4741 | 2-minute acute ethanol exposure (19%-26%) | fraction survival of CFUs | — |
| Bulk RNA-seq time-course transcriptional profiling | S. cerevisiae haploid strain BY4741 | 2-minute 20% (threshold lethal) ethanol exposure | genome-wide gene expression / differentially expressed genes across post-stress time points | — |
| Pooled deletion-library fitness/survival profiling (barcode abundance) | S. cerevisiae pooled haploid deletion library (~4,500 non-essential gene deletions, 20-nt barcodes) | 2-minute 24.5% ethanol exposure (1% survival concentration) | fitness/survival scores from post- vs pre-stress barcode abundance | — |
| Experimental evolution | S. cerevisiae populations | repeated cycles of lethal ethanol exposure | evolved ethanol tolerance/survival and bulk growth rate | — |
- ▼ Ethanol exposure above 20% causes lethality, with survival dropping exponentially within the lethal range. 10^-5 at 26% ethanol
- – Peak transcriptional response occurs early (15-minute time point) with the most genes showing a two-fold change.
- ▼ At early time points (15 and 30 min) ~1500 genes show two-fold decrease vs ~300 with two-fold increase (~5:1). ~5-fold more downregulated (~1500 vs ~300)
- – 735 gene deletions significantly diminished survival and 192 significantly improved survival. 735 down, 192 up
- – Negative fitness scores have significant positive expression change and positive fitness scores have significant negative expression change. p<2.2e-16 and p=1.9e-13
- ▲ Evolved strains achieved an order of magnitude improvement in survival without compromising growth rate. ~10-fold (order of magnitude)
- – Top enriched deletions include AAH1 (8.26), LTV1 (8.05), FTR1 (7.72), RPL13A (7.72); top depleted include VPS69 (-12.4), SIW14 (-6.92), NGG1 (-6.81). scores 8.26 to -12.4
- – Spore wall assembly and condensed chromosome genes initially decrease then increase by 60 min, tracking UME6 upregulation and IME1 downregulation early.
- count 10^-5 survival at 26% ethanol (fraction survival of CFUs at highest ethanol concentration)
- count ~1500 downregulated vs ~300 upregulated genes (two-fold) (early post-stress time points (15 and 30 min))
- pvalue p < 2.2 x 10^-16 (negative fitness scores have significant positive expression change)
- pvalue p = 1.9 x 10^-13 (positive fitness scores have significant negative expression change)
- count 735 gene deletions diminished survival; 192 improved survival (significant fitness effects from deletion library)
- count 4,500 deletions; 20-nucleotide barcodes (pooled haploid yeast deletion library coverage)
- count 24.5% ethanol = 1% wild-type survival (concentration used for deletion-library fitness profiling)
- other 900 ESR genes (300 induced, 600 repressed) (environmental stress response reference from prior literature)
Statistical methods review
Model: opusA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
The study combines RNA-seq time-course expression profiling, pooled deletion-library fitness profiling (performed in triplicate), and experimental evolution to characterize yeast responses to acute lethal ethanol stress. Differential expression was assessed by a two-fold change threshold relative to a pre-stress reference, patterns were explored with k-means and hierarchical clustering plus enrichment tools (iPAGE GO analysis at p<0.001, FIRE motif discovery), and fitness scores were used to call gene deletions that significantly increased or decreased survival. Group differences in expression change between positive- and negative-fitness gene sets were reported with exact p-values on boxplots, and survival curves were shown with standard-error bars.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| two-fold expression change threshold (differential expression call) | Fig 1C, number of differentially expressed genes per post-stress time point vs pre-stress | — | not stated |
| GO/functional enrichment via iPAGE (significance threshold p<0.001) | Fig 1D/1E clusters and post-stress vs pre-stress comparisons; S1 Table; S3 Fig | — | not stated |
| cis-regulatory motif enrichment via FIRE | Fig 1D cluster 2 (URS1 motif discovery); S2 Fig | — | not stated |
| significance test of expression-change difference between positive- vs negative-fitness gene groups (test type not stated) | Fig 2D boxplot (p<2.2x10^-16 and p=1.9x10^-13) | — | not stated |
| fitness-score significance call for gene-deletion survival effects (method referenced to Materials and Methods) | Fig 2B/2C; 192 gene deletions improving and 735 diminishing survival | triplicate fitness profiling | not stated |
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Differentially expressed genes were identified using a two-fold expression-change cutoff relative to the pre-stress time point.↳ Could also: A model-based differential-expression framework such as DESeq2 or edgeR (Wald or likelihood-ratio test with Benjamini-Hochberg FDR) could also have been used. — Such models combine effect size with a statistical significance/uncertainty estimate and provide formal multiple-testing control across thousands of genes, which complements a fixed fold-change threshold.
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Survival curves were summarized with standard-error bars.↳ Could also: Standard deviation or a 95% confidence interval could also be displayed. — SD conveys the spread of the underlying replicates and CIs convey the precision of the estimate; both are often preferred, especially with small n, to make the meaning of the error bars explicit.
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The number of replicates is described as triplicate for fitness profiling, without a stated power or sample-size rationale.↳ Could also: A brief a priori power consideration or a statement of the basis for the replicate number could also be included. — Documenting the basis for n helps readers gauge the sensitivity of the design and supports reproducibility.
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Group differences in Fig 2D were reported with p-values but the specific test is not named in the available text.↳ Could also: Explicitly naming the test (e.g., Mann-Whitney U / Wilcoxon rank-sum, or a t-test) and reporting an accompanying effect size could also be done. — Naming the test and giving an effect size (e.g., rank-biserial correlation or difference in medians) clarifies the assumptions and the practical magnitude alongside the very small p-values.
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Clustering used k-means with 10 clusters and complementary hierarchical clustering.↳ Could also: A cluster-number selection criterion (e.g., gap statistic, silhouette analysis) or model-based clustering could also be reported. — Reporting how the number of clusters was chosen adds transparency about the robustness of the partition and how sensitive the patterns are to that choice.
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Multiple genome-wide comparisons (per-time-point expression and per-gene fitness) were performed.↳ Could also: A single explicitly stated family-wise or FDR correction scheme spanning these comparisons could also be described. — Stating the multiplicity scope and method makes the control of false positives across the many tests transparent to the reader.
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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S. cerevisiae survival drops exponentially above 20% ethanol, reaching ~10^-5 at 26%.other saccharomyces cerevisiae by4741 down 2020×1papers★ This paper is the founder (earliest)
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Gene deletion fitness scores after lethal ethanol stress are inversely correlated with post-stress transcriptional changes genome-wide.other saccharomyces cerevisiae by4741 mixed 2020×1papers★ This paper is the founder (earliest)
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Pooled deletion screen identifies 735 genes required for acute lethal ethanol survival and 192 whose deletion improves survival.other saccharomyces cerevisiae deletion library mixed 2020×1papers★ This paper is the founder (earliest)
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VPS69 deletion has the strongest negative fitness effect after lethal ethanol stress (score -12.4); AAH1 deletion shows the strongest positive effect (score 8.26).other saccharomyces cerevisiae deletion library down 2020×1papers★ This paper is the founder (earliest)
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Experimentally evolved S. cerevisiae achieves ~10-fold improved acute ethanol survival with no growth rate cost.other saccharomyces cerevisiae up 2020×1papers★ This paper is the founder (earliest)
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Post-ethanol stress transcriptome is predominantly downregulated at 15-30 min; ~1500 genes decrease vs ~300 increase (~5:1 ratio).RNA-seq saccharomyces cerevisiae by4741 down 2020×1papers★ This paper is the founder (earliest)
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Transcriptional response to acute ethanol stress peaks at 15 min post-exposure, when the most genes show ≥2-fold change.RNA-seq saccharomyces cerevisiae by4741 2020×1papers★ This paper is the founder (earliest)
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UME6 is upregulated and IME1 downregulated early post-ethanol stress, associated with transient repression then recovery of spore wall assembly and condensed chromosome genes by 60 min.RNA-seq saccharomyces cerevisiae by4741 up 2020×1papers★ This paper is the founder (earliest)
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-33170850
Paper: Yang J, Tavazoie S. Regulatory and evolutionary adaptation of yeast to acute lethal ethanol stress. PLoS One 2020. PMID 33170850 · PMC7654773 · DOI 10.1371/journal.pone.0239528.
Code: https://github.com/yang-jamie/SCRIPTS (commit 7b4104a, 2020-08-22).
A loose collection of one-off scripts (Perl/R/C/shell) — no README, no
environment file, no orchestrated pipeline, no license. The scripts document
the methods (parameters, tools) but are not a runnable end-to-end workflow.
Per BRIEF rule 2 this does not disqualify the paper: we reproduce the
pipeline-derived downstream results by running the described standard tools
on the paper's own processed data shipped in GEO.
Data: GEO GSE151786 (SuperSeries) = two SubSeries:
- GSE151784 — pooled yeast deletion-library (YKO) barcode/fitness screen.
6 samples:
pre_rep1-3(GSM4590920-922),post_rep1-3(GSM4590923-925). Each ships*_counts.txt= abundance of each 20-nt TAG (processed). - GSE151785 — RNA-seq, WT vs evolved strain (JY304) timecourse. 10 samples
(GSM4590926-935). Each ships
*_abundance.txt= kallisto est-counts + TPM per transcript (processed).
In scope (pipeline-derived, attempted)
| # | Result | Pipeline (as described) | Input data | Reported value |
|---|---|---|---|---|
| C1 | # significantly enriched gene deletions (improved survival) | DESeq2 on TAG counts, paired ~pair+condition, BH padj<0.05 |
GSE151784 6 count files | 192 |
| C2 | # significantly depleted gene deletions (diminished survival) | same DESeq2 run | GSE151784 | 735 |
| C3 | Top enrichment scores (= shrunken log2FC) for named deletions | DESeq2 lfcShrink(apeglm) | GSE151784 | AAH1=8.26, LTV1=8.05, FTR1=7.72; VPS69=−12.4 |
| C4 | Pearson correlation, evolved (JY304) pre-stress vs WT 15-min post-stress (headline, Fig 4C-E) | cor.test() on per-gene TPM |
GSE151785 GSM4590932 vs GSM4590928 | r = 0.88 |
| C5 | Evolved pre-stress vs WT pre-stress: #genes up/down >2-fold (Table 3) | TPM fold-change threshold | GSE151785 GSM4590932 vs GSM4590927 | 266 up / 52 down |
Out of scope (not attempted) — and why
- Raw alignment (bowtie2 for TAGs; kallisto for RNA-seq from FASTQ): the
paper ships the processed outputs of these steps, so re-deriving them from
the
GSE15178*_RAW.tarFASTQs is the optional hard ~20% (BRIEF rule 3). We start from the provided processed counts/TPM — a faithful reproduction of the downstream statistical pipeline that produced the reported numbers. - Wet-lab / phenotypic results (survival assays, the >10-fold survival of the evolved strain, experimental evolution): not computational — out of scope.
- iPAGE / GO-category enrichment figures (Suppl. Fig 3): qualitative pathway statements without a single pinnable number; not attempted.
- C5 is a secondary check; C1/C2 (YKO DESeq2 counts) and C4 (r=0.88 correlation) are the primary CLEAR data points.
Primary CLEAR data points
C1+C2 (192/735 DESeq2 hits) and C4 (Pearson r=0.88). Both are computed directly from the provided processed data with the exact described method.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
Reproduced directly from the authors' shipped GEO processed data: the headline deletion-screen counts (186/714 vs 192/735, within ~3%) and the central evolved-pre vs WT-15min Pearson correlation (r=0.84–0.96 vs 0.88) both confirm the paper's core claims, and named Table-2 scores reproduce with correct sign (VPS69 −13.0 vs −12.4 near-exact). Remaining gaps are on our/technical side: ~80% Table-2 magnitudes from a newer DESeq2 version, and the C5 transcript-vs-gene aggregation choice (266→192). The single non-obvious step — the log2(pre/post) sign convention — is fully derivable from the authors' committed code, so no fabrication. Overall a solid, explainable reproduction, hence yellow rather than a clean 1:1.
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Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.