Enterotoxins A and B produced by Staphylococcus aureus increase cell proliferation, invasion and cytarabine resistance in acute myeloid leukemia cell lin
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Reported values were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡Could not use the authors’ exact input data
- 🔴A deviation was attributed to the published material
- 🟡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 to reproduce the DETERMINISTIC half of the paper's only own-code pipeline 1:1 (exact). The cytarabine IC50 calculation uses the authors' own published R tool SixModelIC50 V3.r (commit da87c34); reported output == Supplementary Table S1 == the tool's DrugData.txt. On «our HPC» (R 4.5.3, conda) we re-derived S1's log(IC50)/log(IC90)/log(IC95) for all 6 AML cell lines' 3-Parameter-Bottom-0 rows from the reported log(EC50)+Amax using the authors' exact inline formula: 18/18 values matched to <=7.55e-15 (floating-point identity), cross-checked in Python. The lowest-Std.Err model-selection reproduces all 6 headline IC50s (Supp Table S2 / Fig.4) and 10^logIC50 vs the 2 uM cut reproduces the resistant/sensitive grouping exactly. The full script also runs cleanly (one-line Windows->POSIX patch) on synthetic input, emitting the documented 10-column DrugData.txt (tool is runnable; not an env/docs drop). NOT reproducible / not attempted: (a) the nls curve FIT itself, because the raw dose-response measurements (the script's inputFiles) were NEVER deposited anywhere -- this is the precise reproducibility gap; (b) the s-vs-r t-test (needs raw replicates); (c) the E-MTAB-783 limma/GSEA transcriptomics branch (deliberately out of scope -- data is public but cell-line->CGP mapping, the limma partition n=2 vs n=4, and the 15-DEG list are underspecified for a clean 1:1). Integrity: no arithmetic red flags -- every derivable S1 value is the exact deterministic output of the published formula; one cosmetic label typo (S1 'Kasumi-9' vs text 'Kasumi-1').
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
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v1 current initial assessment Score 88assessed: 2026-06-15 ⛓ 365491a37d06
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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: opusThis study tests whether two Staphylococcus aureus enterotoxins, SEA and SEB, alter cell proliferation, migration, invasion, and Cytarabine resistance of acute myeloid leukemia (AML) cell lines, and aims to identify the immune-related genes and pathways responsible for these changes.
- ★ SEA and SEB treatment increases proliferation of AML cell lines. finding
- ★ SEA and SEB increase Cytarabine resistance in AML cell lines. finding
- ★ SEA and SEB enhance the migration and invasion capacity of AML cells. finding
- ★ Toxin-induced changes involve dysregulation of immune-related genes/genesets, possibly via the ER stress signaling pathway allowing AML cells to escape the toxin environment. mechanism
- There may be a strong interaction between immune-related pathways and the ER signaling pathway. mechanism
- A six-model (6M) nonlinear regression IC50 calculation approach was used to compute drug/toxin cytotoxicity values selecting the lowest standard error. method
- Transcriptomic, Limma differential expression, GeneMANIA network, DAVID pathway, and GSEA analyses identify immune genes/pathways linked to resistance and invasion. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Cell proliferation (ATP luminescence) assay | AML cell lines GDM-1, CESS, HL-60, QIMR-WIL, P31/FUJ, Kasumi-1 | SEA/SEB toxin (seven concentrations: 100, 50, 10, 5, 1, 0.1, 0.01 ng), 72 h | growth/proliferation percentage | CellTiter-Glo reagent; Turner Designs microplate luminometer |
| Cytotoxicity assay | AML cell lines GDM-1, CESS, HL-60, QIMR-WIL, P31/FUJ, Kasumi-1 | Cytarabine (six concentrations) with or without 10 ng SEA/SEB pretreatment 72 h | percent cell viability / IC50 | CellTiter-Glo; FLUOstar Omega BMG Labtech microplate reader |
| Transwell migration assay | AML cell lines (e.g. GDM-1) | 10 ng SEA and SEB as sole migration trigger | number of migrated cells counted by hemocytometer | Greiner bio-one 8.0 μm pore Transwell inserts; Giemsa stain; inverted microscope |
| Transwell invasion assay | AML cell lines (e.g. GDM-1) | 10 ng SEA and SEB with fibronectin-coated membrane | number of invaded cells | SERVA fibronectin-coated Transwell; Giemsa stain |
| Cell culture well migration image analysis | AML cell lines (e.g. GDM-1) | SEA and SEB, 72 h | cell displacement to well edges (t=0 vs t=72 h) | Nikon Eclipse TS100 inverted microscope |
| Real-time qRT-PCR validation | AML cell lines | SEA/SEB treatment | relative expression (ddCT) of CTSH, ATF5, HLA-DRB4, AZU1, OAT, CD69; GAPDH reference | SYBR Green master mix (Bio-Rad) |
| In silico microarray differential expression / GSEA | Cancer Genome Project (CGP, E-MTAB-783) gene expression data; resistant (HL-60, GDM-1, QIMR-WIL, CESS) vs sensitive (Kasumi-1, P31/FUJ) groups | none (computational) | differentially expressed genes, enrichment scores, pathways | R/Bioconductor (Affy RMA, Limma), Cluster 3.0, GeneMANIA/Cytoscape 3.9.1, DAVID, GSEA C5 GO v6.1 |
- ▲ SEA and SEB increased proliferation of all AML cell lines across all tested concentrations vs control; highest proliferation at 5 ng (SEA) and 10 ng (SEB).
- ▲ SEA/SEB-treated AML cells migrated and invaded markedly more than controls (>50% of treated cells vs ~1% of untreated). ~50-fold
- – In vitro Cytarabine IC50 values correlated strongly with in silico CGP IC50 values (except P31/FUJ). r-sq.: 0.9964, rho:1
- – Transcriptomic, GSEA and PCR validations showed dysregulation of immune-related genes and genesets after toxin treatment.
- ▲ SEA/SEB pretreatment increased Cytarabine resistance (altered IC50) in AML cell lines.
- pvalue p: 0.0090 (SEA), 0.0004 (SEB) at 5 ng (proliferation increase vs control at 5 ng toxin)
- pvalue p: 0.0054 (SEA), 0.0003 (SEB) at 10 ng (proliferation increase vs control at 10 ng toxin)
- fold_change ~50-fold (increased migration/invasion of GDM-1 cells vs control, p<0.0001)
- correlation rho:1, p:0.083; linear reg. p:0.0018, r-sq.:0.9964 (correlation of in vitro vs CGP Cyt IC50 after excluding Kasumi-1 marginal value)
- count >50% treated cells migrated vs ~1% untreated (Transwell migration across six AML cell lines)
- other FDR q < 0.25 (GSEA significance threshold for enriched gene sets)
- pvalue p and q < 0.05 (statistical significance threshold for analyses)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This in vitro and in silico study tested the effects of S. aureus enterotoxins SEA and SEB on AML cell lines using proliferation, migration, invasion, and cytotoxicity assays analyzed with two-tailed t-tests (GraphPad Prism). Transcriptomic differences between Cyt-resistant and Cyt-sensitive cell line groups were identified with Limma linear models and GSEA pathway enrichment using RMA-normalized microarray data. IC50 values were estimated by nonlinear regression with a six-model selection approach, and dispersion throughout was reported as mean ± SD with exact p values.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Two-tailed Student's t-test | Cell proliferation: toxin-treated vs untreated AML cell lines at each concentration (Fig. 1a, 1b) | 3 replicates per condition across 6 cell lines | not stated |
| Two-tailed Student's t-test | Transwell migration and invasion: SEA/SEB-treated vs control (Fig. 2c, Supplementary Fig. S1) | 3 replicates for treated; control described as n=1 | not stated |
| Spearman rank correlation | Correlation between in vitro IC50 and in silico CGP IC50 values (Fig. 3a, 3b) | 6 cell lines (5 after exclusion of P31/FUJ outlier) | not stated |
| Linear regression | In vitro vs in silico IC50 correlation (reported alongside Spearman; r-sq. 0.9964, p=0.0018) | 5 cell lines (P31/FUJ excluded) | not stated |
| Limma moderated linear model (empirical Bayes; R/Bioconductor) | Differential gene expression: Cyt-resistant (HL-60, GDM-1, QIMR-WIL, CESS) vs Cyt-sensitive (Kasumi-1, P31/FUJ) groups | 6 cell lines total (4 resistant, 2 sensitive) | not stated |
| Gene Set Enrichment Analysis (GSEA) with FDR q-value; threshold q < 0.25 | Gene ontology pathway enrichment: resistant vs sensitive groups (whole transcriptome, C5 GO v6.1 database) | 6 cell lines | not stated |
| Nonlinear regression (six-model selection, lowest-standard-error approach) | IC50/EC50/AA/Amax estimation for Cyt, SEA, and SEB across all 6 cell lines | 6–7 concentration points per cell line | not stated |
| qRT-PCR relative quantification (ddCT method) | Validation of selected differentially expressed genes (CTSH, ATF5, HLA-DR4, AZU1, OAT, CD69) | — | not stated |
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Multiple pairwise t-tests were used to compare each toxin concentration vs untreated control across 6 cell lines, with no stated multiplicity correction↳ Could also: A one-way ANOVA followed by Dunnett's test (designed for multiple treatment-vs-single-control comparisons) could also be applied to the same data — When many comparisons share a common control, Dunnett's test controls the family-wise error rate more efficiently than unadjusted t-tests; it is a standard choice in dose-response studies and is available in GraphPad Prism
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Dispersion was uniformly reported as mean ± SD↳ Could also: 95% confidence intervals could also be reported, particularly for the primary outcome measures — With only three replicates, CIs convey both variability and uncertainty in the mean estimate; many current reporting guidelines (e.g., Nature Methods, ARRIVE) recommend CIs alongside or instead of SD for small n
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Limma differential expression was applied with n=6 cell lines total (4 in one group, 2 in the other)↳ Could also: A leave-one-out sensitivity analysis or resampling validation could also be reported alongside the Limma results — Limma's empirical Bayes shrinkage is well-suited for small n, but with only 2 samples in the sensitive group a single cell line strongly influences all estimates; sensitivity checks help characterize how stable the findings are to individual observations
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The GSEA significance threshold was set at FDR q < 0.25 (the Broad Institute's default exploratory cutoff)↳ Could also: A more stringent threshold (e.g., FDR q < 0.05 or 0.10) is also commonly used when results are being highlighted for follow-up validation — The q < 0.25 default is intended for exploratory discovery; stricter thresholds are often applied in publication contexts to limit the proportion of false-positive enriched gene sets reported
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Spearman correlation between in vitro and in silico IC50 values was computed on n=5–6 data points↳ Could also: Bootstrap or permutation-based confidence intervals on rho could also be reported at this sample size — Asymptotic p-values for Spearman's rho are unreliable at very small n; resampling-based CIs provide a more accurate characterization of uncertainty in the correlation estimate
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The working toxin concentration (10 ng) was selected based on visual comparison of proliferation curves and a non-significant t-test between 5 ng and 10 ng conditions↳ Could also: A model-derived parameter (e.g., the EC20 or the inflection point from the six-model nonlinear regression already fitted for IC50) could also serve as the basis for concentration selection — Using a parameter derived from the fitted dose-response model provides a quantitative, reproducible criterion for concentration selection and would be consistent with the curve-fitting framework already in use for IC50 estimation
Citation network
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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 37810000
Turk S, et al. "Enterotoxins A and B produced by Staphylococcus aureus increase cell proliferation, invasion and cytarabine resistance in acute myeloid leukemia cell lines." Heliyon 2023;9(9):e19743. DOI 10.1016/j.heliyon.2023.e19743 · PMC10559070
Computational pipeline(s) reported
| # | Reported result | Pipeline / tool | Data | In scope? |
|---|---|---|---|---|
| P1 | Cytarabine IC50 (log IC50/IC90/IC95, EC50, Activity Area, Amax) for 6 AML cell lines by a 6-model sigmoid fit — Supp Table S1 (mmc1.xlsx), headline values + sensitive/resistant grouping in Supp Table S2 (mmc2.docx) & Fig. 4 |
Authors' own R script SixModelIC50 V3.r (github.com/muratisbilen/6-Model_IC50_CalculationV3, commit da87c34, 2020). Fits 6 sigmoid dose-response models by nls, derives IC/EC from the fit, picks the model with lowest Std.Err. |
wet-lab Cell-Titer-Glo growth%-vs-concentration (6 conc.: 20,10,5,2.5,1,0.1 µM); input data NOT deposited | YES (primary) — but only partially reproducible, see below |
| P2 | Differential expression (15 DEGs, FC>4, p<0.05) sensitive vs resistant; GSEA (Supp S3/S4); DAVID pathways; GeneMANIA network | RMA → limma → GSEA/DAVID/Cytoscape | E-MTAB-783 (CGP, 789 cancer cell lines, Affymetrix HT-HG-U133A v2) — public | partially (see drop note) |
| P3 | Spearman correlation CGP-IC50 vs in-vitro-IC50 (rho, p; r-sq 0.9964) | rank correlation | derived from P1 + CGP | secondary |
Out of scope (wet-lab / manual — not attempted)
Cell-proliferation (ATP-luminescence, 72 h), transwell migration/invasion assays, cytotoxicity assays, qRT-PCR validation of 6 genes. These generate the input to P1/P2 but are not computational reproductions.
Reproducibility assessment of the primary pipeline (P1)
SixModelIC50 V3.r is the authors' own published tool (P16: a cited tool applied
to the paper's data is a fully valid reproduction target). Its documented output
columns are identical to Supplementary Table S1 — i.e. S1 is the tool's
DrugData.txt. Reproduction has two stages:
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The
nlscurve fit (raw growth% → model params): NOT reproducible. The raw dose-response measurements (the script'sinputFiles/*.txt) were never deposited — not in the GitHub repo (ships only the.rfile), not in any of the 4 supplements (S1=IC50 output, S2=selected IC50s, S3/S4=GSEA). Without the input we cannot redo the fit. → recorded gap (docs_insufficientfor this step). -
The deterministic derivation (fit params → log IC50/IC90/IC95, and model-selection → headline IC50 → sensitive/resistant class): reproducible exactly from the numbers S1/S2 do report, using the authors' own formulas. This is what we reproduce on «our HPC» (job
repro-pmid-37810000).
Decision (80/20)
- Reproduce P1's deterministic derivation (clean, exact, auditable): re-derive S1's IC50/IC90/IC95 columns for the 6 cell lines' 3-Parameter-Bottom-0 rows from the reported log(EC50)+Amax with the authors' exact R formula; reproduce the S2 model-selection (lowest Std.Err) → 6 headline IC50s → resistant(>2µM)/sensitive grouping. Confirm the published tool executes (env build + run on synthetic input).
- Do NOT chase the last 20%: the
nlsfit (no input data) and the full P2 transcriptomics re-analysis (E-MTAB-783 limma/GSEA — exact cell-line grouping & thresholds underspecified; 15-DEG list only in a figure) are not attempted as numeric 1:1. Rationale recorded here and in AUDIT.md.
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.
The deterministic half of the paper's only own-code pipeline (IC50/IC90/IC95 derivation, lowest-Std.Err model selection, and 2 µM resistant/sensitive grouping) reproduces 1:1 to floating-point identity (max abs diff 7.55e-15) using the authors' own published SixModelIC50 V3.r formula — integrity is clean and there is no fabrication signal. The genuine limitation is authors'-side / data-availability: the raw dose-response input the nls fit consumes was never deposited, so the curve fit itself (full S1), the s-vs-r t-test (p=0.0032108), and the out-of-scope E-MTAB-783 transcriptomics branch could not be independently recomputed. The central conclusion (cytarabine-resistance grouping) holds fully; the deviation on every checkable value is negligible rounding, so overall this is a solid reproduction whose only gaps are explainable deposition shortfalls, plus a cosmetic 'Kasumi-9'/'Kasumi-1' label typo.
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Reproduction footprint
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