PI3K inhibitors protect against glucocorticoid-induced skin atrophy.
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.
- ✓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
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡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 -> 1:1 reproduced within tolerance. The paper ships no analysis repo (the registry 'code' link github.com/slowkow/ggrepel is only a ggplot2 label library); per P16 we reproduced the described pipeline (Bioconductor limma::neqc, Methods 2.11) on the paper's own public microarray data GSE120991 (Illumina HumanHT-12 v4, 12 arrays). The headline claim 'We identified 706 differentially expressed genes affected by FA' (FA vs DMSO, Fig 3a) reproduced as 704 DEG (384 up / 320 down) vs reported 706 (374/332) -- all within ~3%. Key clarifications for the human auditor: (a) the paper labels the threshold '(P<.01, FDR<0.1)' but FDR<0.1 alone gives only 84 genes; the count 706 is a raw-P<0.01 count, so the threshold label is internally inconsistent (loose reporting, NOT fabrication -- the value is fully derivable). (b) the no-batch model on the 8 DMSO+FA arrays (matching the paper's stated methods, which mention no batch correction) is the design that reproduces the number; batch-term / 12-array variants do not. (c) NORMALIZATION DEVIATION: paper's neqc used real Illumina control probes, but the GEO non-normalized supplementary ships only the 47315 regular probes + detection p-values, so we used neqc's documented control-free path (neg-control distribution inferred from detection p) -- the most likely source of the small differences. NOT ATTEMPTED (out of scope / 80-20): wet-lab phenotyping, IHC, qPCR; Ingenuity pathway figures; exact gene membership of Suppl. Tables S2/S3/S5; symbol-level spot-check of named GR genes (non-normalized file has only ILMN probe IDs, needs illuminaHumanv4.db).
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
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 76assessed: 2026-06-14 ⛓ 9cd6175a1920
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
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
- 2026-06-14
- 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: opusBecause REDD1 and FKBP51 (negative regulators of mTOR/Akt signaling) are central drivers of glucocorticoid-induced skin atrophy, the authors hypothesized that dual REDD1/FKBP51 inhibitors could protect skin against the catabolic/atrophic side effects of glucocorticoids while preserving anti-inflammatory activity.
- ★ PI3K/mTOR/Akt inhibitors are a pharmacological class that represses glucocorticoid-induced REDD1 and FKBP51 expression, identified via LINCS drug-repurposing screen. finding
- ★ Selected PI3K/mTOR/Akt inhibitors (WM, LY294002, AZD8055, NVP-BEZ235, MK-2206) block glucocorticoid-induced REDD1/FKBP51 expression in human keratinocytes and mouse skin. finding
- ★ PI3K/mTOR/Akt inhibitors shift the global glucocorticoid receptor transcriptional response toward therapeutically important transrepression. mechanism
- ★ PI3K/mTOR/Akt inhibitors reduce GR Ser211 phosphorylation, GR nuclear translocation, and GR loading onto REDD1/FKBP51 promoters, and inhibit NF-κB. mechanism
- ★ Topical LY294002 combined with fluocinolone acetonide protects mice against FA-induced proliferative block and skin atrophy without altering FA anti-inflammatory activity. finding
- A bioinformatics LINCS screening approach can repurpose existing drugs as REDD1/FKBP51 repressors. method
- Combining glucocorticoids with PI3K/mTOR/Akt inhibitors improves the therapeutic index of glucocorticoids for inflammatory skin disease. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| In silico transcriptional signature screen (LINCS drug repurposing) | human cell transcriptome data across 50 cell types | drug treatment (>20,000 compounds) | REDD1/FKBP51 ranking among down-regulated DEGs | LINCS library / custom DNA arrays; R v3.2.5 |
| RT-PCR/Q-PCR gene expression | HaCaT and NHEK human keratinocytes | FA +/- PI3K/mTOR/Akt inhibitors (WM, LY294002, AZD8055, NVP, MK-2206) | REDD1/FKBP51 mRNA fold change (normalized to RPL27) | Roche LightCycler 480; SsoAdvanced SYBR Green |
| Luciferase reporter assay | HaCaT reporter cells (GRE-Luc, NF-κB-Luc, mCMV-Luc) | FA (1 μM) +/- WM/LY294002/AZD8055 | reporter luciferase activity normalized to total protein | TD-20/20 luminometer |
| Western blot (nuclear/cytosolic fractionation) | HaCaT keratinocytes | FA +/- inhibitors | GR, phospho-GR(Ser211), phospho-Akt, phospho-rpS6, NF-κB/p65, IκB protein levels | LI-COR Odyssey imager |
| Microarray gene expression | HaCaT keratinocytes | FA (1 μM) +/- LY294002 (50 μM) | genome-wide differentially expressed genes (FDR<0.1) | Illumina HumanHT-12 BeadChip (GSE120991) |
| Chromatin immunoprecipitation (ChIP) + Q-PCR | HaCaT keratinocytes | FA (1 μM) +/- WM (10 μM) or LY294002 (50 μM) | GR fold-enrichment at REDD1/FKBP51 promoter GREs | EMD Millipore EZ-Magna ChIP A/G kit |
| Immunofluorescence | HaCaT keratinocytes on glass slides | FA +/- inhibitors | GR subcellular (nuclear) localization | Zeiss Axioplan2 microscope / AxioCam HRC |
| In vivo skin atrophy / ear edema / BrdU proliferation | F1 C57BL/6 × 129 female mice skin and ears | topical FA (1 μg) +/- LY294002 (10 nmoles); croton oil for edema | epidermal/adipose width, dermal cell number, BrdU+ proliferative index, ear swelling weight | — |
- ▼ PI3K/mTOR/Akt inhibitors (WM, LY294002, AZD8055, NVP, MK-2206) blocked FA-induced REDD1 and FKBP51 expression in HaCaT and NHEK keratinocytes
- – LINCS screen identified PI3K/mTOR/Akt inhibitors as the most prominent pharmacological class of REDD1/FKBP51 repressors
- – Inhibitors shifted GR-driven transcriptome away from transactivation toward transrepression
- ▼ Inhibitors reduced GR phosphorylation, nuclear translocation, and GR loading on REDD1/FKBP51 promoters
- ▼ Topical LY294002 + FA protected mice against FA-induced proliferative block and skin atrophy
- – LY294002 did not alter the anti-inflammatory (ear edema) activity of FA
- count >20,000 unique compounds (LINCS library compounds screened across 50 cell types)
- count ~1 million experiments (scale of LINCS library experiments)
- count ~20,000 transcriptional signatures (LINCS database of drug-induced signatures screened)
- other FDR < 0.1 (threshold for differentially expressed genes in microarray)
- pvalue P < .05 (statistical significance threshold (two-tailed Student's t-test))
- count 4 animals/group; 40 images/treatment group (mouse morphometric analysis sampling)
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 combined a bioinformatics drug-repurposing screen of the LINCS transcriptional database with experimental validation in keratinocytes and mice. For most bench experiments, results were summarized as mean and standard deviation and groups were compared with unpaired two-tailed Student's t-tests (described as non-parametric) using GraphPad Prism, with P<.05 considered significant; experiments were run at least in duplicate. Microarray data were analyzed with the Limma package (neqc normalization, FDR<0.1 for differential expression), and downstream enrichment used Fisher's exact tests with Benjamini-Hochberg adjustment plus GSEA.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| unpaired two-tailed Student's t-test (described in text as non-parametric) | general comparisons between treatment groups across cell and mouse experiments (e.g. REDD1/FKBP51 expression, reporter assays, morphometry) | experiments conducted at least in duplicate; mouse groups n=4; morphometry 4 samples/40 images per group | not stated |
| Limma moderated statistics (linear models for microarray) | identification of differentially expressed genes from HumanHT-12 BeadChip array (FA ± LY294002) | experiment repeated twice | not stated |
| Fisher's exact test | overlap between DEGs and gene sets for functional annotation/pathway analysis | — | na |
| Pearson linear correlation | comparison of microarray vs Q-PCR gene expression values | — | not stated |
| GSEA enrichment (GO molecular function, Hallmark gene sets) | gene set enrichment of DEGs | — | na |
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Comparisons were summarized with mean and standard deviation.↳ Could also: Reporting a 95% confidence interval or showing individual data points alongside the mean would also convey spread and estimate precision. — Confidence intervals and dot plots add information about estimation uncertainty and the underlying distribution, which is often emphasized for small sample sizes.
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Multiple treatment groups were compared using pairwise unpaired two-tailed t-tests.↳ Could also: A one-way (or two-way) ANOVA followed by a post-hoc test such as Tukey HSD or Dunnett's could also be used when several groups are compared. — An ANOVA-based framework with a post-hoc correction simultaneously models all groups and controls the family-wise error rate across the set of comparisons.
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The text labels the test as a non-parametric unpaired two-tailed Student's t-test.↳ Could also: A clearly designated rank-based test such as the Mann-Whitney U (Wilcoxon rank-sum) test could also be used when a non-parametric comparison is intended. — Specifying either the parametric t-test or the rank-based Mann-Whitney U precisely communicates the distributional assumptions being made for each comparison.
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Sample sizes were stated descriptively (e.g., at least duplicate, 4 mice per group) without a formal power calculation.↳ Could also: An a priori power analysis or report of the effect size targeted could also accompany the chosen n. — A power/effect-size statement helps readers gauge the study's sensitivity to detect a given difference and aids replication planning.
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Microarray differential expression used Limma with an FDR<0.1 threshold.↳ Could also: Reporting effect-size estimates (e.g., log fold-change with confidence bounds) alongside the FDR, or a stricter FDR cutoff, could also be presented. — Pairing significance thresholds with effect-size magnitudes helps distinguish statistically detectable from biologically substantial changes.
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Counts/proportions such as the BrdU proliferative index were compared with t-tests.↳ Could also: Generalized linear models for proportions (e.g., logistic or binomial regression) or mixed-effects models accounting for multiple fields per animal could also be applied. — Models that respect the count/proportion structure and the nesting of fields within animals can account for within-animal correlation and the bounded nature of proportions.
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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Topical LY294002 co-treatment reduces FA-induced skin atrophy and restores epidermal proliferative index (BrdU+) in mice.imaging mouse skin down 2019×1papers★ This paper is the founder (earliest)
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PI3K inhibition shifts the NR3C1-driven transcriptome away from glucocorticoid transactivation toward transrepression in HaCaT keratinocytes.microarray human hacat-keratinocyte mixed 2019×1papers★ This paper is the founder (earliest)
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In silico LINCS drug-repurposing screen identifies PI3K/mTOR/Akt inhibitors as the top pharmacological class of DDIT4 (REDD1) and FKBP5 (FKBP51) repressors across human cell transcriptomes.other human cell transcriptome 2019×1papers★ This paper is the founder (earliest)
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PI3K inhibitors reduce NR3C1 (GR) phosphorylation at Ser211, nuclear translocation, and chromatin occupancy at DDIT4 and FKBP5 promoter GREs in HaCaT keratinocytes.other human hacat-keratinocyte down 2019×1papers★ This paper is the founder (earliest)
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LY294002 does not alter the anti-inflammatory ear edema suppression activity of FA in mice, leaving glucocorticoid transrepression intact.other mouse ear none 2019×1papers★ This paper is the founder (earliest)
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PI3K/mTOR/Akt inhibitors suppress fluocinolone acetonide-induced DDIT4 (REDD1) and FKBP5 (FKBP51) mRNA expression in HaCaT and NHEK human keratinocytes.qPCR human hacat-keratinocyte down 2019×1papers★ 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.
No assessed neighbours yet — the network grows as more papers are assessed.
Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-30737086
Paper: Agarwal S, Mirzoeva S, Readhead B, Dudley JT, Budunova I. PI3K inhibitors protect against glucocorticoid-induced skin atrophy. EBioMedicine 2019. PMID 30737086 · PMCID PMC6441871 · DOI 10.1016/j.ebiom.2019.01.055
The "code" link is a plotting library, not a pipeline
The registry code_url = https://github.com/slowkow/ggrepel. The paper cites it only
for figure labelling: "R packages Ggplot2 and Ggrepel (http://github.com/slowkow/ggrepel)
were used for visualization." There is no authors' analysis repository. Per BRIEF
rule P16, this is fine: we reproduce by running the described third-party pipeline
(Bioconductor limma + neqc) on the paper's own public data.
The dataset
GEO GSE120991 — "Genome-wide analysis of LY294002 effect on the glucocorticoid receptor function in human keratinocytes." Illumina HumanHT-12 v4.0 BeadChip (GPL10558), microarray (NOT RNA-seq). 12 samples (47,315 probes), HaCaT keratinocytes, two experimental batches:
| Group | n | GSMs |
|---|---|---|
| DMSO (solvent control) | 4 | GSM3423470, 74, 78, 80 |
| FA (fluocinolone acetonide, 1 µM) | 4 | GSM3423471, 75, 79, 81 |
| LY294002 (PI3K inh, 50 µM) | 2 | GSM3423472, 76 (batch B1 only) |
| FA + LY294002 | 2 | GSM3423473, 77 (batch B1 only) |
Supplementary file used: GSE120991_non-normalized_data.txt.gz (2.1 MB) — required
because neqc needs the raw probe intensities (+ detection p-values / control probes).
Methods as described (Methods §2.11)
"Microarray processing was performed using the Limma package. The neqc function was used for background subtraction, and quantile normalization with both positive and negative control probes." Threshold: P < 0.01 and FDR < 0.1.
IN SCOPE (pipeline-derived, attempted)
- C1 — DEG count. "We identified 706 differentially expressed genes (DEG, P value <.01, FDR <0.1) affected by FA" → contrast FA vs DMSO.
- C2 — up-regulated. "374 genes were up-regulated".
- C3 — down-regulated. "332 down-regulated". (Results §, Fig 3a volcano plot; Suppl. Tables S2/S3/S5.)
- C4 (qualitative) — named GR-target genes among DEGs (FKBP5/FKBP51, DDIT4/REDD1, PLIN2, GLUL, HSD11B2, SGK1, BIRC3, SCNN1G, BEST2, PNLIPRP3) appear up-regulated by FA.
Pipeline for C1–C4: read non-normalized Illumina data → limma::neqc (normexp bg
correction + quantile normalization with control probes) → lmFit/eBayes on
FA vs DMSO → count probes with raw P.Value < 0.01 & adj.P.Val < 0.1, split by sign.
OUT OF SCOPE (not attempted, why)
- Wet-lab: mouse skin-atrophy phenotyping, IHC, keratinocyte proliferation, GR reporter assays, qPCR validation — bench work, no pipeline.
- IPA / pathway-enrichment figures — depend on a commercial tool (Ingenuity) and on the DEG list; not the primary quantitative claim. Skipped (80/20).
- Exact gene membership of Suppl. Tables — we check the DEG counts + direction and spot-check named genes, not full table identity (last-20%).
Decision
Single «our HPC» job, env repro-geo-limma (limma+GEOquery+statmod). Download the GEO
supplementary inside the compute job (front1 has no internet), run on «infra», return
only small count outputs to «host».
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 the described limma neqc pipeline on the paper's own public data (GSE120991): the headline 706 DEG reproduces as 704 (384 up / 320 down) vs reported 706 (374/332) — all within 0.3-3.6%. The small differences trace to a forced control-free neqc normalization (the GEO non-normalized file omits Illumina control probes). A benign reporting issue worth flagging: the paper's threshold label '(P<.01, FDR<0.1)' is internally inconsistent — FDR<0.1 alone yields only 84 genes, so 706 is the raw-P<0.01 count (loose reporting, value fully derivable, not fabrication). The registry code link (ggrepel) is a harvest false-positive. A solid within-tolerance reproduction.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
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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.