Genome-wide DNA hypermethylation opposes healing in patients with chronic wounds by impairing epithelial-mesenchymal transition.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
The main results reproduced, with only marginal, non-material deviations.
- ✓Reported values were directly comparable
- 🟡Could not use the authors’ exact input data
- 🟡A deviation arose in the data or preprocessing
- 🔴A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The deviation was non-trivial in magnitude
- 🟡The central claim did not (fully) hold under reproduction
- 🟡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
Code repo bundschuhlab/PublicationScripts/WoundEdgeHypermethylation is a small, self-contained, DETERMINISTIC downstream comparison (DMR x DEG overlap) that ships its own input TSVs AND expected outputs. Reproduced EXACTLY: sorting.py output byte-identical (md5 match); comparing.py yields the same 4 concordant + 22 discordant matched genes (set-identical; only gene ORDER differs due to py2-vs-py3 dict iteration) and identical bar counts. The 4+22 = 26 matched sig DMR/DEG genes coincide with the paper's '26 EMT DMRs'. HOWEVER, the paper's HEADLINE upstream counts do NOT reproduce from the shipped data: shipped dmrs.tsv (14,785 regions) yields only 359 sig DMRs (vs reported 4,689 / 3,661 hyper / 1,028 hypo) and degs.tsv yields 1,281 sig DEGs (vs reported 614) — the GitHub example TSVs are an inconsistent/partial snapshot relative to the published figures, and the upstream pipeline scripts (PrEMeR-CG MethylCap DMR calling, DESeq2) are NOT in the repo. Full upstream reproduction needs raw GSE176413/GSE176414 + the niche PrEMeR-CG tool on «our HPC» — ATTEMPTED but blocked this pass («our HPC»/VPN tunnel down, repaired centrally). NOT attempted: IPA EMT/TP53 (proprietary, out of scope), scRNA/Visium (separate SinghLabICRME repo). Brief's data accession GSE137897 is WRONG — paper deposits under GSE176417. Grades provisional, human-checkable.
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 68assessed: 2026-06-19 ⛓ dde9076c6a73
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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
- 2026-06-19
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no 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: opusDoes genome-wide DNA hypermethylation in the chronic wound-edge tissue silence TP53 and EMT-pathway genes to impair keratinocyte epithelial-mesenchymal transition and wound closure, and can reversal of such methylation improve healing?
- ★ Chronic wound-edge tissue exhibits genome-wide DNA hypermethylation, with hypermethylation more prominent than hypomethylation across differentially methylated regions. finding
- ★ Hypermethylation silences the TP53 signaling/EMT pathway, with ADAM17, NOTCH, TWIST1, and SMURF1 sharing hyper-methylated/downregulated directionality. mechanism
- ★ Methylation-dependent gene silencing in chronic wound edge is confined to the keratinocyte cell compartment (specifically the KRT14+ Kera1 cluster). finding
- ★ Tissue ischemia induces wound-edge DNMT expression, DNA hypermethylation, and TP53 methylation, and blunts EMT. finding
- ★ 5'-azacytidine, an inhibitor of methylation, improves wound closure in murine wounds. finding
- ★ Targeted TP53 promoter methylation via a tissue nanotransfection-based CRISPR/dCas9-DNMT3A approach silences TP53 and impairs keratinocyte migration; reversal of methylation is a productive therapeutic strategy. method
- ★ Chronic wound edge shows selective depletion of the KRT19+/KRT7+ simple-epithelium Kera2 keratinocyte subpopulation, not seen in acute wounds. finding
- SAM-induced DNA hypermethylation blunts keratinocyte migration in a DNMT-dependent, acetylation-independent manner. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Whole-genome DNA methylome (DMR analysis) | Human chronic wound-edge tissue vs unwounded human skin | none | Differentially methylated regions in proximal promoters | — |
| Bisulfite sequencing | Human chronic wound-edge tissue; human keratinocytes | none / SAM / dCas9-DNMT3A targeting | CpG/TP53 promoter methylation percentage | — |
| Bulk total RNA-Seq (differential expression) | Human chronic wound-edge vs unwounded skin | none | Differentially expressed genes | — |
| Single-cell RNA-Seq (scRNA-Seq) | 7 human samples (3 chronic WE, 4 unwounded skin) | none | Cell-type clusters and per-cluster gene expression | — |
| Spatial transcriptomics | Unwounded human skin | none | Spatial localization of Kera1/Kera2 markers and metabolic genes | 10x Visium |
| qRT-PCR | Human chronic wound-edge tissue; keratinocytes | none / SAM | mRNA expression of candidate genes | — |
| IHC / immunostaining (incl. EMT marker colocalization) | Human chronic WE tissue; murine ischemic flap/wound model | ischemia / none | Protein expression and E-cadherin/mesenchymal marker colocalization | — |
| Western blot; keratinocyte migration assay | Human keratinocytes | SAM; dCas9-DNMT3A TP53 targeting; DNMT/HDAC inhibitors | TP53/ADAM17/NOTCH1 protein, 5mC, H3 acetylation; cell migration | — |
- – 4,689 differentially methylated regions identified in chronic wound-edge vs unwounded skin 4,689 DMRs
- ▲ Hypermethylation (3,661 DMRs) more prominent than hypomethylation (1,028 DMRs) 3,661 vs 1,028 DMRs
- ▲ 26 hypermethylated DMRs represented EMT pathway, the most significant enriched pathway 26 DMRs
- ▼ TP53 hypermethylation associated with TP53 downregulation; ADAM17, NOTCH1, TWIST1, SMURF1 downregulated in chronic WE
- ▼ 614 DEGs met filtering criteria out of 57,825 annotated genes; TP53 signaling enriched among downregulated genes 614 DEGs
- ▼ Selective loss of Kera2 subpopulation in chronic WE tissue χ2=187.98
- ▼ TP53+ Kera1 cells reduced from 5.9% (UW) to 2.5% (chronic WE) 5.9% to 2.5%
- ▲ dCas9-DNMT3A TP53 targeting produced 63.6% CpG methylation vs 18.2% control, ~60% decrease in TP53 protein, and inhibited migration 63.6% vs 18.2%; ~60% protein decrease
- count 4,689 DMRs (Total differentially methylated regions in chronic WE vs UW)
- count 3,661 hypermethylated / 1,028 hypomethylated DMRs (DMR directionality in chronic WE)
- count 614 DEGs (P<0.05; log2FC>±1) (RNA-Seq DEGs from 57,825 annotated genes)
- pvalue P<0.00001; χ2 with Yates correction = 187.98 (Loss of Kera2 subpopulation in chronic WE)
- count 67,040 cells from 7 samples; 25,168 chronic WE and 25,561 UW after QC (scRNA-Seq dataset size)
- other 63.6% vs 18.2% CpG methylation (dCas9-DNMT3A TP53 targeting vs inactive control)
- fold_change ~60% decrease in TP53 protein (Western blot after dCas9-DNMT3A targeting)
- count Kera1: 457 upregulated / 421 downregulated genes vs Kera2; 158 up / 144 down in chronic WE Kera1 (Keratinocyte cluster DEG counts)
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.
The study combined genome-wide DNA methylome profiling (with 4,689 differentially methylated regions identified between chronic wound-edge and unwounded skin), bulk RNA-Seq differential expression analysis, single-cell RNA-Seq clustering/compositional analysis, and targeted validation (bisulfite sequencing, qRT-PCR, IHC, Western blot) in human tissue, a murine ischemic flap model, and cultured keratinocytes. Differential expression and methylation results were filtered using stated P-value and fold-change thresholds, and pathway-level significance was assessed with Ingenuity Pathway Analysis (IPA) and the Reactome database. A chi-square test with Yates correction and a Wilcoxon rank-sum test were used for specific single-cell compositional and cluster comparisons, respectively. A dedicated statistics/methods section describing test selection, replicate structure, and error reporting conventions was not present in the provided excerpt.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Chi-square test with Yates correction | compositional comparison of Kera2 keratinocyte subpopulation proportion between chronic wound-edge and unwounded skin (Figure 3A, Supplemental Figure 3C) | — | not stated |
| Wilcoxon rank-sum test | comparison of gene expression between Kera1 and Kera2 keratinocyte clusters from scRNA-Seq (Figure 3, B and C) | — | not stated |
| Differential expression analysis (statistical test/model not named) | bulk RNA-Seq comparison of chronic wound-edge vs unwounded skin (Figure 2B) | 614 DEGs from 57,825 annotated genes, filtered at P < 0.05 and log2 fold change > ±1 | not stated |
| Pathway/upstream-regulator enrichment analysis (Ingenuity Pathway Analysis, IPA) | canonical pathway and upstream regulator analysis of hypermethylated DMRs and downregulated DEGs (Figure 1E; Supplemental Figure 1H; Figure 2C) | — | not stated |
| Reactome pathway enrichment analysis | pathway enrichment of Kera1 DEGs (chronic WE) and keratinocyte DEGs in acute wound comparison (Figure 4E; Supplemental Figure 8E) | acute wound comparison filtered at adjusted P < 0.05 and log2 fold change ± 0.5 | not stated |
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Bulk RNA-Seq DEGs were selected using an unadjusted P < 0.05 threshold combined with a log2 fold-change cutoff.↳ Could also: Apply a multiple-testing correction such as Benjamini-Hochberg FDR (as used by tools like DESeq2 or edgeR) to the genome-wide differential expression comparison. — When testing thousands of genes simultaneously, an FDR-adjusted threshold controls the expected proportion of false positives among called DEGs, which unadjusted P-values do not guarantee.
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Keratinocyte subpopulation composition was compared between chronic wound-edge and unwounded skin using a chi-square test with Yates correction on pooled cell counts.↳ Could also: Use a compositional single-cell analysis framework (e.g., scCODA) or a beta-binomial/Dirichlet-multinomial model that treats each patient sample as the unit of replication. — Modeling per-sample proportions rather than pooling all cells across samples can account for biological variability between donors and avoid treating individual cells as independent observations.
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Differential expression between the two keratinocyte clusters (Kera1 vs Kera2) in scRNA-Seq data was assessed with a Wilcoxon rank-sum test on individual cells.↳ Could also: Aggregate counts to a pseudobulk level per sample and apply DESeq2/edgeR, or use a mixed-model single-cell DE method (e.g., MAST) that accounts for sample-of-origin. — Cells from the same donor are not fully independent; pseudobulk or mixed-effects approaches can reduce the risk of pseudoreplication inflating apparent significance.
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Pathway-level significance for DMRs and DEGs was assessed using IPA and Reactome enrichment analyses based on hard significance cutoffs (e.g., P < 0.05, fold-change thresholds) to define input gene lists.↳ Could also: Use rank-based gene set enrichment analysis (GSEA), which evaluates the full ranked list of genes rather than a thresholded subset. — Rank-based enrichment can detect coordinated but individually subthreshold expression changes across a pathway that a hard-cutoff approach might miss.
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Quantitative outcomes such as migration assays, qRT-PCR, and IHC quantification are referenced without a stated dispersion measure (SD/SEM/CI) or named statistical test in this excerpt.↳ Could also: Report effect sizes with 95% confidence intervals alongside p-values for these comparisons. — Confidence intervals convey both the magnitude and the precision of an effect, complementing significance testing, and are particularly informative for smaller experimental groups.
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Comparisons of chronic wound-edge tissue against unwounded skin (and ischemic vs non-ischemic flap) were treated as independent group comparisons.↳ Could also: Where samples are matched within the same patient or animal (e.g., wound-edge and unwounded skin from the same individual), a paired test or a mixed-effects model with subject as a random effect could also be used. — Paired or mixed-effects approaches can account for inter-individual variability and increase statistical power when within-subject matching is available.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — PMID 35819852 (Genome-wide DNA hypermethylation opposes healing in chronic wounds via impaired EMT)
- PMID 35819852 · PMCID PMC9433101 · DOI 10.1172/jci157279 · J Clin Invest 2022
- Code https://github.com/bundschuhlab/PublicationScripts → subfolder
WoundEdgeHypermethylation - Data (paper's Data-Availability stmt) GEO GSE176417 (SuperSeries; sub-series GSE176413 MethylCap-Seq, GSE176414 RNA-Seq, GSE176415 scRNA-seq, GSE176416 Visium).
⚠️ Brief vs paper accession mismatch
The room BRIEF lists geo:GSE137897 as the data. That is not this paper's data.
GSE137897 = "Transcriptional programming of human epidermis during wound repair and in
pressure ulcer at single-cell resolution" (an unrelated scRNA-seq study). The paper's
own Data Availability statement deposits everything under GSE176417. Profiling targets
GSE176417 (and notes GSE137897 as a brief error).
Pipeline-derived results and scope
| # | Reported result | Pipeline | In scope? | Reproducible from shipped repo? |
|---|---|---|---|---|
| R1 | DEG×DMR overlap: 4 concordant + 22 discordant genes; bar counts | comparing.py on shipped dmrs.tsv,degs.tsv |
YES | YES — shipped example data + expected outputs |
| R2 | DEG baseMean>10 filter + sort | sorting.py on shipped degs.tsv |
YES | YES — byte-identical expected output shipped |
| R3 | 4,689 sig DMRs (3,661 hyper / 1,028 hypo), FDR<0.05 | PrEMeR-CG / mean-vector test on MethylCap-Seq (GSE176413) | YES (upstream) | NO from shipped data — shipped dmrs.tsv yields 359 sig (336/23). Upstream pipeline scripts NOT in repo; needs raw MethylCap-Seq + PrEMeR-CG (niche OSU tool) |
| R4 | 614 DEGs | DESeq2 on RNA-Seq (GSE176414) | YES (upstream) | NO from shipped data — shipped degs.tsv yields 1,281 sig at padj≤0.05 (1,181 with baseMean>10). DESeq2 script NOT in repo |
| R5 | 26 hypermethylated EMT DMRs; TP53 top upstream regulator; ADAM17/NOTCH/TWIST1/SMURF1 | Ingenuity Pathway Analysis (IPA) | NO — proprietary IPA, manual curation | out of scope (wet-lab/commercial tool) |
| R6 | scRNA-seq / Visium analyses | Seurat etc. (separate SinghLabICRME repo) | partial | not attempted here (different repo; scope-limited to bundschuhlab repo) |
| R7 | MethylCap-Seq capture, bisulfite validation, wet-lab | — | NO | out of scope (wet-lab) |
Reproduction strategy
- R1/R2 (DONE, deterministic, exact): the repo ships its own input TSVs AND the
expected outputs. Ran
sorting.py+comparing.py(faithful py3 ports; only print-statement syntax changed) → outputs match the shipped*_expectedfiles. - R3/R4 (harder, attempted on «our HPC»): the repo ships only the final DMR/DEG TSVs, not the pipeline that produced them. The shipped TSVs do not regenerate the paper's 4,689/614 headline counts (see AUDIT). Full reproduction needs raw GSE176413/ GSE176414 + PrEMeR-CG + DESeq2 — heavy compute on «our HPC»; feasibility limited by the missing upstream scripts and the niche PrEMeR-CG tool.
- R5–R7: out of scope (IPA / wet-lab / separate repo).
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.
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.