Gene module regulation in dilated cardiomyopathy and the role of Na/K-ATPase.
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.
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
- 🟡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
Described well enough to attempt, P16 third-party-data reproduction: paper applies stock DESeq2/WGCNA to public MAGNet data (GSE141910). Data identity 1:1 - the MAGNet sample sheet reproduces every Table-1 cohort count EXACTLY (confirms genuine input). The headline DESeq2 result did NOT reproduce 1:1: same data, exact same 166/166 cohort, exact stated threshold (|log2FC|>=1 & p<0.01), DESeq2 1.50.2 default results() gave 2181 DEGs (raw p) / 2157 (padj) vs reported 1469 - same direction (1564 up/617 down) and order of magnitude, ~48% overshoot. Best explanation is method under-specification, NOT fabrication: Methods omit LFC-shrinkage and any low-count prefilter and the DESeq2/R version; default MLE log2FC inflates |LFC| for low-count genes, pushing extra genes past the cut. NOT attempted (80/20 out-of-scope): WGCNA modules (non-deterministic colors, no pinnable number), GO/Enrichr, KEGG/WebGestalt-GSEA, ChEA3, Cytoscape/STRING - external web tools or figure-only. Verdict: partial - data and pipeline-direction reproduce, exact DEG count does not due to undocumented DESeq2 options.
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 55assessed: 2026-06-15 ⛓ 2f8b659e3683
✎ 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
- 2026-06-15
- 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 study investigates the relationship between gene expression (co-expressed gene modules and pathways) and cardiac function in dilated cardiomyopathy (DCM), whether genetic regulation of DCM differs between African Americans and Caucasians, and whether Na/K-ATPase gene expression correlates with cardiac function (LVEF).
- ★ Several co-expressed gene modules are significantly associated with left ventricle ejection fraction (LVEF) and the DCM phenotype, enriched in fibrosis-related, small molecule transporting-related, and immune response-related pathways. finding
- ★ Gene modules associated with LVEF in African Americans are almost identical to those in Caucasians, suggesting more common than disparate genetic regulation in DCM etiology between the two groups. finding
- ★ Na/K-ATPase gene expression level has a strong correlation with LVEF, supporting the clinical significance of Na/K-ATPase regulation in DCM. finding
- WGCNA combined with pathway and consensus analysis can be used to define DCM-related gene modules and compare regulation across racial groups. method
- Extracellular matrix-related, immune, and receptor-ligand activity pathways are overrepresented/upregulated in DCM patients versus donors while some metabolic pathways are downregulated (non-significant). finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-sequencing (FPKM/count data, secondary analysis) | human heart left ventricle tissue (DCM patients and non-failing donors, GSE141910/MAGNet cohort) | none (disease vs donor comparison) | gene expression levels (FPKM, counts) | — |
| differential expression analysis (DESeq2) | human LV tissue, DCM patients (n=166) vs donors (n=166) | none (DCM vs donor) | log2FoldChange and p-values; differentially expressed genes | DESeq2 R package / RStudio |
| Gene Ontology (GO) enrichment analysis | DEGs from DCM vs donor comparison | none | overrepresented GO biological process, molecular function, cellular component pathways | Enrichr / Appyter |
| KEGG pathway / GSEA analysis | whole gene set log2FoldChange data (DCM vs donor) | none | coordinately up/down-regulated pathways with FDR | WebGestalt (webgestalt.org) |
| Weighted gene co-expression network analysis (WGCNA) | human LV RNA-seq (GSE141910, 332 samples after outlier removal) | none | co-expressed gene modules and module-trait correlation coefficients/p-values | WGCNA R package |
| Consensus network analysis | African American (AA) vs Caucasian (CA) subgroups of LV RNA-seq | none | consensus gene dendrogram, module correlation, and module preservation between groups | WGCNA R package |
| Transcription factor enrichment analysis | magenta gene module gene list (healthy donors vs patients) | none | prioritized TFs regulating Na/K-ATPase gene expression | ChEA3 online platform |
| Protein-protein interaction network / pathway mapping | top module genes per phenotype; Na/K-ATPase/Src wikipathway | none | PPI network map and Na/K-ATPase-related pathway overlay | Cytoscape v3.8.1 / STRING / WikiPathway WP5051 |
- – 1469 genes significantly differentially expressed in DCM patients versus donors 1469 genes (log2FC>=1 or <=-1, p<0.01)
- – 29 co-expressed gene modules detected; several significantly associated with both DCM phenotype and LVEF (opposite directions) 29 modules
- – Greenyellow and black gene modules significantly related to both LVEF and Na/K-ATPase alpha 1 gene expression
- – Gene modules associated with LVEF in African Americans almost identical to Caucasians (consensus analysis)
- – Type I diabetes mellitus, immune system, and cell adhesion molecule pathways significantly upregulated; metabolic pathways downregulated but not significant in DCM vs donors downregulated FDR>0.5 (non-significant)
- ▲ ECM-related genes overrepresented in DCM patients (GO Biological Process and Cellular Component)
- ▲ Risk of developing DCM in black people ~3-fold compared to whites (background literature) 3-fold
- count 166 DCM patients and 166 donors used for DESeq2 analysis (DCM vs donor comparison cohort)
- count 366 total LV samples; 166 DCM, 166 donors, 28 HCM, 6 PPCM (full GSE141910 cohort composition)
- count 124 African Americans, 242 Caucasian Americans (racial composition of cohort)
- count 1469 DEGs significantly changed in DCM vs donors (DEG threshold log2FC>=|1|, p<0.01)
- mean LVEF 0.56±0.12 (donors) vs 0.18±0.10 (patients) (left ventricle ejection fraction by group)
- count 29 co-expressed gene modules detected (WGCNA module detection)
- mean Age 55.9±14 (donors) vs 51.1±11.3 (patients) (patient/donor age)
- other soft threshold power 10, minimum module size 30, merge cut height 0.25 (WGCNA network construction parameters)
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 re-analyzed publicly available RNA-seq data (GSE141910; n=166 DCM patients, n=166 non-failing donors) using DESeq2 for differential gene expression and WebGestalt-based GSEA for KEGG pathway enrichment. Weighted gene co-expression network analysis (WGCNA) was applied to detect gene modules and correlate their eigengenes with continuous clinical phenotypes including LVEF; a consensus WGCNA was then performed to compare module structure between African American and Caucasian subgroups. Results were reported as log2 fold-changes on volcano plots, module-trait Pearson correlations in a heatmap, and FDR values for GSEA pathways.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| DESeq2 negative-binomial Wald test | Differential expression: DCM patients vs non-failing donors (volcano plot, Fig 1); DEG threshold log2FC ≥1 or ≤−1 and p≤0.01 | n=166 DCM patients vs n=166 donors | not stated |
| WGCNA Pearson correlation (moduleTraitCor / moduleTraitPvalue functions) | Module-trait relationship heatmap (Fig 4); each module eigengene correlated with continuous clinical phenotypes and Na/K-ATPase expression levels | n=329 (332 DCM+donor samples minus 3 outliers removed by sample clustering) | not stated |
| GSEA (gene set enrichment analysis) with FDR correction | KEGG pathway analysis using full-gene log2FoldChange dataset uploaded to WebGestalt (Fig 3); FDR<0.05 used as significance threshold | n=166 DCM patients vs n=166 donors | not stated |
| Overlap-based enrichment test (via Enrichr) | Gene ontology (GO) analysis of 1469 DEGs across biological process, molecular function, and cellular component (Fig 2) | 1469 DEGs as stated | not stated |
| ChEA3 overlap-based transcription factor enrichment ranking | TF enrichment analysis of genes in the magenta WGCNA module; TFs compared between donors and patients | — | not stated |
| WGCNA consensus module preservation analysis | Comparison of gene module topology and preservation between African American and Caucasian subgroups | AA subgroup n=121 (77 DCM + 44 donors); CA subgroup n=211 (89 DCM + 122 donors) as derivable from Table 1 | not stated |
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DEGs were defined using a fixed p-value threshold of ≤0.01, without explicitly stating whether this represents raw or BH-adjusted p-values from DESeq2↳ Could also: Explicitly threshold on BH-adjusted p-values (padj) from DESeq2, or apply a standard FDR threshold (e.g., FDR<0.05 or FDR<0.10) across all tested genes — With ~20,000 genes tested simultaneously, explicit and transparent FDR control is a widely expected convention in RNA-seq reporting; making this choice explicit helps readers understand the expected false-discovery rate among the 1469 reported DEGs
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Module-trait correlations were reported with individual p-values across a matrix of 29 modules by multiple phenotypes, without a stated correction for multiple comparisons↳ Could also: Apply Benjamini-Hochberg FDR correction across the full module-phenotype correlation matrix — Testing many module-trait pairs simultaneously increases the probability of spurious correlations; FDR correction across the matrix is a standard approach that would make the significance claims more interpretable
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WGCNA module eigengene–trait correlations used Pearson correlation (the default in the WGCNA package)↳ Could also: Spearman rank correlation, which does not assume bivariate normality and is less sensitive to outliers in eigengene or trait distributions — Clinical variables such as LVEF can be skewed or contain extreme values; Spearman correlation is a standard nonparametric alternative that relaxes distributional assumptions while remaining interpretable as a monotonic association measure
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Comparison between African American and Caucasian subgroups relied on WGCNA consensus module preservation analysis to assess network-level similarity↳ Could also: Include a race × diagnosis interaction term in a DESeq2 or limma linear model to formally test differential gene expression between racial groups at the individual gene level — Consensus WGCNA characterizes topological similarity of co-expression networks but does not provide a formal statistical test for race-by-disease interaction at the gene level; an interaction model would complement network-level findings with gene-level inference
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The WGCNA co-expression analysis was performed on FPKM values, while differential expression analysis used raw count data processed by DESeq2↳ Could also: Use variance-stabilizing transformation (VST) or regularized log (rlog) counts from DESeq2 for WGCNA, applying a single normalization framework throughout — FPKM does not account for between-sample library size differences in the same way as VST/rlog; the WGCNA authors recommend using a count-based normalized transformation to stabilize variance across the expression dynamic range before network construction
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Descriptive statistics in Table 1 are reported as mean ± values (consistent with SD) for continuous clinical variables↳ Could also: Report median and interquartile range (IQR) for variables with potentially skewed distributions, or add 95% confidence intervals around group means — Variables such as LVEF (donor mean 0.56 vs patient mean 0.18) may be skewed within groups; median/IQR is often more informative for skewed clinical data, and 95% CIs convey the precision of the group estimate alongside its central tendency
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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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.
Downstream reach in the literature
56 downstream papers · 1 datasetsHow widely the datasets deposited by this paper are reused across the whole literature (Europe PMC), beyond our assessed set. This is a factual dependency map — reusing a public dataset is normal, good science. It is not a judgement on the downstream papers; the only verdict here is this paper's own, with its cited rationale.
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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-35901050
Paper: Gao Y, et al. Gene module regulation in dilated cardiomyopathy and the role of Na/K-ATPase. PLoS One 2022. PMID 35901050 / PMC9333241 / doi:10.1371/journal.pone.0272117
Data: GEO GSE141910 (MAGNet consortium). Count matrix + sample sheet were obtained by the authors from GitHub mpmorley/MAGNet (their stated source). This is a P16 / third-party-data reproduction: the paper applies standard off-the-shelf tools (DESeq2, WGCNA, Enrichr, WebGestalt, ChEA3, Cytoscape) to a public consortium dataset. There is no authors' analysis repo; we reproduce by re-running the described tool (DESeq2) on the same public data with the stated parameters — per the brief, equally valid.
Pipeline-derived results (in scope)
| id | result | pipeline | reproducible? |
|---|---|---|---|
| C1 | 1469 DEGs in DCM (n=166) vs donors (n=166), threshold |log2FC|≥1 & p<0.01 | DESeq2 on GSE141910 raw counts | YES — primary target |
| C2 | Cohort composition: 166 DCM, 166 NF, 28 HCM, 6 PPCM; 172 F; 242 CA / 124 AA; AA_DCM 77 / AA_NF 44 / CA_DCM 89 / CA_NF 122 (Table 1, Results) | count of sample sheet | YES — metadata sanity check (already confirmed exact, control-plane) |
In scope but lower priority (the hard ~20%)
| id | result | why deferred |
|---|---|---|
| C3 | WGCNA: soft power 10, min module size 30, merge 0.25; removed 3 outliers at tree cutoff 220; module–trait correlations (Fig 4–5) | reproducible in principle, but module colors/labels are not deterministic across WGCNA/blockwiseModules versions; the paper reports no single pinnable number (heatmap of correlations). High effort, low pin-ability. |
Out of scope (not a pipeline / not pinnable / external web tools)
- GO (Enrichr/Appyter), KEGG/GSEA (WebGestalt), ChEA3 TF enrichment, Cytoscape/STRING network figures — performed on external web platforms with no reported exact numeric output to compare; figures only.
- All wet-lab / prior-study Na/K-ATPase biology claims.
- Consensus AA-vs-CA WGCNA preservation (Fig 6) — qualitative.
Primary reproduction plan
Re-run DESeq2 on the MAGNet Counts.csv (the authors' stated count source),
subset to the 332 DCM+NF samples via phenoData.csv, design = ~etiology,
contrast DCM vs NF, default DESeq2 normalization. Count DEGs at the paper's
threshold (|log2FC|≥1 & raw p<0.01 — the paper writes "p value<0.01" and used
EnhancedVolcano, whose default y-axis is raw p). Report padj-based count too for
transparency. Compare to reported 1469.
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.
Data identity is solid — the MAGNet phenoData.csv reproduces every Table-1 cohort count exactly (q1/q2 green). The single pinnable claim, 1469 DEGs, did not reproduce 1:1: same 166/166 cohort and exact |log2FC|>=1 & p<0.01 threshold yielded 2181 (raw p) / 2157 (padj), a ~48% overshoot with the same direction (1564 up / 617 down). The discrepancy sits on the authors' side as method under-specification (omitted LFC-shrinkage, low-count prefilter, and DESeq2 version), not fabrication — the value is plausibly derivable under a specific undocumented config. The paper's actual core (WGCNA modules, Na/K-ATPase) was out of 80/20 scope, so the central conclusion is only partly confirmed; overall a solid reproduction with an explainable, moderate deviation.
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.