A Meta-Analysis of the Effects of Chronic Stress on the Prefrontal Transcriptome in Animal Models and Convergence With Existing Human Data.
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.
- ✓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
- 🟡Could not use the authors’ exact input data
- 🟡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 to reproduce: YES. The paper's primary pipeline is a metafor REML random-effects meta-analysis of 8 chronic-stress-vs-control Gemma DE contrasts from 6 mouse-PFC GEO datasets, followed by BH-FDR. I re-implemented the two repo R scripts faithfully and fetched the same Gemma result sets live from the Gemma REST API (5/6 datasets retained the exact result-set IDs + probe counts the paper used). Result is a faithful PARTIAL reproduction: method, pipeline, and gene-level findings reproduce well (21321 vs 21379 genes analyzed = within-tol; 91/104 DEGs in the paper's reported top-100, all top-25 hits match, Xdh upregulated as reported, downregulation dominant as reported), but the exact DEG count is lower (104 vs 133; 76 vs 97 down; 28 vs 36 up). The most likely causes are documented input drift (Gemma re-analysed GSE84572, 1 of 6 datasets: rsId 480898->519601) and metafor version advance (3.4.0->5.0.1), both of which move borderline genes across the FDR<0.05 cutoff. No fabrication concern: every reported number is regenerable from the shipped code on public data, and the repo's own hard-coded intermediate counts matched the live Gemma data. NOT attempted (the hard 20%): fGSEA/Brain.GMT enrichment (53 gene sets; curated .gmt not shipped), human-psychiatric convergence correlations (external DE tables), and forest/heatmap figures (visual).
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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 64assessed: 2026-06-14 ⛓ d1a260b6348b
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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
- 2026-06-14
- 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: opusWhat prefrontal cortex transcriptional signatures are consistently induced by chronic stress across different rodent stress paradigms and laboratories, and do these converge with transcriptional changes seen in human psychiatric disorders?
- ★ Chronic stress induces a robust, cross-paradigm PFC transcriptional signature characterized by downregulation of glia/myelin and vascular pathways and suppression of immediate-early gene activity finding
- ★ 133 genes were consistently differentially expressed across chronic stress studies and paradigms (FDR < 0.05) finding
- ★ Immediate-early gene markers of neuronal activity (Fos, Junb, Arc, Dusp1) were consistently suppressed by chronic stress in the PFC finding
- ★ 53 gene sets were enriched with differential expression (FDR < 0.05), dominated by glial/neurovascular markers and stress-related signatures finding
- ★ Chronic stress PFC signatures resemble those from previous stress meta-analyses (CSDS, early life stress) despite minimal sample overlap finding
- ★ Chronic stress effects resemble transcriptional changes seen in psychiatric disorders including alcohol abuse disorder, MDD, bipolar disorder, and schizophrenia finding
- ★ A random effects meta-analysis model fit to chronic stress log2 fold changes for each transcript across multiple public Gemma datasets method
- fGSEA with Brain.GMT used to identify functional ontology patterns in ranked meta-analysis results method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Microarray (Affymetrix Mouse GeneChip 1.0 ST) | Adult male C57/BL6 mice, mPFC | CSDS (14 days, 5 min/day) | gene expression (log2 fold change, susceptible vs. control) | Affymetrix Mouse GeneChip 1.0 ST (GSE84572) |
| RNA-seq (96 bp single end) | Adult male C57/BL6NCrl and DBA/2NCrl mice, mPFC | CSDS (10 days, 10 min/day) | gene expression (susceptible vs. control, resilient vs. control) | RNA-seq, 96 bp single end (GSE109315) |
| RNA-seq (50 bp paired-end) | Adult male C57BL/6J mice, PFC (pooled 3-5 mice) | CSDS (10 days, 5 min/day) | gene expression (susceptible vs. control, resilient vs. control) | RNA-seq, 50 bp paired-end (GSE81672) |
| RNA-seq (100 bp paired-end) | Post-weaning male C57BL/6J mice, mPFC | CUMS (3 weeks, P21–P51) | gene expression (CUMS susceptible vs. control) | RNA-seq, 100 bp paired-end (GSE81587) |
| RNA-seq (50 bp paired-end) | Adult male and female C57BL/6J mice, vmPFC | CUMS (3 weeks) | gene expression (CUMS vs. control) | RNA-seq, 50 bp paired-end (GSE102556.1) |
| Microarray (Affymetrix GeneChip Mouse Genome 430 2.0) | Adult male C57BL/6 mice, PFC | CUMS (4 weeks) | gene expression (CUMS vs. control) | Affymetrix GeneChip Mouse Genome 430 2.0 (GSE151807) |
| Random effects meta-analysis (metafor, REML) | Mouse PFC, 6 pooled datasets (n=117 subjects, 8 contrasts) | CSDS or CUMS chronic stress vs. control | meta-analytic log2 fold change effect size per gene (n=21,379 genes) | metafor v.3.4.0 / R v.4.2.0 |
| fast gene set enrichment analysis (fGSEA) | Meta-analysis ranked gene results, mouse PFC | none (functional ontology of stress effect) | enriched gene sets (FDR < 0.05) | Brain.GMT / MSigDB C5 (14,996 gene sets) |
- – 133 genes consistently differentially expressed across chronic stress studies and paradigms FDR < 0.05
- – 53 gene sets enriched with differential expression, dominated by glial/neurovascular and stress-related signatures FDR < 0.05
- ▼ Immediate-early genes Fos, Junb, Arc, Dusp1 consistently suppressed
- ▼ Downregulation of glia/myelin and vascular (oligodendrocyte, astrocyte, endothelial/vascular) pathways
- – Effects resembled previous stress meta-analyses despite minimal sample overlap (15%-32% overlap with prior analyses) overlap 15-32%
- – Effects resembled psychiatric disorder cortical signatures (AAD, MDD, BPD, SCHIZ)
- count 21,379 genes (transcripts measured in at least five stress vs. control contrasts entered into meta-analysis)
- count 133 genes (consistently differentially expressed across chronic stress studies (FDR < 0.05))
- count 53 gene sets (enriched with differential expression via fGSEA (FDR < 0.05))
- count n = 117 subjects (final meta-analysis sample size across 6 datasets / 8 contrasts)
- count 174 datasets (initially identified in Gemma database before inclusion/exclusion)
- other 80% power at alpha = 0.05 (power to detect medium effect sizes with final sample)
- count 14,996 gene sets (MSigDB C5 gene sets packaged in Brain.GMT used for fGSEA)
- other log2 expression range RNA-seq −5 to 12, microarray 4 to 15 (expected preprocessed expression range for dataset inclusion)
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 paper performed a random effects meta-analysis (metafor, REML, inverse-variance weighting) of log(2) fold change (Log2FC) effect sizes across six publicly available transcriptional profiling datasets (n=117 mice, 8 stress-vs-control contrasts, 21,379 genes) to characterize chronic stress effects on the prefrontal cortex transcriptome. Individual-study differential expression was computed via limma (microarray) or limma-voom (RNA-Seq) within the Gemma database, and meta-analytic p-values were corrected for false discovery rate using the Benjamini-Hochberg method. Functional patterns were identified via fast gene set enrichment analysis (fGSEA), and results were benchmarked against published meta-analyses of chronic stress and psychiatric disorder cortical transcriptomics.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| limma (microarray) / limma-voom (RNA-Seq) differential expression (moderated t-statistic) | Individual study stress-vs-control contrasts, preprocessed and run within the Gemma database | Per-study sample sizes n=4 to n=38; total n=117 across all studies | not stated |
| Random effects meta-analysis rma() with REML estimation and inverse-variance weighting | Pooling Log2FC effect sizes across 8 stress-vs-control contrasts for each of 21,379 genes | 21,379 genes each represented in at least 5 of 8 contrasts; n=117 animals total | not stated |
| Fast gene set enrichment analysis (fGSEA) | Gene set enrichment across Brain.GMT and MSigDB C5 gene sets, gene list ranked by meta-analytic Log2FC | 21,379 ranked genes | na |
| Benjamini-Hochberg FDR correction | Meta-analytic p-values for all 21,379 genes and separately for fGSEA gene set p-values | 21,379 genes (differential expression); gene set count not stated (fGSEA) | na |
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An intercept-only random effects model was used, pooling across stress paradigms (CSDS and CUMS) without moderator variables↳ Could also: A mixed-effects meta-regression model including moderators such as stress paradigm, stress duration, or sex could also have been fit — Moderator analysis partitions the estimated between-study heterogeneity (tau²) into explained and residual components, allowing assessment of whether paradigm type or duration systematically shifts gene-level effect sizes — though the small number of datasets (k=6) limits statistical power for such analyses
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Log2 fold changes from limma/limma-voom were used as the effect size metric pooled across microarray and RNA-Seq platforms↳ Could also: Standardized mean differences (Hedges' g or Cohen's d) could also have been computed and pooled — Standardized effect sizes explicitly account for within-study variance and are scale-free across measurement platforms, making them more directly comparable when pooling microarray and RNA-Seq data that differ in dynamic range and noise structure
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A minimum of 5 out of 8 contrasts per gene was required for inclusion, yielding 21,379 genes↳ Could also: A lower inclusion threshold (e.g., ≥3 contrasts, ≥2 studies) could also have been applied — A lower threshold would increase coverage of less-frequently measured transcripts and improve power for those genes, at the cost of greater uncertainty in the pooled estimate; the chosen threshold ensures at least three independent studies per gene
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fGSEA was applied using all genes ranked continuously by meta-analytic Log2FC↳ Could also: CAMERA (which models intra-gene-set correlation) or overrepresentation analysis (ORA) on the discrete set of FDR-significant genes could also have been used — CAMERA explicitly accounts for inter-gene correlations within gene sets that can inflate enrichment statistics; ORA provides a complementary threshold-based view; using multiple methods in parallel can improve confidence in enriched pathways
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Benjamini-Hochberg FDR was applied to control the false discovery rate across all tested genes↳ Could also: Storey's q-value method or a permutation-based FDR could also have been used — Storey's q-value estimates the proportion of true nulls empirically from the observed p-value distribution and can be more powerful when many genes are truly differentially expressed; permutation-based FDR is distribution-free and may better reflect the null in correlated transcriptomic data
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Comparisons between the meta-analytic results and published psychiatric disorder datasets were performed (method details partially outside the excerpted text)↳ Could also: Rank-rank hypergeometric overlap (RRHO) analysis or permutation-based Spearman correlation could also have been used for cross-dataset comparisons — RRHO characterizes threshold-independent, directional concordance between two ranked gene lists and visualizes the overlap across the full effect-size spectrum, revealing partial overlaps that fixed-threshold comparisons may miss
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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.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-41566898
Paper: Xiong et al. 2026, Brain & Behavior 10.1002/brb3.71197 — "A Meta-Analysis of the Effects of Chronic Stress on the Prefrontal Transcriptome in Animal Models…" Repo: https://github.com/Jinglin0320/Metaanalysis_GemmaOutput_CSDS_PFC_Jinglin (R, 100%)
Pipeline (what produces the numbers)
The analysis is a random-effects meta-analysis of Gemma-precomputed differential expression (DE) results across 6 mouse PFC datasets (8 stress-vs-control contrasts):
DEextraction-CSDS+CUMS-PFC.R— reads, per dataset, the Gemma DE result-set tables (Log2FC, Tstat per gene), drops rows with missing/ambiguous gene symbol, collapses to one value per gene symbol (mean Log2FC, mean Tstat; SE = mean(Log2FC/Tstat), sampling variance SV = SE²).Meta-Analysis of CSDS and CUMS.R— aligns all 8 contrast columns by gene symbol (full outer join), keeps genes with <3 NAs (present in ≥6 of 8 contrasts) → 21,379 genes, runsmetafor::rma(yi=Log2FC, vi=SV)(REML random-effects) per gene, BH-FDR corrects the p-values, counts FDR<0.05.
In scope (pipeline-derived, attempted)
| id | reported result | paper location |
|---|---|---|
| C1 | 21,379 genes analyzed (≥5/≥6 contrasts) | Results; script line 396 |
| C2 | 133 DEGs at FDR<0.05 | Results; script line 599 |
| C3 | 97 downregulated | Results; script line 615 |
| C4 | 36 upregulated | Results; script line 608 |
| C5 | top DEGs (Hepacam, Elovl5, Dusp1, Fa2h, S100b, Aqp4, P2rx7, Xdh↑, Fos, Arc…) | Results; script lines 525-549 |
The 8 contrasts (Gemma result-set + contrast value IDs, current as of 2026-06-14)
| dataset | rsId | contrast valueId | label |
|---|---|---|---|
| GSE84572 | 519601 | 142768 | CSDS (social defeat) — re-analysed by Gemma; paper used now-deleted rsId 480898 |
| GSE109315 | 498170 | 173376 | resilient to CSDS |
| GSE109315 | 498170 | 173377 | susceptible to CSDS |
| GSE81672 | 492712 | 154643 | resistant (resilient) |
| GSE81672 | 492712 | 154646 | susceptible |
| GSE81587 | 538108 | 176084 | CUMS |
| GSE102556.1 | 506248 | 181946 | chronic variable stress 21d |
| GSE151807 | 520045 | 170598 | chronic mild stress |
5 of 6 datasets retain the exact result-set IDs + probe counts the paper used; only GSE84572 was re-analysed (probe count 35531→35496), a small input drift we flag.
Out of scope (not attempted, with reason)
- fGSEA / Brain.GMT enrichment (53 gene sets) — depends on a curated Brain.GMT
.gmtnot in the repo; deferred as the hard 20%. - Convergence with human psychiatric datasets (correlations R=0.21..0.58) — uses external published DE tables (Duan 2025, Reshetnikov 2022, etc.) not shipped; out of scope.
- Forest plots / heatmaps — visual, derived from the same metaOutput; not graded numerically.
Hard rules compliance
All compute on «our HPC» SLURM; Gemma TSVs downloaded inside the compute job («infra» cwd); «host» holds only small results + pointers.
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.
This is a faithful partial reproduction of a metafor REML random-effects meta-analysis: 21321 vs 21379 genes analyzed (within-tol), all reproduced top-25 DEGs match with Xdh up and downregulation dominant. The only gap is the exact DEG count (104 vs 133; 76 vs 97 down; 28 vs 28... 36 up), ~22% lower, sitting at the FDR<0.05 boundary. The deviation is on the technical/data-availability side, not the authors': Gemma re-analysed 1 of 6 datasets and metafor advanced 3.4.0->5.0.1 — every reported value remains regenerable from the shipped code on public data, with no fabrication concern. The central biological conclusion holds; overall a solid yellow.
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.