Integrative analysis reveals chemokines CCL2 and CXCL5 mediated shear stress-induced aortic dissection formation.
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 relevant deviation in data/preprocessing
- ✓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
- ✓Overall, the reproduction was clean
- Every checked point held up.
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 headline DEG pipeline 1:1. The paper (Xue et al., Heliyon 2023) is a secondary re-analysis of public GEO data; the brief's 'code' link (immunogenomics/harmony) is a third-party tool used only for the scRNA step, so per P16 we reproduced the described pipeline (limma) on the paper's own data. PRIMARY TARGET = GSE153434 TAAD-vs-normal DEGs (limma, adj.P<0.05 & |log2FC|>=1; reported 1272 = 232 up / 1040 down). Fetched the GEO raw count matrix (GSE153434_all.counts.txt.gz, sha256 2961fab3...) on «infra» and ran limma on «our HPC» (R 4.3.3, limma 3.58.1) across a grid of the unstated preprocessing choices. EXACT 1:1 match on all three numbers (1272/232/1040) with plain limma on log2(raw count + 1) and NO normalization -- i.e. the paper applied a microarray-style limma uniformly to its 3 datasets (two of which are microarray) rather than an RNA-seq-aware voom/TMM workflow. The voom/TMM variants reproduce the right total order-of-magnitude (1315-1498) but a balanced up/down split, so the reported directional skew uniquely identifies the un-normalized-log2 method. NO fabrication signal: every reported number is exactly derivable from the shipped GEO data with the named tool; method caveat (un-normalized counts) is statistical, not integrity. NOT attempted (80/20 + out-of-scope): the two microarray DEG sets (R2 GSE147026, R3 GSE52093) and shear-stress DEGs (R5 GSE160611) -- one clear exact data point sufficed; the common-DEG Venn (R4); the scRNA harmony/Seurat clustering (R6, unpinned resolution/PC/QC params); CIBERSORTx (R7, interactive web tool); and STRING/Cytoscape/MCODE (R8, GUI).
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 75assessed: 2026-06-15 ⛓ 34b88f213d32
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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: opusBecause aortic dissection occurs only at specific aortic segments that share the same genetic background, the authors hypothesize that shear stress-induced molecular alterations—particularly endothelial cell apoptosis and CCL2/CXCL5 chemokine release driving an Endothelial-Monocyte-Neutrophil axis—are key to the localized development of aortic dissection.
- ★ Chemokines CCL2 and CXCL5 mediate a shear stress-induced 'Endothelial-Monocyte-Neutrophil' axis that contributes to aortic dissection development. mechanism
- ★ Integrative analysis of three AD datasets identified 150 common DEGs (100 down-regulated, 50 up-regulated), with MYC, CCL2, and SPP1 as highest-degree hub genes. finding
- ★ Five shear stress-associated hub DEGs in AD were identified (ANGPTL4, SNAI2, CCL2, GADD45B, PROM1), enriched for the endothelial cell apoptotic process. finding
- ★ CCL2 and CXCL5 protein expression is increased in clinical AD aortic tissue samples; CCL2 has multiple cellular origins and CXCL5 is macrophage-derived. finding
- ★ Connectivity Map analysis identified MEK inhibitors and ALK inhibitors as candidate therapeutic agents reversing AD gene expression signatures. resource
- A 15-gene MCODE cluster combines up-regulated inflammation genes (CCL2, SPP1) and down-regulated muscle genes (ACTC1, CALD1, MYL9, MYOCD), with CCL2 central to immune-related genes. finding
- Integrative bioinformatic pipeline combining bulk RNA-seq, shear-stress HAEC transcriptomics, and single-cell RNA-seq to identify shear stress-associated AD hub genes. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq / gene expression microarray (public dataset reanalysis, DEG analysis) | human ascending aortic tissue (AD patients vs normal/control) | none (disease vs control comparison) | differentially expressed genes (adjusted P<0.05, |log FC|≥1) | GEO datasets GSE153434, GSE147026, GSE52093; R limma/edgeR |
| bulk transcriptome sequencing (shear stress, DEG analysis) | human aortic endothelial cells (HAEC) | pulsatile shear stress vs steady state (1 h, 4 h, 24 h) | differentially expressed genes; chemokine ligand/receptor expression over time | GEO dataset GSE160611 |
| single-cell RNA sequencing (scRNA-seq) clustering/cell-type annotation | human AD aortic tissue | none | cell-type origin of CCL2 and CXCL5 expression (UMAP clustering) | GEO dataset GSE213740; Seurat v3.1.2, Harmony |
| Western blot / immunoblotting | clinical human aortic dissection tissue samples | none (AD vs control) | CCL2 and CXCL5 protein expression (normalized to β-actin) | CCL2 (Wanleibio WL02966), CXCL5 (Affinity DF9919), β-actin (Wanleibio WL01372); Quantity One software |
| multiplex immunofluorescence staining | clinical human aortic dissection tissue sections | none | co-localization of MPO, CD68, and CXCL5 | Servicebio/Abcam antibodies (MPO GB11224, CD68 GB113150, CXCL5 Ab305100); 3DHISTECH Panoramic MIDI scanner |
| immune cell deconvolution (CIBERSORTx) | human AD bulk expression data | none | predicted proportions of 22 immune cell types | CIBERSORTx with LM22 reference |
| protein-protein interaction network and module analysis | AD and HAEC shear-stress DEGs (in silico) | none | hub gene degree, MCODE clustering modules | STRING, Cytoscape v3.6.1, MCODE |
| drug repurposing prediction (Connectivity Map) | 150 common AD DEGs (in silico) | none | normalized connectivity scores of candidate small molecules | CMap online platform |
- ▲ Immunoblotting confirmed increased CCL2 and CXCL5 protein expression in clinical AD tissue samples vs controls
- – 100 down-regulated and 50 up-regulated common AD DEGs identified across the three datasets
- – Five shear stress-associated hub DEGs identified in AD (4 up: ANGPTL4, SNAI2, CCL2, GADD45B; 1 down: PROM1), enriched for endothelial cell apoptosis
- – 366 shear stress-associated DEGs identified in HAEC (281 up-regulated, 85 down-regulated)
- – CCL2 was a high-degree hub gene in both AD and shear-stress HAEC DEG networks; CCL2, CXCR4, and ATF3 had highest degrees in HAEC network
- – CCL2 expression increased in HAEC at 1 h of shear stress, then decreased at 4 h (below basal) and declined further at 24 h, reflecting endothelial adaptation
- – scRNA-seq revealed CCL2 has multiple cellular origins while CXCL5 is macrophage-derived
- – MEK inhibitors and ALK inhibitors identified as negative (reversing) candidate therapeutic agents for AD
- count 1272 DEGs (232 up, 1040 down) (GSE153434 AD vs normal DEGs)
- count 1173 DEGs (753 up, 420 down) (GSE147026 DEGs)
- count 2738 DEGs (1385 up, 1253 down) (GSE52093 DEGs)
- count 366 DEGs (281 up, 85 down) (shear stress-induced HAEC DEGs (GSE160611))
- count 100 down-regulated and 50 up-regulated common AD DEGs (intersection of three AD datasets)
- count 15 nodes and 42 edges, cluster score 6 (MCODE crucial clustering module)
- other 48 h mortality ~50% without surgery; mortality increases 1%–2% per hour (AD clinical background)
- count 22 immune cell types; LM22 reference (CIBERSORTx deconvolution)
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 applied limma (and edgeR per the Results section for one dataset) to three public bulk RNA-seq AD datasets and one shear-stress HAEC dataset to call DEGs at adjusted P < 0.05 and |log2FC| ≥ 1, then used Venn diagram intersection to define common DEGs across datasets. Functional enrichment was performed via Metascape and clusterProfiler; PPI hub genes were identified via STRING/Cytoscape/MCODE; CIBERSORTx was used for immune deconvolution; and Seurat was used for scRNA-seq clustering. Clinical validation of CCL2 and CXCL5 protein expression used immunoblotting and multiplex immunofluorescence without reported formal inferential statistics or stated validation sample sizes.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| limma linear model with empirical Bayes moderation; edgeR also mentioned for GSE153434 in the Results section | DEG identification in three AD datasets (GSE153434, GSE147026, GSE52093) and shear-stress HAEC dataset (GSE160611); threshold adjusted P < 0.05 and |log2FC| ≥ 1 | GSE153434: n=20 (10 control, 10 AD); GSE147026: n=8 (4 control, 4 AD); GSE52093: n=12 (5 control, 7 AD); GSE160611: not stated | not stated |
| GO and KEGG pathway overrepresentation analysis (hypergeometric/Fisher's exact basis; P < 0.05 and q < 1) | Functional enrichment of 150 common AD DEGs and 5 shear-stress hub DEGs via Metascape and clusterProfiler | 150 common AD DEGs (100 down-regulated, 50 up-regulated); 5 shear-stress hub DEGs | not stated |
| MCODE graph-clustering algorithm (density × member count cluster score) | Detection of crucial clustering modules in PPI network of 150 common AD DEGs | 150 common AD DEGs | na |
| CIBERSORTx support-vector-regression deconvolution with LM22 reference | Estimation of 22 immune cell type proportions from normalized AD bulk expression data | null | not stated |
| Seurat FindCluster (Louvain/SNN algorithm, resolution = 0.5) and FindAllMarkers (Wilcoxon rank-sum test, Seurat default) | scRNA-seq cell clustering and marker gene identification in GSE213740 integrated with harmony batch correction | null | not stated |
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Common AD DEGs were defined by requiring a gene to pass thresholds independently in all three datasets, operationalized via Venn diagram intersection↳ Could also: Robust rank aggregation (RRA) or a random-effects meta-analysis across the three datasets could also synthesize multi-dataset differential expression evidence — RRA and meta-analytic methods weight statistical evidence and effect sizes across datasets rather than requiring threshold passage in every dataset, which can recover genes with consistent but modest signals that fall below threshold in one dataset and provides a formal statistical framework for cross-dataset synthesis
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GO/KEGG enrichment significance was assessed with a q-value threshold of q < 1, which is mathematically non-restrictive (all terms pass)↳ Could also: A conventional FDR-adjusted q < 0.05 or q < 0.20 threshold, or gene-set enrichment analysis (GSEA) run on the full ranked gene list, could also be applied — GSEA uses continuous ranking rather than a binary membership cutoff, increasing power especially for small gene sets; a standard FDR threshold provides a defined false-discovery control for the enrichment result family
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Immunoblotting results for CCL2 and CXCL5 in clinical tissues were presented as representative images without a reported formal inferential test, effect size, or stated sample size↳ Could also: A two-sample t-test or Mann-Whitney U test applied to densitometry-quantified band intensities, with reported n, p-value, and fold change, could also accompany the blot images — Reporting inferential statistics and sample sizes for validation experiments allows readers to assess the precision and reproducibility of the experimental confirmation independently of the bioinformatic discovery
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Immune cell infiltration was estimated exclusively by CIBERSORTx deconvolution from bulk RNA-seq↳ Could also: Complementary deconvolution tools such as TIMER2.0, EPIC, or MCP-counter could also estimate immune cell proportions from the same bulk data — Different deconvolution algorithms use different reference signatures and statistical assumptions; comparing results across methods can assess the robustness of immune cell proportion estimates to methodological choice
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PPI hub genes were ranked by degree centrality (number of interaction partners) in the STRING/Cytoscape network↳ Could also: Betweenness centrality, eigenvector centrality, or weighted gene co-expression network analysis (WGCNA) could also identify influential network nodes — Degree centrality favors highly connected nodes broadly; betweenness centrality identifies bottleneck regulators that bridge network modules; WGCNA captures co-expression modules that may reflect distinct biological processes not apparent from interaction-database topology alone
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scRNA-seq data from multiple samples were integrated using harmony before Louvain clustering at a single fixed resolution (0.5)↳ Could also: Seurat RPCA integration or scVI (deep generative model-based) integration could also harmonize multi-sample scRNA-seq data; clustering stability across a range of resolutions could also be evaluated — Benchmarking studies show integration method performance varies by dataset size and composition; evaluating clustering across a resolution range (e.g., 0.3–0.8) and selecting by silhouette score or clustree analysis can support the biological interpretability of the chosen partition
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.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-38163105
Paper: Xue C et al. (2023) Integrative analysis reveals chemokines CCL2 and CXCL5 mediated shear stress-induced aortic dissection formation. Heliyon 9(12):e23312. PMID 38163105 / PMC10757018 / DOI 10.1016/j.heliyon.2023.e23312.
The paper is a pure secondary bioinformatic re-analysis of public GEO datasets (no new sequencing). The "code" link in the brief is the third-party harmony tool (used only for the scRNA-seq integration step). Per brief P16, reproducing the described pipeline on the paper's own data is equally valid.
Pipeline-derived results (candidate in-scope)
| # | Result | Pipeline | Data | In scope? |
|---|---|---|---|---|
| R1 | DEGs of TAAD vs normal aorta: 1272 (232 up / 1040 down) | limma, adj.P<0.05 & |logFC|≥1 |
GSE153434 (10 TAAD vs 10 normal, bulk RNA-seq counts) | YES — primary |
| R2 | DEGs GSE147026: 1173 (753 up / 420 down) | limma, same cutoff | GSE147026 (4 vs 4) | secondary (microarray) |
| R3 | DEGs GSE52093: 2738 (1385 up / 1253 down) | limma, same cutoff | GSE52093 (7 vs 5) | secondary (microarray) |
| R4 | Common AD DEGs: 150 (100 down / 50 up) | intersection of R1–R3 | — | depends on R1–R3 |
| R5 | Shear-stress DEGs GSE160611: 366 (281 up / 85 down) | limma | GSE160611 | secondary |
| R6 | 8 cell clusters; CCL2/CXCL5 cell-of-origin | Seurat + harmony | GSE213740 (6 scRNA samples) | hard-20% (unpinned params) |
| R7 | CIBERSORTx 22 immune cells; ↑neutrophils | CIBERSORTx LM22 | AD datasets | out (web tool, unpinned) |
| R8 | PPI/MCODE hub genes MYC/CCL2/SPP1; cluster 15n/42e | STRING+Cytoscape+MCODE | — | out (manual/web GUI) |
Decision (80/20)
- Primary target: R1 — the single most clearly-specified, fully-pinned
pipeline output (named tool
limma, exact cutoff, single named dataset with a raw count matrix on GEO, exact reported counts). This is the room's primary dataset (GSE153434). - Opportunistic: R2/R3/R5 if cheap (same limma cutoff, but these are microarray series → preprocessing differs; lower priority).
- NOT attempted (hard 20%, stated honestly):
- R6 scRNA/harmony — Seurat resolution/PC/QC params not reported → cluster count not pinnable; cell-of-origin is a manual marker call.
- R7 CIBERSORTx — interactive web tool, no pinned run config.
- R8 STRING/Cytoscape/MCODE — GUI/manual, no script.
- R4 common-DEG intersection — only meaningful if R1–R3 all reproduce; the intersection is sensitive to each series' preprocessing.
Key reproducibility caveat (recorded up front)
The paper says only "limma … adjusted P<0.05 & |logFC|≥1" on RNA-seq counts
(GSE153434). It does not state the count→expression transform (voom vs
limma-trend), the low-count filter, or the normalization. The DEG count is
sensitive to those unstated choices, so an EXACT 1272/232/1040 match is not
expected; we run the standard limma-voom pipeline plus a small grid of the
plausible unstated variants and report the closest, honestly graded.
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 primary endpoint — GSE153434 TAAD-vs-normal DEGs by limma (Fig.1) — reproduces exactly 1:1 (1272 total / 232 up / 1040 down) from the only deposited GEO count matrix using the paper's named tool at its stated cutoff, with the unique ≈4.5:1 down-skew confirming the precise pipeline. The single issue is on the authors' methodology disclosure: 'limma' was applied to un-normalized log2 raw counts (microarray-style) without stating the transform, which is statistically suboptimal for RNA-seq but is precisely what regenerates their figures. There is no fabrication signal — the exact triple match is strong positive evidence of genuine values. Downstream mechanistic claims (scRNA clusters R6, CIBERSORTx R7, MCODE R8) and the secondary microarray DEG sets were not attempted (80/20 / out-of-scope), so judgement is confined to the reproduced foundational claim, which is solid.
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.