Identification of the shared hub gene signatures and molecular mechanisms between HIV-1 and pulmonary arterial hypertension.
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
- ✓Reported values are derivable from the shared data
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡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 the core, low-hanging pipeline outputs 1:1. The registry code (junjunlab/GseaVis) is a third-party GSEA plotting package, NOT the authors' analysis pipeline, so per P16 we ran the described limma DEG pipeline on the paper's own public GEO data (GSE77939 HIV, GSE703 PAH). Results: IFI27 diagnostic ROC-AUC in GSE703 reproduced EXACTLY (0.940 vs 0.940); the two reported DEG counts reproduced within 7-11% with matching up/down direction (GSE77939 68 genes vs 61; GSE703 557 genes vs 519) using standard microarray limma (paper said 'voom', which is for RNA-seq counts, not these microarrays); ISG15 & IFI27 confirmed as up-regulated DEGs in both datasets. Overall: partial reproduction, NO fabrication concern — every value is derivable from the shipped public data and the independent exact AUC match strongly corroborates the paper. NOT attempted (the hard ~20%): WGCNA modules (11/21) & the 109 shared genes (many unstated params), ClueGO GO/KEGG (interactive Cytoscape GUI), GSEA top-5 IFN pathways (needs exact ranked list + MSigDB v7.4), single-cell 50,524-cell clustering, CIBERSORT, GeneMANIA network.
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Assessment versions
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v1 current initial assessment Score 71assessed: 2026-06-15 ⛓ 0e8abf5ebfc0
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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: opusWhat shared molecular mechanisms and hub genes link HIV-1 infection to pulmonary arterial hypertension (PAH)? The authors hypothesize that a heightened type I interferon response in HIV-1 is a crucial susceptibility factor for PAH.
- ★ HIV-1 and PAH share 109 co-expressed genes primarily enriched in type I interferon (IFN) pathways. finding
- ★ ISG15 and IFI27 are pivotal shared hub genes between HIV-1 and PAH. finding
- ★ A heightened type I IFN response in HIV-1 may be a crucial susceptibility factor for PAH. mechanism
- ★ Monocytes are pivotal cells involved in the shared type I IFN response pathway linking HIV-1 and PAH. mechanism
- WGCNA combined with DEG intersection and external validation can identify shared disease hub genes. method
- Shared genes are enriched in NOD-like and RIG-I-like receptor signaling and defense response to virus. finding
- ISG15 and IFI27 show diagnostic potential (ROC/AUC) for HIV-1 and PAH across datasets. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Gene expression microarray (WGCNA co-expression analysis) | Human PBMC (GSE140713 HIV; GSE33463 PAH) | disease vs control (HIV-1 / PAH) | module eigengene–trait correlation, shared module genes | GPL6480 (Agilent); GPL6947 (Illumina) |
| Microarray differential expression analysis (limma/voom) | Human PBMC (GSE77939 HIV; GSE703 PAH) | disease vs control | differentially expressed genes (DEGs) | GPL15207; GPL80 |
| Microarray hub gene expression / ROC diagnostic validation | Human PBMC (GSE140713, GSE77939, GSE2171, GSE33463, GSE703, GSE19617) | disease vs control (incl. ART and IPAH/SPAH subgroups) | hub gene expression levels (t-test) and AUC | GPL6480, GPL15207, GPL201, GPL6947, GPL80 |
| GO/KEGG and PPI enrichment analysis | 109 shared genes (in silico) | none | enriched GO-BP terms and KEGG pathways; PPI network | ClueGO/Cytoscape; GeneMania; Metascape |
| Gene set enrichment analysis (GSEA) | Human PBMC (GSE140713 HIV; GSE33463 PAH) | ISG15/IFI27 high vs low expression groups | hallmark pathway normalized enrichment score (NES) | clusterProfiler / MSigDB v7.4 |
| Immune cell infiltration analysis (CIBERSORT) | Human PBMC (GSE140713 HIV; GSE33463 PAH) | disease vs control | relative abundance of 22 immune cell types; hub gene–cell correlation | CIBERSORT R package |
| Single-cell RNA-seq (scRNA-seq) | Human PBMC (10X 8381 PBMCs; GSE157829; GSM6647828) | healthy vs HIV-1 infected donors | cell cluster identification and hub gene/pathway signature expression | 10X Genomics Chromium 3'; Seurat/AUCell |
- – 109 shared genes overlap between HIV-1 and PAH positively correlated modules 109 genes
- ▲ ISG15 and IFI27 identified as hub genes by intersecting upregulated DEGs and WGCNA shared genes
- – Shared genes enriched in type I IFN / defense response to virus / toll-like receptor signaling
- ▲ GSE140713 green module strongly positively correlated with HIV-1 r=0.65, P<0.001
- ▲ GSE33463 turquoise module strongly positively correlated with PAH r=0.74, P<0.001
- – GSE77939 yielded 61 DEGs (40 up, 21 down) in HIV-1 61 DEGs
- – GSE703 yielded 519 DEGs (389 up, 130 down) in PAH 519 DEGs
- – Monocytes implicated as pivotal cells in type I IFN response by CIBERSORT and scRNA-seq
- correlation r=0.74, P<0.001 (GSE33463 turquoise module vs PAH)
- correlation r=0.65, P<0.001 (GSE140713 green module vs HIV-1)
- correlation r=0.59, P<0.001 (GSE33463 blue module vs PAH)
- correlation r=0.56, P<0.001 (GSE140713 blue module vs HIV-1)
- correlation r=0.50, P<0.001 (GSE33463 greenyellow module vs PAH)
- count 109 shared genes (overlap of HIV-1 and PAH modules)
- count 519 DEGs (389 up, 130 down) (GSE703 PAH=14, control=6)
- fold_change |log2FC|>0.585, P<0.05 (DEG screening criteria for GSE77939 and GSE703)
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 computational bioinformatics study used six public PBMC microarray datasets to identify shared transcriptomic signatures between HIV-1 infection and pulmonary arterial hypertension (PAH). WGCNA was applied to the two largest datasets for co-expression module discovery, followed by limma/voom differential expression analysis on validation datasets to narrow hub gene candidates; results were further evaluated with ROC/AUC, GSEA (Benjamini-Hochberg corrected), CIBERSORT immune deconvolution, and scRNA-seq clustering. Group-level comparisons used Student's t-test and Wilcoxon rank sum test, and inter-variable associations used Spearman correlation.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| WGCNA with Pearson correlation (network construction) and Spearman correlation (module-trait association) | Co-expression network construction and module-phenotype correlation in GSE140713 (HIV) and GSE33463 (PAH) | GSE140713: 50 HIV + 7 controls; GSE33463: 72 PAH + 41 controls | not stated |
| limma/voom linear model with empirical Bayes moderation | Differential expression analysis in validation datasets GSE77939 (HIV) and GSE703 (PAH); threshold |log2FC| > 0.585 and P < 0.05 | GSE77939: 17 HIV + 4 controls; GSE703: 14 PAH + 6 controls | not stated |
| Student's t-test (unpaired, two-group) | Hub gene (ISG15, IFI27) expression comparison between control and HIV-1/PAH groups across all six datasets and ART/PAH-subtype subgroup analyses | varies by dataset; see Table 1 (control n ranges from 4 to 41) | not stated |
| Wilcoxon rank sum test | Immune cell infiltration abundance (CIBERSORT output) comparison between control and HIV-1/PAH groups in GSE140713 and GSE33463 | GSE140713: 50 HIV + 7 controls; GSE33463: 72 PAH + 41 controls | not stated |
| Spearman correlation | Correlation between hub gene expression and relative immune cell type abundance (CIBERSORT); significance threshold |cor| > 0.3 and P < 0.05 | null | not stated |
| ROC curve / AUC (via pROC package) | Biomarker diagnostic performance of ISG15 and IFI27 evaluated across all six datasets | varies by dataset; see Table 1 | na |
| GSEA (clusterProfiler, MSigDB v7.4 hallmark gene sets) with Benjamini-Hochberg FDR adjustment | Pathway enrichment in high- vs. low-ISG15/IFI27 expression subgroups within GSE140713 and GSE33463; significance threshold adjusted P < 0.05 | null | not stated |
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Student's t-test was used to compare hub gene expression between cases and controls in datasets where control group sizes are small (n = 4 or n = 7)↳ Could also: Wilcoxon rank sum test (Mann-Whitney U) or a permutation-based test — Non-parametric alternatives make fewer distributional assumptions and are often preferred when sample sizes are too small to verify normality reliably; the study already applied the Wilcoxon test for CIBERSORT comparisons, so the approach is established within the same workflow
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Multiple hub-gene expression comparisons were performed across six datasets and multiple subgroups using uncorrected P < 0.05↳ Could also: Bonferroni, Holm, or Benjamini-Hochberg correction applied across the family of hub-gene × dataset comparisons — An explicit correction over this comparison family would bound the type I error rate; the study already applies BH correction within GSEA, so extension to other comparison families would be consistent with that choice
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WGCNA was applied to only two of the six available datasets (one per disease, selected for largest n) for module discovery↳ Could also: Consensus WGCNA across all same-disease datasets after ComBat or limma-based batch correction — Consensus WGCNA is specifically designed to find modules reproducible across multiple datasets and can reduce dataset-specific noise; using all available datasets could increase the robustness of identified modules
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Immune cell deconvolution was performed with CIBERSORT alone↳ Could also: xCell, TIMER2.0, or MCP-counter applied in parallel or as a sensitivity check — Different deconvolution algorithms use distinct reference signatures and estimation methods; concordance of cell-type proportion estimates across multiple tools is commonly used to increase confidence in findings
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Group comparison figures used threshold-based significance symbols (* P < 0.05, ** P < 0.01) rather than exact p-values for hub gene and immune cell comparisons↳ Could also: Report exact p-values alongside effect size estimates (e.g., log2FC, rank-biserial correlation) and 95% confidence intervals — Exact p-values and effect sizes with confidence intervals allow readers to assess the precision and practical magnitude of differences and are recommended by many statistical reporting guidelines (e.g., APA, ICMJE)
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Measures of dispersion were not reported for hub gene expression group comparisons↳ Could also: Report SD, SEM, or 95% CI alongside group means or medians, particularly in box or violin plots — Displaying spread is especially informative when control group sizes are small (n = 4–12 across datasets), helping readers gauge the reliability of central tendency estimates and the degree of overlap between groups
Citation network
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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-38528047
Paper: Mai et al. 2024, Sci Rep 14:7048. "Identification of the shared hub gene signatures and molecular mechanisms between HIV-1 and pulmonary arterial hypertension." DOI 10.1038/s41598-024-55645-x · PMCID PMC10963360.
Registry code: https://github.com/junjunlab/GseaVis — this is a third-party GSEA visualization R package, NOT the authors' analysis pipeline. The authors ship no analysis code. Per BRIEF rule 2 (P16), reproducing by running the standard pipeline they describe on their own public GEO data is equally valid. We do that.
Pipeline-derived results in the paper (candidate claims)
| # | Reported result | Pipeline | In scope? |
|---|---|---|---|
| C1 | GSE77939 (HIV): 61 DEGs = 40 up + 21 down at |log2FC|>0.585 & P<0.05, limma | limma DEG | YES — exact method + threshold + dataset stated |
| C2 | GSE703 (PAH): 519 DEGs = 389 up + 130 down, same threshold/method | limma DEG | YES — exact method + threshold + dataset stated |
| C3 | IFI27 diagnostic AUC = 0.940 in GSE703 | pROC ROC-AUC | YES (stretch) — single clean number on a public dataset |
| C4 | ISG15 & IFI27 are the 2 hub genes (up-regulated, IFN-related) | sanity check | partial — verify they appear up-regulated in the DEG lists |
| — | 109 shared WGCNA genes; module counts (11 / 21); β=14/6 | WGCNA | OUT — many unstated params (cut height, merge, module-trait), high variance, the hard 20% |
| — | GO/KEGG terms (ClueGO Cytoscape plugin) | manual GUI | OUT — interactive plugin, not scriptable 1:1 |
| — | GSEA top-5 IFN pathways (GseaVis plots) | clusterProfiler | OUT — depends on upstream ranked list + MSigDB v7.4 exactly |
| — | scRNA: 50,524 cells → 15 clusters → 6 populations; CIBERSORT | scanpy/Seurat | OUT — separate single-cell dataset, heavy, many unstated params |
| — | GeneMANIA associated proteins | web tool | OUT — external web service, not a reproducible compute step |
What we attempt (the 80, not the 20)
Re-run the explicitly-specified limma DEG analysis on the two datasets where the paper gives both the method and the exact threshold and an exact DEG count (C1, C2), plus the one clean diagnostic number (C3) and a hub-gene sanity check (C4). These are the low-hanging, clearly-specified, 1:1-checkable outputs.
Method faithfulness notes / caveats (recorded up front, not hidden)
- Paper says "voom method via limma".
voomis for RNA-seq counts; GSE77939 (GPL15207) and GSE703 (GPL80) are microarrays with log-intensity values, so voom is not strictly applicable. We run the standard microarray limma pipeline (lmFit+eBayeson log2 intensities) — the correct method for these data — and flag the voom mention as a possible methods inconsistency. - "P < 0.05" is taken as the raw p-value (as printed). We also report the adjusted-p count for context.
- DEG counts can be reported at probe vs gene level; we report both (probe count and unique-gene-symbol count) so the human auditor can see which the paper's number matches.
All compute on «our HPC» (SLURM). Data fetched via GEOquery inside the compute job; large intermediates stay on «infra»; only small result JSONs come back.
Assessments & scoring basis
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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 solid partial reproduction with no fabrication concern: all reported values are derivable from the paper's own public GEO data, and the IFI27 GSE703 ROC-AUC reproduced exactly (0.940) while both DEG counts came within 7–11% with matching up/down direction. The deviations sit on the input/preprocessing side and stem from our self-chosen steps under an underspecified method (paper's 'voom' is wrong for microarrays; normalization and probe-collapse unspecified) — not from the authors' side and not from data unavailability. The authors' actual analysis code was never shipped (the linked repo is a generic plotting package), and the hard ~20% (WGCNA, GSEA, single-cell, CIBERSORT) was not attempted, so overall quality is yellow — confirmed core, explainable gaps, incomplete scope.
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
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