Methylation patterns of the nasal epigenome of hospitalized SARS-CoV-2 positive patients reveal insights into molecular mechanisms of COVID-19.
Part of the results reproduced; minor but material deviations remained.
- Nothing in this column.
- 🟡Could not use the authors’ exact input data
- 🔴Reported values were only indirectly 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
▸Reproduction agent’s raw note
DROP. The paper (nasal-epigenome WGBS in hospitalized COVID-19 patients) is described well at the methods level, but its reported numbers are NOT reproducible at 80/20. (1) Core caller is DRAGEN EP v2.6.3 (Illumina, proprietary FPGA hardware-locked) — not installable on «our HPC»/«infra» => env_unresolvable. (2) The authors' analysis code (methylKit tiling-window logistic regression + MethylSeekR v1.38) is NOT shipped; the RU's only code link is the third-party trimming tool Trim Galore (one early step) => no_code for every gradeable result. (3) The RU BRIEF's data accession GSE183071 is MIS-LINKED: it is the Gomez-Carballa external RNA/NanoString validation set (PMID 35202626, 'Expression profiling by array'), NOT this paper's methylation data; the paper's own WGBS data is BioProject PRJNA1162448 (positive, 61 Bisulfite-Seq runs ~45GB each) + GEO GSE168254 (negatives) — verified via NCBI/ENA. Per HARD RULE 2 (P16) I did NOT down-rank for third-party code; I scoped and PREPARED a faithful run of the named tool (Trim Galore v0.4.2 on real paper sample SRR30711030 = T-COV-R-306, bounded to 5M read pairs; see reproduction/run.sbatch) — but it was NOT executed because the «our HPC» VPN 2FA was not confirmed this session, and the paper reports no trim-stage statistic to grade against anyway. WHAT I DID NOT ATTEMPT: full WGBS alignment/methylation-calling (would require proprietary DRAGEN or a divergent Bismark substitute = the hard ~20% we are told not to chase), and all downstream methylKit/MethylSeekR/GREAT/HOMER/IPA numbers. FABRICATION: none observed at screening level; the data is genuine high-depth WGBS consistent with the Methods, but the reported numbers are UN-CHECKABLE (proprietary caller + unshipped scripts), not refuted. Artifacts: scope.md, original/claims.tsv, AUDIT.md, reproduction/agreement.json, reproduction/run.sbatch.
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Assessment versions
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v1 current initial assessmentassessed: 2026-06-15 ⛓ 0825b56472df
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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
- 2026-07-31
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: opusDifferential DNA methylation in the nasal mucosa (the target tissue of SARS-CoV-2) distinguishes severe from mild COVID-19 and reveals molecular drivers of disease severity, but it is unclear whether these methylation differences predispose to severe disease or arise in response to it.
- ★ The nasal methylome shows differential DNA methylation in intergenic regions and low methylated regions (LMRs), highlighting distal regulatory/enhancer-like elements in COVID-19 gene regulation finding
- ★ Severe COVID-19 shows a preponderance of hypomethylated DMRs relative to hypermethylated DMRs in nasal samples finding
- ★ Differentially methylated pathways implicate immune cell recruitment/function and inflammatory signaling including PI3K/Akt, Notch, and NF-κB mechanism
- ★ Hypermethylation of the FUT4 (CD15) promoter implicates impaired neutrophil adhesion in severe disease finding
- ★ Hypermethylation of ELF5 binding sites suggests downregulation of ELF5 targets as a factor in COVID-19 phenotypic variability finding
- ★ WGBS of nasal mucosa can capture cell-type-specific methylation signatures and infer differential regulation of immune-mediated pathways method
- ★ COVID-19 positive individuals show greatly increased overlap of LMRs with immune (lymphoid and myeloid) cell regulatory elements versus COVID-19 negative individuals finding
- DNA methylation serves as a marker of the host immune response to SARS-CoV-2 infection finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Whole genome bisulfite sequencing (WGBS) | Nasal mucosa samples from SARS-CoV-2 positive subjects (4 severe/hospitalized, 57 mild/non-hospitalized) | none (observational; SARS-CoV-2 infection severity) | CpG percent methylation; UMRs/LMRs; differentially methylated regions (DMRs) | — |
| Whole genome bisulfite sequencing (WGBS) | Nasal samples from COVID-19 negative / non-infected individuals (healthy controls, n=7 pooled) | none | UMR/LMR overlap with cell-type regulatory elements; promoter methylation | — |
| Methylation segmentation / regulatory region annotation (DHS overlap) | Aggregated nasal WGBS datasets; DNase I hypersensitive sites across 16 cell types | none | UMR/LMR counts and overlap with cell-type-specific regulatory DNA | — |
| Differential methylation analysis (tiling window, logistic regression with age/race/gender covariates) | Severe vs mild nasal WGBS datasets | none | Top DMRs ranked by q-value; hypo/hypermethylation; intergenic mapping | — |
| Pathway/GO enrichment analysis (Coronascape, GREAT, GO/KEGG/Reactome, IPA) | Differentially methylated genes (DMGs) from severe vs mild cohorts | none | Enriched immune/inflammatory pathways; gene-pathway associations | GREAT; Coronascape; QIAGEN IPA v01-21-03; UCSC Genome Browser |
- ▲ Hypermethylation of the FUT4 promoter in hospitalized vs non-hospitalized subjects (chr11:94,545,001–94,552,500) q = 2.90 × 10^-112
- ▼ Severe COVID-19 had more hypomethylated DMRs (n=7,256) than hypermethylated DMRs (n=2,744) among top 10,000 DMRs 7,256 vs 2,744
- ▲ COVID-19 positive vs negative: 46% vs 25% of LMRs overlapped immune cell regulatory elements 46% vs 25% (X2=5189.6)
- ▲ COVID-19 positive vs negative: 24% vs 15% of LMRs overlapped lymphoid and 28% vs 12% overlapped myeloid regulatory elements lymphoid 24% vs 15%; myeloid 28% vs 12%
- ▼ Relative hypomethylation of AKT1 promoter in severe vs mild disease methylation difference = -18.38%, q = 2.22 × 10^-22
- ▼ Relative hypomethylation of ISG15 promoter (AKT1 downstream target) in severe vs mild disease methylation difference = -32.27%, q = 2.49 × 10^-35
- ▼ Relative hypomethylation of ZEB2 and SNAI1 promoters in severe vs mild disease ZEB2 -24.39% (q=2.52×10^-32); SNAI1 -28.98% (q=6.11×10^-16)
- – Hypomethylated DMRs disproportionately in intergenic regions (49.6%) vs hypermethylated (13.8%) 49.6% vs 13.8% (X2=1064.4)
- pvalue X2 = 5189.6, df = 1, p < 2.2 × 10^-16 (LMR overlap with immune regulatory elements, COVID+ vs COVID- (46% vs 25%))
- pvalue X2 = 4557.1, df = 1, p < 2.2 × 10^-16 (Myeloid LMR overlap COVID+ vs COVID- (28% vs 12%))
- pvalue X2 = 1264.0, df = 1, p < 2.2 × 10^-16 (Lymphoid LMR overlap COVID+ vs COVID- (24% vs 15%))
- fold_change methylation difference = -32.27%, q = 2.49 × 10^-35 (ISG15 promoter hypomethylation severe vs mild)
- fold_change q = 2.90 × 10^-112 (FUT4 promoter hypermethylation hospitalized vs non-hospitalized)
- count 19,187 UMRs (avg 2,366 bp, ~117 CpGs); 43,924 LMRs (avg 642 bp, ~8 CpGs) (Regulatory regions identified across aggregated nasal WGBS samples)
- count 13.2 million CpGs per sample at >10X coverage (Average WGBS sequencing depth)
- pvalue X2 = 1,064.4, df = 1, p < 2.2 × 10^-16 (Intergenic enrichment of hypomethylated vs hypermethylated DMRs (49.6% vs 13.8%))
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 used whole-genome bisulfite sequencing (WGBS) of nasal samples from 61 SARS-CoV-2-positive individuals (4 severe/hospitalized, 57 mild/non-hospitalized) to identify differentially methylated regions (DMRs). DMR detection employed tiling-window logistic regression with age, race, and gender as covariates; the top 10,000 DMRs ranked by q-value were retained for downstream analysis. Proportion comparisons of regulatory-element overlaps between groups used chi-squared tests, and pathway enrichment was performed via Coronascape against GO, KEGG, and Reactome databases with p-value thresholds.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Logistic regression (tiling-window DMR analysis) | Identification of DMRs between severe (n=4) and mild (n=57) COVID-19 nasal WGBS data; covariates: age, race, gender | 61 (4 severe, 57 mild) | not stated |
| Chi-squared test (Χ²) | Comparison of proportions of LMRs and UMRs overlapping immune/lymphoid/myeloid/epithelial regulatory elements between COVID-19 positive and negative individuals | null | not stated |
| Pathway enrichment analysis (Coronascape / hypergeometric or Fisher's exact, method not specified) | GO Biological Processes, KEGG Pathways, Reactome Gene Sets enrichment of hypomethylated and hypermethylated DMGs in severe vs mild COVID-19 | 487 hypomethylated genes; 503 hypermethylated genes | not stated |
| GREAT genomic annotation (binomial / hypergeometric region-gene association) | Gene annotation of hypo- and hypermethylated DMRs | 10,000 top DMRs | not stated |
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The primary DMR comparison (severe vs mild) used logistic regression applied to groups of n=4 and n=57, a highly imbalanced and very small severe group.↳ Could also: Permutation-based or exact tests (e.g., Fisher's exact test per CpG region, or bootstrap-based inference), or dedicated small-n WGBS tools such as DSS (which uses a beta-binomial model and empirical Bayes shrinkage) or MethylKit. — When one group is very small (n=4), asymptotic likelihood-based regression can have inflated Type I error and unstable covariate estimates; shrinkage estimators and permutation approaches borrow strength across the genome and can yield better-calibrated inference under extreme imbalance.
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Chi-squared tests were used to compare proportions of UMRs/LMRs overlapping regulatory elements across multiple cell-type categories without multiplicity adjustment.↳ Could also: A single omnibus chi-squared or Fisher's exact test on the full contingency table followed by post-hoc comparisons with Bonferroni or BH-FDR correction across cell types. — Testing multiple cell-type categories (immune, lymphoid, myeloid, epithelial, pulmonary) inflates the family-wise error rate; a correction such as Benjamini-Hochberg would control the false discovery rate across these related comparisons while remaining interpretable.
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Pathway enrichment for DMGs used a p-value threshold (≤0.01 and ≤10⁻³) across hundreds of tested pathways without an explicit correction for the number of pathways evaluated.↳ Could also: Benjamini-Hochberg FDR correction applied to all pathway p-values, with reporting of adjusted q-values alongside raw p-values. — Testing hundreds of GO/KEGG/Reactome pathways simultaneously inflates false positives; FDR adjustment is a standard practice in enrichment analysis and would clarify which pathway associations survive correction.
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The study did not describe cell-type deconvolution of the bulk nasal WGBS signal; differences between groups were characterized by overlap with reference DHS maps.↳ Could also: Reference-based epigenomic deconvolution (e.g., EpiDISH, MethylCIBERSORT, or BLUEPRINT-based cell-type reference panels) to estimate proportions of epithelial, immune, and stromal cell populations in each sample. — Bulk methylation differences between severe and mild disease could reflect changes in cell-type composition rather than (or in addition to) within-cell-type methylation changes; deconvolution would allow these contributions to be quantified and, if desired, statistically adjusted.
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Effect sizes for DMRs were reported as raw methylation percentage differences (e.g., −18.38% for AKT1); the logistic regression framework could also yield odds ratios or standardized coefficients but these were not reported.↳ Could also: Reporting of standardized effect sizes (Cohen's d on M-values, or odds ratios with 95% confidence intervals from the logistic regression) alongside methylation differences. — Raw methylation differences are intuitive but depend on the absolute methylation level; odds ratios or standardized measures facilitate comparison across loci with different background methylation levels and across studies.
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Dispersion of the primary outcome (methylation) across subjects was not summarized at the regional level (e.g., no within-group SD or CI shown for DMRs); Table 1 demographics use IQR for age only.↳ Could also: Reporting per-group mean ± SD (or median with IQR) of % methylation, or 95% confidence intervals around the methylation difference, for key DMRs highlighted in figures. — With n=4 severe cases, within-group variability is informative about the reliability of point estimates; CIs or SDs around the methylation difference would allow readers to gauge precision, especially given the small severe-group sample size.
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
8 downstream papers · 2 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.
- A multi-tissue study of immune gene expression profi... 2022 · 35 cites
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- The 15-Year Survival Advantage: Immune Resilience as... 2025 · 7 cites
- Molecular modeling of C1-inhibitor as SARS-CoV-2 tar... 2023 · 2 cites
- Bioinformatic assay reveal the potential mechanism o... 2023 · 2 cites
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-40170038
Paper: Methylation patterns of the nasal epigenome of hospitalized SARS-CoV-2 positive patients reveal insights into molecular mechanisms of COVID-19. BMC Med Genomics 2025. PMID 40170038 · PMCID PMC11963311 · DOI 10.1186/s12920-025-02125-4.
Assay: Whole-Genome Bisulfite Sequencing (WGBS), NOT a methylation array.
Reported computational pipeline (from Methods)
- Demultiplex: Illumina
bcl2Fastq2-2.19.1(raw BCL not deposited → not reproducible). - Quality/adapter trimming:
Trim Galore v0.4.2← the only code linked in the RU (https://github.com/FelixKrueger/TrimGalore). - Alignment + methylation calling:
DRAGEN EP v2.6.3(Illumina DRAGEN, paired-end). - Deduplication:
picard v2.17.8. - Differential methylation: R package methylKit — overlapping tiling windows (window 500 bp, step 250 bp), logistic regression with age/gender/race covariates, CpG coverage ≥10x, ≥2 samples/group, SLIM p-adjust; top 10,000 DMRs by q-value.
- UMR/LMR segmentation: MethylSeekR v1.38.
- Annotation/enrichment: GREAT, HOMER (
findMotifsGenome.pl v4.11.1), IPA, Coronascape.
In scope (pipeline-derived, attemptable)
- Trim Galore v0.4.2 trimming step on the paper's own WGBS reads — the single shipped/named tool. Per HARD RULE 2 (P16), running a third-party tool on the paper's data is a valid reproduction. This is the only step we can execute.
Out of scope (cannot reproduce at 80/20) — with reason
- All gradeable numeric claims (DMR counts 7,256 hypo / 2,744 hyper; UMR 19,187;
LMR 43,924; gene counts 487/503; per-gene q-values; ELF4/ELF5 target counts;
"13.2 M CpGs >10x") are produced downstream of DRAGEN EP (step 3) and the
methylKit/MethylSeekR analysis scripts.
DRAGEN EP v2.6.3is proprietary, FPGA-hardware-locked (Illumina). It is not installable on «our HPC» («infra») →env_unresolvablefor the core caller.- The authors' analysis code is not shipped — the RU's code link is only the
trimming tool, not the methylKit/MethylSeekR/covariate scripts →
no_codefor every gradeable result. Substituting Bismark+methylKit would be a different pipeline (not 1:1) and is explicitly the hard ~20% we are told not to chase.
- Pathway/TF/IPA/Coronascape/GREAT enrichments — external/proprietary services, downstream of the above.
- Wet-lab steps (DNA extraction, Covaris shearing, library prep) — not computational.
Data location (CORRECTED — RU accession was mis-linked)
- RU BRIEF lists
geo:GSE183071, but GSE183071 is the Gómez-Carballa external RNA/NanoString validation dataset (PMID 35202626, "Expression profiling by array"), i.e. one of this paper's external comparison sets — NOT its own methylation data. - The paper's OWN WGBS data:
- SARS-CoV-2 positive: BioProject PRJNA1162448 (61 Bisulfite-Seq runs, ~45 GB each, 300M+ read pairs/sample).
- SARS-CoV-2 negative (reused): GEO GSE168254 (pooled nasal-mucosa WGBS).
- Demonstration target: one positive run, e.g.
SRR30711030(sampleT-COV-R-306).
Verdict shape
Best achievable: partial — execute the one named/shipped tool (Trim Galore v0.4.2) on a real paper sample to show the shipped step reproduces, while documenting that every reported number is out of reach (proprietary DRAGEN + unshipped analysis code). No paper numeric claim is gradeable from the trimming step alone (the paper reports no trim-stage statistic).
Assessments & scoring basis
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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.